Predictive Analytics for Student Success

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PREDICTIVE ANALYTICSFOR

STUDENT SUCCESS:

Developing Data-Driven Predictive Models of Student

Success

Final Report

University of Maryland University College

January 6, 2015

A Research Project Funded by the Kresge Foundation



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Table of Contents

EXECUTIVE SUMMARY ............................................................................................................ 4

SECTION 1: INTRODUCTION ................................................................................................. 10

Grant Partnership ................................................................................................................................... 11

Objectives and Milestones ...................................................................................................................... 11

SECTION 2: LITERATURE REVIEW ..................................................................................... 13

Theoretical Models of Community College Transfer Student Performance ..................................... 13

Educational Data Mining ....................................................................................................................... 15

Predicting Transfer Students’ First-Term GPA .................................................................................. 16

Predicting Transfer Student Re-Enrollment ........................................................................................ 18

Predicting Re-Enrollment for Non-Traditional Students .................................................................... 19

Literature Guiding Interventions .......................................................................................................... 20

Community College Transfer Students’ Transitioning ....................................................................... 22

Literature to Support Specific Interventions ....................................................................................... 22

Checklist ............................................................................................................................................. 22Community College Mentor ............................................................................................................... 23

SECTION 3: RESEARCH SCOPE AND DESIGN ................................................................... 25

Research Questions ................................................................................................................................. 25

Student Population.................................................................................................................................. 26

SECTION 4: DATA SOURCES .................................................................................................. 27

SECTION 5: SURVIVAL ANALYSIS: REGISTRATION AND WITHDRAWAL IN THE

ONLINE CLASSROOM .............................................................................................................. 29

SECTION 6: PROFILES OF STUDENTS USING DATA MINING ...................................... 31

Profiles of Student Success ..................................................................................................................... 31

Further Findings from Data Mining ..................................................................................................... 33

SECTION 7: PREDICTIVE MODELING OF STUDENT SUCCESS ................................... 34

Initial Predictive Modeling ..................................................................................................................... 34

Predicting Successful GPA ................................................................................................................. 34

Predicting Re-enrollment .................................................................................................................... 35

Updated Predictive Modeling ................................................................................................................ 36

Population ........................................................................................................................................... 38

Predicting Earning a Successful First-term GPA ................................................................................ 39

Predicting Re-Enrollment ................................................................................................................... 40Predicting Retention ............................................................................................................................ 42

Predicting Graduation ......................................................................................................................... 44

Summary of Results from Predictive Modeling.................................................................................. 45

SECTION 8: GRADUATION RATES ....................................................................................... 48

SECTION 9: EXAMINING LEARNER BEHAVIOR IN THE ONLINE CLASSROOM ... 49

Online Classroom Behaviors and Class Performance ......................................................................... 51



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Student Level Online Classroom Behaviors and Course Performance ............................................. 53

Engagement Profiles and Course Performance ................................................................................... 56

Modeling Retention ................................................................................................................................. 57

SECTION 10: STUDENT MOTIVATION AND SELF-REGULATION ............................... 59

Population ........................................................................................................................................... 59

Methodology ....................................................................................................................................... 59

Results ................................................................................................................................................. 60

Key Findings ....................................................................................................................................... 63

SECTION 11: INTERVENTION IMPLEMENTATION AND EVALUATION ................... 64

Checklist .................................................................................................................................................. 65

Participants .......................................................................................................................................... 65

Results ................................................................................................................................................. 65

College Success Mentoring ..................................................................................................................... 66

Participants .......................................................................................................................................... 66

Results ................................................................................................................................................. 66

Jumpstart Summer ................................................................................................................................. 68

Results ................................................................................................................................................. 68

Accounting 220 and Accounting 221 ..................................................................................................... 69

Participants .......................................................................................................................................... 69

Results ................................................................................................................................................. 69

Key Findings ....................................................................................................................................... 69

SECTION 12: DISSEMINATION .............................................................................................. 70

Presentations at Conferences ................................................................................................................. 70

Publications ............................................................................................................................................. 70

Learner Analytics Summit ..................................................................................................................... 71

Success Calculator .................................................................................................................................. 73

SECTION 13: FINANCIAL SUPPORT ..................................................................................... 74

SECTION 14: CONCLUSIONS .................................................................................................. 75

Future Directions .................................................................................................................................... 76

REFERENCES .............................................................................................................................. 77



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EXECUTIVE SUMMARY

The purpose of the Predictive Analytics for Student Success (PASS) project was to: (a) aggregatedata across multiple institutions to track the academic progress and completion of communitycollege transfer students, (b) identify factors associated with success, and (c) implement

interventions that promote student success. In completing the PASS project research andinterventions, University of Maryland University College (UMUC) partnered with twocommunity colleges, Montgomery College (MC) and Prince George’s Community College(PGCC). This work was funded by a grant from the Kresge Foundation -- Developing Data-Driven Predictive Models of Student Success.

The purpose of the grant was:

To build an integrated database tracking students across institutions from communitycollege to UMUC.

To use predictive statistical models and data mining techniques to track and model

students’ progress across institutions. To identify factors predictive of students’ success at UMUC

To inform the development of interventions aimed to improve outcomes for undergraduatestudents transferring from community colleges to UMUC or to other four-year institutions.

This report will summarize the data development, research, intervention implementation andevaluation, dissemination, and application creation completed through the PASS project.

Phase 1

During the first 24 months (Phase 1) of the grant, UMUC and the partner institutions developed

and signed a Memorandum of Understanding (MOU) to ensure data security and establish parameters for data use. The MOU allowed the PASS project team to conduct research usingindividual student data while protecting student information and confidentiality. Once the MOUwas in place, researchers identified the population of interest, conducted an initial literaturereview to identify variables of interest, and began data collection and exploratory analyses.

The research team identified over 250,000 students enrolled at UMUC between 2005 and 2012.Of those, over 30,000 students transferred from MC and PGCC. Student demographics,academic performance at the three institutions, behavior in the online classroom at UMUC, andadvising data were combined into an integrated, multi-institutional database: the Kresge DataMart (KDM).

The literature review covered student performance in online courses, successful coursecompletion, factors associated with re-enrollment and retention, and the use of data miningtechniques in higher education. Existing research showed that factors such as the number ofschools attended, the number of credits transferred, and community college GPA influencedstudent success. Key measures of success included successful course completion and retention.



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In Phase 1, initial data mining was conducted to identify variables that were associated withsuccess. Specific courses were identified as having predictive value in relation to success atUMUC. Regression analyses determined that student online classroom activities prior to thestart of a class (i.e., entering the online classroom prior to the first day) and during the earlyweeks of the course were predictive of successful course completion.

Phase 2

Phase 2 of the PASS project was completed in months 25 to 36 of the grant. The initial plan forPhase 2 was to:

Secure external evaluators

Further develop collaboration with the community colleges

Identify the scope of the project

Clarify the research plan and conduct associated analyses

Begin initial dissemination of research findings

UMUC began meeting regularly with the community colleges to develop the Phase 2 research plan and evaluate research findings and grant progress. Two external evaluators were selected toconduct an independent evaluation of the research project. These collaborations proved to behighly beneficial in developing the research program and designing interventions. As a result ofthe collaborations, new data were identified for collection, and a full scope of the research wasoutlined in the form of a research plan.

A research plan was developed to model students’ progress and performance from thecommunity college to graduation from a four-year institution. The research plan created a modeladdressing the relation between students’ prior academic work and perf ormance at UMUC to

include graduation. The full path model of students’ academic trajectory from communitycollege to UMUC is below.

The plan identified the following research goals for Phase 2:

1.

To develop profiles of transfer students at UMUC2. To identify factors from students’ community college academic backgrounds that predictsuccess at UMUC

3. To develop predictive models of student success based on demographic information,community college course taking behaviors, and first-term factors.

4. To develop interventions designed to improve the success of students transferring fromcommunity colleges to UMUC.

Community

College

Data

UMUC First

Term GPA Graduation Retention

Re-

enrollment



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Phase 2 considered two primary outcomes of interest in predictive models: 1) earning a first-termGPA of 2.0 or above at UMUC, termed successful first-term GPA, and 2) students’ re-enrollmentat UMUC within 12 months following their first academic term, termed retention.

Key findings from Phase 2 include:

Across studies, age and marital status were associated with success at UMUC. Older,

married students were found to be more likely to succeed.

Four profiles of student success at UMUC were identified based on students’ GPAs andretention rates. The profiles differed in terms of community college course taking preferences and course load and in the change in GPA when transferring to UMUC.These results suggest that the degree of student preparedness, particularly in specifictarget areas (e.g., accounting, economics), is predictive of success at UMUC.

Course efficiency, or the ratio of credits earned to credits attempted, in the communitycollege was determined to be a predictor of success at UMUC. The higher the courseefficiency, the more likely a student was to succeed.

A new factor, delta GPA, was introduced in these analyses, corresponding to thedifference between students’ GPA at the community college and at UMUC. While most

students experienced a decreased GPA when transferring to UMUC, the magnitude ofthis decrease was predictive of students’ continued enrollment at UMUC beyond the firstterm.

Students who took math or honors courses in community college were more likely tosucceed at UMUC, suggesting that rigor of community college courses may preparestudents to succeed at a four-year university.

Student behaviors in the online classroom indicated high variability in the extent to whichthey engage in course content and course-related activities. A substantial percentage ofstudents accessed course content and course materials to a limited extent, thus impacting

successful course completion.

Phase 3

Phase 3, the final year of the Kresge Grant, focused on four goals:

1. Data enhancement2. Extended research3. Implementation and evaluation of the interventions4. Continued dissemination of research findings and intervention results

As a result of continued collaboration with the community colleges, the KDM was expanded toinclude additional variables from the community colleges as well as updated data from UMUC toallow for expanded analyses of retention and graduation.

With the inclusion of new data, Phase 3 analyses focused on re-enrollment, retention, andgraduation from UMUC.



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Key Findings from Phase 3 include:

Demographic Factors. Gender and marital status were associated with both performance

(earning a successful first-term GPA) and persistence (re-enrollment). These

characteristics may indicate students’ maturity and commitment to pursuing academicgoals. Interestingly, while African American status was negatively associated withearning a successful first-term GPA, it was positively associated with persistence metrics.This suggests that while not always successful in their first semester, African Americanstudents are nonetheless committed to their educational goals.

Math at the Community College. Across models examining both persistence and

performance, variables associated with taking math at the community college were foundto be significant predictors. Within our sample, taking math at the community collegereflects academic abilities and may also reflect students’ commitment to meeting the

requirements necessary for transfer and graduation.

Community College Success and Completion. In models predicting first-term GPA, re-

enrollment, and graduation, students’ community college GPA was a significant predictor. This suggests that, overall, performance at the community college matters forsuccess and persistence at a four-year institution.

First-Term Performance. As in findings from Phase 2, students’ performance in their firstsemester at UMUC remains crucial in predicting re-enrollment, retention, and graduation.In fact, across models, it was the strongest individual predictor of performance. First-term GPA may be an indicator of factors contributing to students’ success, beyondacademic abilities. Specifically, students who are better at acclimating to an onlineuniversity and the demands associated with a four year institution may have a higherfirst-term GPA and may be more able to persist.

Online Classroom Engagement . A particularly rich finding from Phase 3 analyses is the

association between student online classroom engagement as measured in the learningmanagement system (LMS) and course performance. The general pattern was thatstudents earning higher grades in a particular course were also significantly moreengaged in the online classroom. Further, online course engagement, in combinationwith students’ community college GPA, was predictive of overall course performance;such a model linked students’ community college backgrounds with four -yearinstitutional experience.

Phase 3 also included the examination of the efficacy of four interventions aimed at promotingcommunity college transfer student success at UMUC. In addition, two interventions at thecommunity college were used to better prepare students for transfer.

Interventions undertaken at UMUC were:

Checklist. New student orientation checklist administered to community college transfer

students to aid them in navigating online resources at UMUC. Although no significantdifferences were found, students responding to an evaluation survey found the checklist to be a useful tool.



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Mentoring . Eight week structured mentoring program, where new UMUC communitycollege transfer students were paired with a peer mentor -- a successful student at UMUCwho had transferred from the same community college. Each week, mentors emailedmentees with study tips and information to support adjustment to UMUC. Although nostatistically significant improvements in semester performance were found for mentees,

unexpectedly, students serving as mentors had a significantly higher cumulative GPA anda significantly higher rate of successful course completion when compared to the controlgroup of students who were invited to be mentors and elected not to participate. This phenomenon may be due to the bias inherent in the self-selection process.

Jumpstart Summer . A program that paired mentoring with Jumpstart, a four-weekonboarding course, designed to support students’ goal setting and academic planning. Fourexperimental conditions were examined: (a) a control group, (b) students only completingthe Jumpstart course, (c) students only participating in the mentoring program, and (d) aJumpstart Summer group, receiving both mentoring and enrolled in the Jumpstart course. No significant differences in performance were found; however, students successfullycompleting the Jumpstart course had a higher rate of successful course completion and re-

enrolled at a higher rate. Accounting 220/221: The online tutoring intervention was developed by faculty for

students taking Accounting 220 and Accounting 221 -- courses with a disproportionallyhigh failure rate both at UMUC and nationally. Students who participated in the onlinetutoring had a significantly higher GPA at the end of the semester and a significantlyhigher rate of successful course completion, when compared to students not participatingin online tutoring.

Interventions developed at the community colleges were:

Diverse Male Student Initiative (DMS-I). DMS-I is a two-year program at Prince

George’s Community College that provides minority male students with role models andacademic and career mentoring. DMS-I held a two-day summer institute at PGCC thatfeatured keynote speakers and awarded book and tuition vouchers for early registration to participants with the aim of improving academic planning and persistence. PGCC andUMUC will track and evaluate the success and persistence of students who participatedin the program and who transfer to UMUC.

Women’s Mentoring, Boys to Men, TriO: Women’s Mentoring, Boys to Men, and TRiO

are comprehensive mentoring programs, developed at Montgomery College, that provideminority students with comprehensive academic and social support throughout theirtransfer pathways from high school to MC, and ultimately to a four-year institution. MCand UMUC will identify students participating in these programs who transfer to UMUC

and will track them to evaluate their performance. UMUC will provide similar mentoringand support if these students transfer to UMUC.

Findings from research and interventions were disseminated through ten conference presentations and manuscripts. In addition, a website, http://www.umuc.edu/PASS, was createdto share project goals and results with a broad audience of stakeholders.

http://www.umuc.edu/PASS






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This report was produced by the UMUC Office of Institutional Research and Accountability andcontains 12 sections:

Section 1: IntroductionSection 2: Literature Review

Section 3: Research Scope and DesignSection 4: Data SourcesSection 5: Survival Analysis: Registration and Withdrawal for Online CoursesSection 6: Mining of Community College DataSection 7: Predicting Student SuccessSection 8: Graduation RatesSection 9: Data Mining of Online Learner BehaviorSection 10: Students’ Motivation and Self -RegulationSection 11: Implementation and Evaluation of InterventionsSection 12: DisseminationSection 13: Financial Statement

Section 14: Conclusions



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SECTION 1: INTRODUCTION

The purpose of this report is to present the results of research conducted by the University ofMaryland University College (UMUC), in partnership with two community colleges,Montgomery College (MC) and Prince George’s Community College (PGCC) as part of the

PASS project. The scope of work was undertaken as part of a grant from the Kresge foundationand includes: (a) data development, (b) research using data mining and predictive modeling toexamine community college transfer student success, and (c) intervention development,implementation, and evaluation to provide academic, social, and institutional support tocommunity college students both prior to and after transfer. The project was broken out intothree phases. Each phase was built upon results from the previous phase, resulting in continuedresearch development and comprehensive analyses. A research plan identifying the scope of the project began in Phase 1. The final research design and methods were finalized in Phase 3.

The research plan was developed to conceptualize students’ academic pathways from the

community college, to transfer to a four-year institution, to graduation from UMUC. In

developing the research plan, specific milestones in students’ academic pathways were modeled.These included: (a) earning a successful first-term GPA, (b) re-enrolling in the immediate nextsemester after transfer, (c) retention (re-enrollment within a 12-month window), and (d)graduation within an eight-year period. Each of these milestones was predicted based on dataaggregated from the community college and UMUC. Specifically, students’ demographicinformation, community college course taking behaviors, indicators of first-semester performance at UMUC, and behaviors in the online classroom were used in predictive modelingand in data mining. A model presenting students’ academic trajectories from transfer tograduation that guided the research was developed.

PASS project goals included:

To develop a data sharing partnership and create a memorandum of understanding between partner community colleges and UMUC

To build an integrated database tracking students across institutions, from communitycollege to UMUC

To integrate data from students’ community college backgrounds with UMUC performance data for use in research

To use data mining to develop profiles of transfer student success at UMUC

To identify factors from students’ community college academic backgrounds that predictsuccess at UMUC

Develop predictive models of UMUC first-term GPA, re-enrollment, retention, and

graduation based on community college data Examine graduation rates of community college transfer students at UMUC

Examine online classroom engagement as associated with course performance

Profile students’ motivational and self -regulatory attributes

Develop, implement, and evaluate interventions aimed at promoting community collegetransfer students’ success

Disseminate research findings



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Grant Partnership

UMUC is a four-year public university that offers online degree programs to a diverse populationof working adults. With support from the Kresge Foundation, UMUC established partnershipswith two Maryland community colleges that also serve large and diverse student populations.

Montgomery College (MC), established in 1946, enrolls over 60,000 students annually. PrinceGeorge’s Community College (PGCC) enrolls more than 40,000 students from approximately128 different countries. Both institutions serve the metro-D.C. area, but differ in that PGCCserves more low-income students. Both institutions have endorsed the goals of this project andare committed to working with UMUC to find ways to promote student success throughout theiracademic careers.

Objectives and Milestones

Specific objectives and milestones were identified for each phase of the research project. Theseobjectives and milestones have been modified throughout the course of the project, but areconsistent with grant requirements. Table 1 presents the objectives and milestones for each

phase.

Table 1. Project objectives and milestones

Objectives Milestones Status

Phase 1 April 2011 – October 2012

Develop aProject ActionPlan

Develop a project action and collaboration plan withthe partnering agencies.

Complete

Data Collectionand Preparation

Prepare a data ―universe‖ (integrated database system)

on CC transfer students in the UMUC population(KDM)

Complete

Understand variables; define student characteristicsand retention data; develop data dictionary.

Complete

Data Analysis Conduct initial predictive analyses and employ datamining techniques to identify factors contributing tostudents’ success

Complete

ProjectEvaluation

Conduct ongoing project evaluation. Take action onidentified areas for improvement.

Complete

Phase 2 November 2012 – October 2013

Develop andValidateAnalytic Modelsof StudentSuccess

Analyze data and identify factors that predictsuccess/failure.

Complete

Validate predictive analyses and models developedthrough data mining techniques to predict students’

success and retention at UMUC.

Complete

Build student profiles based on analyses. Complete

Disseminate KeyFindings

Discuss results with Kresge Workgroup and sharewith advisory board.

Complete

Discuss results with Project Partners and obtainfeedback.

Complete



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Objectives Milestones Status

Present key findings at national conferences on highereducation

Complete

DevelopInterventions

Work with stakeholders at UMUC and CC partners todevelop a list of potential interventions.

Complete

ProjectEvaluation

Conduct ongoing project evaluation. Take action onidentified areas for improvement.

Complete

Research Plan 3 Design and develop KDM2 to update and improvedata related to student success

Complete

Plan Phase 3 analyses on expanded integrated data. Complete

Phase 3 November 2013 – December 2014

DevelopInterventions

Review relevant literature on interventions that promote student success in online learning.

Complete

Develop an implementation plan and timeline for piloting of interventions.

Complete

Implement Pilot

Interventions

Implement and evaluate pilot interventions. Complete

DisseminateResults onInterventions

Develop and disseminate report on the pilotinterventions

Complete

Phase 3 Analyses Develop and execute Phase 3 research plan Complete

Report Findings Present key findings from Phase 3 analyses at nationalconferences; publish research in journals

Complete

Prepare written report of both Phase 3 analyses andfull scope of Kresge grant work.

Complete

Dissemination ofResults andResources

Develop website and repository for educational datamining and student success.

December2014

Host a national convening on data mining and learneranalytics.

Complete

ProjectEvaluation

Deliver final project evaluation. December2014



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SECTION 2: LITERATURE REVIEW

The literature review was conducted over the course of the four-year project. This section presents a review of the literature in the following areas:

Theoretical models of community college transfer student performance

Educational data mining Predicting transfer student first-term GPA

Predicting transfer student re-enrollment

Literature guiding interventions

Community college student transitioning

Literature to support specific interventions

Theoretical Models of Community College Transfer Student Performance

Two theoretical models of community college transfer student performance and persistence haveguided work in the field, as well as in the PASS project analyses. The first is Tinto’s (1975,

1987) Student Integration Model, which applies a psychological lens to understanding studentattrition. The Student Integration Model identifies four aspects of student-institution interactionsthat have an effect on persistence. Specifically, these are the background characteristics andacademic goal commitments that students bring to a university setting, and in turn, their effectson students’ academic and social integration at the transfer institution. Backgroundcharacteristics include students’ demographic attributes, family backgrounds, and experiences prior to college (Tinto, 1975). Goal commitments include learners’ motivation for degree pursuitand educational expectations as well as institutional commitment to a particular university.

Academic and social integration is based on students’ interactions with a variety of institutional

features over time. Tinto (1975) suggests that these interactions may be evaluated based on both

structural and normative considerations. Structural considerations refer to objective and explicitsocial and academic standards that students may have to meet (e.g., a minimum GPA, meetingwith an advisor), whereas normative components of integration relate to students’ identificationswith these standards (e.g., earning a high GPA). Tinto emphasized the central importance ofstudents’ institutional integration, both academic and social, by saying, ―we learned that

involvement matters and that it matters most during the first cr itical year of college,‖ (Tinto,

2006, p. 3; Upcraft, Gardner, & Barefoot, 2005).

At the same time, academic and social integration into a transfer institution are not givens formany students. Building on Tinto’s earlier work (1975), Bean and Metzner (1985) developed amodel of attrition, reflecting the experiences of non-traditional undergraduate students, termed

the Conceptual Model of Non-Traditional Student Attrition. In their definition, non-traditionalundergraduate students may be defined as those who are older (i.e., 25 and above, Stewart &Rue, 1983), enrolled part-time, non-residential, commuting to campus, or representing somecombination of these characteristics (Bean & Metzner, 1985). Understandably, this populationof students is considered to undergo a different socialization process from that of traditionalstudents conceptualized in Tinto’s model (1975). Non-traditional students may have differentexperiences with and potential for institutional commitment and social integration. Bean andMetzner (1985) suggest that this may be because older students exhibit greater characteristics



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associated with maturity and therefore may be less open to the socialization process and becausethese students have more limited contact with socializing agents (e.g., faculty, peers, Chickering,1974). More generally, non-traditional students may be less interested in institutions’ socialculture, and rather more concerned with academic offerings and credentials.

Juxtaposing the experiences of traditional and non-traditional students, for non-traditionallearners there is (a) more limited interaction with faculty and peers as well as with collegeservices (i.e., more limited social integration, as per Tinto, 1975), (b) similarity in academicfocus and experience (i.e., parallel classroom experience), and (c) much greater interaction withthe external, non-institutional environment (Bean & Metzner, 1985).

Based on the differences identified between traditional and non-traditional students, Bean andMetzner (1985) conceptualize students’ decisions to drop out as predicated on four general types

of variables. The first of these are background factors, including students’ demographics, pastacademic performance, and educational goals and expectations. The second group ofconsiderations is students’ academic performance, or factors reflecting learners’ grades, study

habits, and pursuit of major at the transfer institution. The third group of factors are students’intent to leave, considered to be more psychological; these include students’ goal commitment,

perceived utility of a given degree, and institutional satisfaction. Finally, unique to this model,the fourth group of factors are external factors that may have a direct effect on students’decisions to drop-out; these include finances, out-of-school work, and family commitments(Bean & Metzner, 1985).

There are two compensatory relationships between variables identified. First, if students’academic outcomes are low, they may nonetheless persist, compensating with high levels of psychological commitment. Further, when academic performance is low, students will persist ifenvironmental factors support their continued enrollment. Conversely, when environmentalfactors do not support persistence, for non-traditional students, even high academic performancemay not be sufficiently compensatory. More generally, Bean and Metzner (1985) suggest thatfor non-traditional students, environmental factors may have a much more pronounced effect onattrition decisions than do academic factors, as non-traditional students are much more closelyaffiliated with the non-institutional environments than are traditional students residing onuniversity campuses (Bean & Metzner, 1985; Metzner, 1984).

As such, understanding non-traditional student persistence may be particularly challenging at theinstitutional level as, in large part, it may be attributed to environmental factors that theinstitution may not be aware of or able to control. Indeed, for non-traditional students, the primary point of institutional interaction has to do with academic factors; as such these areasrepresent targets for intervention (Bean & Metzner, 1985).

As part of the PASS project, we were interested in gaining insight into community collegetransfer students’ persistence at a four -year institution by looking longitudinally to considerwhich background factors, including learner characteristics and community college experiences,may impact student re-enrollment and continued pursuit of educational goals.



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Educational Data Mining

Current literature on student success focuses on outcomes such as course success, coursewithdrawal and retention. For example, variables such as student characteristics, previous coursework, grades, and time spent in course discussions and activities may be useful in predicting

course success (Aragon & Johnson, 2008; Morris & Finnegan, 2009; Morris, Finnegan & Lee2009; Park & Choi, 2009). Course-level variables acquired from student login data from theLMS may have predictive value in measuring course withdrawal rates (Willging & Johnson,2008; Nistor & Neubauer, 2010). Variables such as student characteristics, number of transfercredits, final grade in any given course, experience in online environments, and course load may be useful in predicting re-enrollment and retention (Aragon & Johnson, 2008; Morris &Finnegan, 2009; Boston, Diaz, Gibson, Ice, Richardson & Swan, 2011).

Although these studies showcase a variety of findings related to student success, the majority ofstudies of retention in online learning environments use traditional statistical or qualitativemethods. Park and Choi (2009) point out that expansion of methods such as data mining may

have utility when student, course, program, and institutional level variables are well defined andinstitutionally meaningful. Literature related to educational data mining focuses on exploratoryresearch.

Data mining is a method of discovering new and potentially useful information from largeamounts of data (Baker & Yacef, 2009; Luan, 2001). Educational data mining is a subset of thefield of data mining that draws on a wide variety of literatures such as statistics, psychometrics,and computational modeling to examine relationships that may predict student outcomes(Romano & Ventura, 2007; Baker & Yacef, 2009). In educational data mining, data miningalgorithms are used to create and improve models of student behavior in order to betterunderstand student learning (Luan, 2002).

Data mining methods are most helpful for finding patterns already present in data, notnecessarily in testing hypotheses (Luan, 2001). Baker and Yucef (2009) suggest that research inhigher education should use a variety of algorithms, such as classification, clustering orassociation algorithms in determining relationships between variables. Although manydefinitions of these techniques exist in data mining literature, Han and Kamber (2001) offer thefollowing definitions. Classification is the process of finding a set of models or functions thatdescribe and distinguish data classes or concepts to predict a class of objects whose class label isunknown. Clustering analyzes data objects that are related to similar outcomes withoutconsulting a class label. Association is the discovery of rules showing attribute value conditionsthat occur frequently together in a given set of data (Han & Kamber, 2001).

Recent research suggests that these data mining algorithms can be used to examine variablesrelated to student success. Yu, DiGangi, Jannach-Pennell, Lo, and Kaprolet (2010) used aclassification algorithm to explore potential predictors related to student retention in a traditionalundergraduate institution. In this study, the authors used a decision tree to explore demographic,academic performance, and enrollment variables as they related to student retention. This studyrevealed a predictable relationship between earned hours and retention, but also found that at thisinstitution, retention was closely related to state of residence (in-state/out of state) and living



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location (on campus/off campus). The authors speculate that this finding points to the potentialutility of online courses in improving retention for out-of-state or off-campus students.

Despite these recent developments in exploring variables related to student success in traditionalhigher education settings, research using data mining techniques to uncover relationships among

variables in online courses is limited in scope. The PASS project is designed to fill this gap in theextant literature by utilizing data on online students who attended multiple institutions.

Predicting Transfer Students’ First-Term GPA

Generally, the transition from community college to a four-year university has been consideredto be a stressful period for students. In early examinations of this transitional period, Hills(1965) determined that when students from junior college transfer to a four-year university theymight experience an ―appreciable loss in their level of grades‖ (p.209), termed transfer shock .Transfer shock has been defined as a decrease in academic performance (i.e., GPA) experienced by students in their first semesters at a four-year university, due to difficulties with adjustment(Keeley & House, 1993). Since Hill’s (1965) initial exploration, a wealth of studies have

emerged examining transfer shock and students’ decreases in GPA when transitioning fromcommunity college to a four-year university (e.g., Best & Gehring, 1993; Keeley & House, 1993;Preston, 1993; Soltz, 1992).

However, recent research has painted a more nuanced picture of transfer shock. Cejda, Kaylor,& Rewey (1998) determined that transfer shock is discipline specific. For instance, whilestudents transferring into mathematics and science majors did experience a drop, those majoringin the fine arts and humanities actually experience an increase in GPA. Further, in a meta-analysis of 62 studies examining transfer shock, Diaz (1992) determined that while the majorityof studies did find that community college students experience a transfer shock, it was slight(i.e., one half of a grade point or less); also, the majority of studies reviewed found that students

recovered from transfer shock over the course of their university careers. Nickens (1972)skeptical of transfer ―shock‖ and ―recovery‖ suggests that transfer students’ GPAs cannot bedistinguished from the GPAs of their native counterparts. Specific decreases in GPA may beexplained by difference in institutional practices and any subsequent increases in GPA may beexplained by regression to the mean and the attrition of weaker students (Nickens, 1972).

Regardless of findings, across studies examining community college students’ performance

when transferring to a four-year university, first-term GPA has been a key outcome of study(Carlan & Byxbe, 2000; Driscoll, 2007; Glass & Harrington, 2010; Hughes & Graham, 1992;Townsend, McNerny, & Arnold, 1993). This may be because first-term GPA has beenconsidered to be a barometer of transfer students’ success and adjustment to a four -year

institution (e.g., Knoell & Medsker, 1965) as well as level of preparedness to meet the academicdemands of a four-year university (Carlan & Byxbe, 2000; Roksa & Calcagno, 2008). Further,first-term GPA has been considered to be strongly associated with persistence or students’

retention and graduation from a four-year university (Gao, Hughes, O’Rear, & Fendley, 2002).

Indeed, there have been a number of studies examining predictors of first-term GPA forcommunity college transfer students (e.g., Graham & Hughes, 1994; Townsend et al., 1993).Most commonly, demographic factors have been examined as potentially impacting community



17

college students’ transfer success. For example, Durio, Helmick, and Slover (1982) found that

demographic factors (i.e., gender and ethnicity) impacted transfer students’ success. Examining

an expanded pallet of variables predicting first-term GPA, Keeley and House (1993) consideredstudents’ age, gender, ethnicity, college major, residence status, as well as class standing (e.g.,sophomore) as predictive of first-term GPA. In particular, age (i.e., being older) and gender (i.e.,

being female) were found to the associated with higher first-term GPA for transfer students, aswas having earned an associate degree prior to transfer ring. In addition to the focus on students’demographic factors, GPA at the community college level has been found to be a keydeterminant of first-term GPA when students transfer to a four-year institution (Baldwin, 1994;Towsend, McNerny, & Arnold, 1993). However, more research is needed to identify predictorsof transfer students’ success at a four -year university (Johnson, 1987).

Course Taking Behavior at the Community College

In examinations of community college transfer students’ performance at four-year institutions, atthe forefront have been considerations of students’ preparedness to handle the challengesassociated with university-level course work (e.g., Berger & Malaney, 2003; Keeley & House,1993; Townsend, 1995; Townsend et al., 1993). Despite concerns over community collegetransfer students’ preparedness, limited research has examined the nature of community college

students’ course taking backgrounds to determine predictors of university success. Some studies provide initial insights. For example, Phlegar, Andrew, and McLaughlin (1981) determined thatstudents fundamentally prepared in key subject areas (i.e, math, science, and English) at thecommunity college level performed better upon transferring. Deng (2006) determined thatstudents attending career-focused community college programs outperformed those attendingliberal-arts community college programs, when transferring to a four-year university. Ratherthan considering specific courses of study, Pennington (2006) determined that students’

enrollment in developmental course work in community college was associated with a decreasedGPA upon transfer to a four-year institution.

Carlan and Byxbe (2000) found community college major to be significantly associated withfirst-term GPA; for instance, students majoring in education and psychology had a higher GPAafter transfer than did students majoring in business and the sciences. However, it is unclearwhether these major-specific differences in GPA drop were associated with different levels ofstudents’ preparedness or with cross-institutional differences in the academic demands required by these various programs of study.

Rather than examining community college majors, Cejda et al. (1998) found students’ first-termGPA to be related to university major. Parallel to prior findings (i.e., Carlan & Byxbe, 2000)students in the sciences, indeed, experienced a drop in first-term GPA, while students in the fine

arts, humanities, and social sciences experienced a GPA increase. This replicated findings thatstudents majoring in the sciences and mathematics (i.e., biology, chemistry, math, physics,accounting, and economics) had a lower GPA than their fellow community college transferstudents (James Madison University, 1989). However, the nature of students’ preparedness for afour-year university and the types of community college academic experiences that may supporttransfer success have yet to be fully examined.



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Studies examining community college students’ preparedness have primarily examined students’

academic backgrounds at the level of the major (e.g., Carlan & Byxbe, 2000). Institutional datasharing as part of the PASS project, allowed the specific courses of community college studentsto be examined as predictors of performance at the four-year institution.

Predicting Transfer Student Re-Enrollment

Historically, research on student retention largely focused on the experiences of traditionalstudents, until Tinto (1993) expanded on extant models of retention to consider which factorsmay impact the retention of non-traditional students. For both traditional and non-traditionalstudents, retention was thought to be a consequence of students’ academic and social integration

(Tinto, 1993). Other research has echoed the central role of social factors in predicting retentionfor non-traditional students, online, and distance learners (Boston, Diaz, Gibson, Ice, Richardson,& Swan, 2009). At the same time, a number of demographic and community college factorshave been considered as predictive of community college transfer students’ persistence at a four -year university.

Based on a comprehensive review of the persistence literature, Peltier, Laden, and Matranga(1999) determined that gender, race and ethnicity, socioeconomic status, high school GPA,college GPA and interaction variables are all related to persistence. In particular race/ethnicityand prior academic achievement have been robust predictors of persistence (e.g., Astin, 1997;Tross, Harper, Osher, & Knwidinger, 2000; Levitz, Noel, & Richter, 1999), whereas findingswith regard to gender have been more mixed, Reason, 2009; St. John et al., 2001). Wetzel,O’Toole, and Peterson (1999) used logistic regression, with a dichotomous outcome variable,

retained or not. Retention was significantly predicted primarily based on academic factors,including GPA, earning a low GPA which places students at low academic risk, and the ratio ofcredit hours earned to the credits attempted.

Murtaugh, Burns and Schuster (1999) used survival analysis to examine the retention ofundergraduate students, enrolling in a university between 1991 and 1996; 25% to 35% of thecohort examined had interrupted enrollment within this period. Specifically, 13.5% stopped outfor a single term, 10.8% had stopped out for two terms, and 1.8% had stopped out for threeterms, after which they were required to undergo a readmission process. Predictors of stoppingout were referred to as hazards. Hazards were examined for one year, two year, and four yearretention. Minority status had a higher rate of withdrawal than did white students; also associatedwith withdrawal was age, high school GPA, first quarter GPA, area of study, and participation infreshman orientation. In particular, Murtaugh et al. (1999) highlight the importance of pre-college characteristics in predicting persistence.

Looking at a sample of traditional, first time freshman, Cabrera, Nora, and Castaneda (1993)used structural equation modeling to analyze predictors from both Tinto’s (1975) and Bean andMetzner’s (1985) models to predict student persistence. Cabrera et al. (1993) ranked variables

predicting persistence; the most important factor was psychological goal commitment, or intentto persist, followed by GPA, institutional commitment, and encouragement from family andfriends. In turn, intent to persist was predicted by institutional commitment, encouragementfrom family and friends, academic goal commitment, and academic integration – these factorshaving an indirect effect on persistence.



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Whereas the aforementioned studies focused on individual student factors predicting retention,Moore and Fetzner (2009) addressed the institutional characteristics that fostered commitment innon-traditional students. These factors included having a leadership culture that fosterscommitment to student success and institutional policies and practices that incorporate studentsupport services and technological support. For online learners, access to services and support

that meet their needs was found to be crucial (Moore & Fetzner, 2009). Further, studentsatisfaction, defined as students happy with their progress and with support received for learning,and with a perception that the knowledge they were learning was valuable, was predictive ofretention. Faculty satisfaction, stemming from involvement in curricular design and training inthe use of online technologies supporting learning, were found to be key to engagement andcontributors to retention (Moore & Fetzner, 2009).

Predicting Re-Enrollment for Non-Traditional Students

Based on theoretical work (Astin, 1975; Bean & Metzner, 1985; Tinto, 1975), we may expectthat community college transfer students’ persistence may be affected by different factors. First,

given that much of the literature examining community college students performance hasfocused on the degree of student preparedness (Carlan & Byzbe, 2000), learners’ prior academic

experiences may be particularly important to examine, especially as they include not only highschool work, as for traditional students, but college-level course work at a two-year institution aswell. Further, to the extent that transfer students enter more connected to external factors beyondtheir experiences at the transfer institution, it may be particularly important to examine learner background characteristics and how these are related to academic factors at the transferuniversity.

Wang (2008), using logistic regression, found that the probability of graduating with a bachelor’s

degree for students starting at community college was predicted by gender, socio-economicstatus, high school curricula, educational expectations, community college GPA, collegeinvolvement, and math remediation; while persistence, prior to graduation, was predicted bycommunity college GPA and locus of control. Just as in the Wang (2008) study, in the PASS project, researchers looked to students’ demographics and community college factors, includingcourse taking behaviors as well as overall performance, to predict next-semester re-enrollment.

Kreig (2010) examined students at Western Washington University, an institution with asubstantial population of community college transfer students comprising each education level,and found that native students were more likely to graduate, even after demographiccharacteristics and prior academic performance were controlled. Krieg (2010) compares theexperience of community college students to that of freshmen at a four-year university. For newcommunity college students, there may be a difficult adjustment to a new learning context, whichmay result in early attrition if students consider themselves to be incompatible with the newenvironment. As such, first year retention is a particularly important factor to consider inunderstanding students’ persistence and ultimate graduation.

While this tension in fit has most commonly been examined by considering the drop in performance (i.e., GPA) that community college students experience upon transfer to a four-yearuniversity, alternately termed transfer shock (Cejda, Kaylor, & Rewey, 199; Townsend,



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McNemy, & Arnold, 1993), Krieg (2010) suggests that this may more profoundly manifest inrapid attrition from the four-year institution. More generally, there may be an interaction between transfer student status, first-term GPA, and drop-out rates (Spady, 1970). Specially,those transfer students scoring a low GPA in the first quarter were twice as likely to drop out aswere native students (Krieg, 2010). Pascarella and Terenzini (1980) likewise conclude that the

majority of attrition occurs in the freshman year, when students are new to the university setting,and further indicate that this marks a misalignment between theory and evidence. For instance,Tinto’s model of academic attrition is better suited to modeling student attrition beyond the firstyear.

The difference Krieg (2010) documents, is not specific to low performing students. Even high performing community college transfer students are more likely to drop-out than are their nativecounterparts. This may be because transfer students have less immediate affiliation andintegration into the transfer institution or because these transfer students are required to take prerequisite courses before entering into a major (Krieg, 2010). This points to the importance oflooking beyond community college students’ prior academic performance, to look at specific

course taking behaviors at the community college as well as to consider first-term GPA at thetransfer institution – these factors were examined as part of the PASS analyses. More broadly,these findings are aligned with the interactional relationships identified in Bean and Metzner’s

model (1985) that suggest that for non-traditional learners, academic success may not be asufficient factor to promote persistence.

Literature Guiding Interventions

Intervention with specific populations (e.g., community college transfer students) and in specificcontexts (e.g., online universities) are needed, as the majority of interventions have focused onfinding solutions that will have a general effect on a broad population of students (Pascarella &Terenzini (1998).

Two prominent models of student retention have been proposed, however, both of these modelsspeak primarily to the needs of traditional students. Tinto proposed the Student IntegrationModel, which identified attrition as resulting from a lack of congruency between students’ needs

and institutional offerings (1987). Specifically, Tinto points to the need for students’ academicabilities and motivational orientations to match an institution’s academic and social

characteristics. In determining whether or not students will persist in post-secondary education,Tinto (1987) suggests that two forms of commitments must be in place. The first is students’

commitment to educational goals and the second is students’ commitment to remain within a particular institution.

From Tinto’s model of student retention, conclusions may be drawn regarding the types offactors that interventions to promote retention ought to foster in students; specifically Tinto’smodel suggest the need for interventions that target students’ (a) academic abilities, (b)

motivational orientations, specifically with regard to the types of educations goals students adoptin pursuing higher education, and (c) institutional connections. Intervention designs shouldemphasize the correspondence between students’ abilities or goals and institutional offerings.



21

Yet more work is needed to understand how to adapt the Student Integration Model (Tinto,1987), to reflect the experiences of non-traditional students, transfer students, and studentsenrolled in online universities, such as UMUC. In particular, factors affecting students’ retentionmay deviate from the proposed model based on differences in the type of institution students area part of as well as students’ gender and ethnicity (Pascarella & Terezini, 1997). To this end,

proposed interventions are geared not only with general UMUC student populations, but alsospeak to the specific needs of female students (e.g., Girls to Women) and diverse learners (e.g.mentors in the Community College Mentor and College Writing interventions are matched withmentees according to demographic characteristics, including ethnicity.

Tinto’s (1987) model has further been critiqued for being limited in considering the role thatexternal factors, or considerations independent of students and institutions, may have onretention (Pascarella & Terezini, 1997). These external factors, including financial and familiarconsiderations, may be particularly important to consider when modeling retention of non-traditional students. Studies have found that often times these students do not persist in post-secondary education because of finances, employment demands, and taxing family

responsibilities (Bean & Metzner, 1985).

To expand Tinto’s model and to consider the needs of non-traditional students – those classifiedas part-time, older, and non-residential (e.g., online learners at UMUC) – Bean and Metzner proposed a Student Attrition Model (1985). This model suggests that students’ persistence andacademic outcomes can be understood as a result of four factors, namely: (a) backgroundvariables, (b) academic variables, (c) psychological factors, and (d) environmental variables.Background variables refer to students’ characteristics that may put them at a risk or deficitrelative to their peers. These factors include age, high-school performance, gender, andethnicity. Academic variables include students’ study habits, the role of advising, and students’

certainty in their major. Psychological factors reflect students’ motivation for engaging in post-graduate education – these include students’ goal commitment, the expected utility or value of adegree, and psychological stress. Finally, environmental factors introduced in the model addressstudents’ responsibilities outside of the university and may represent constraints on students’

pursuit of educational goals. The role of social interaction is featured in this model, as previousmodels have identified the importance of social integration in predicting students’ persistence

(e.g., Tinto, 1975; Pascarella & Chapman, 1983), however, the nature of social interactions maydiffer for traditional versus non-traditional students.

In designing interventions, all four of the factors described in Bean and Metzner’s model wereconsidered. In particular, mentoring programs targeted students and matched mentees withmentors according to background variables. Further, mentoring programs were intended to helpstudents in mitigating the effects of environmental variables; the intention of these interventionswas to provide students with role-models who have successfully persisted, despite limitingexternal factors. In providing students with an Introductory Check-List and academic tutoring,the interventions were designed to impact academic variables. Finally, psychological outcomeswere targeted by encouraging students to take advantage of the advising available throughUMUC and providing students’ with the opportunity to discuss their long term and professionalgoals with mentors.



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One of the unique challenges for non-traditional students is the identified lack of socialintegration and social interaction (Bean & Metzner, 1985). A number of interventions weregeared toward connecting students with social resources. For instance, the checklist interventionencouraged students to be involved with available student organizations. Further, to the extentthat persistence is marked by a match between a student and an institution (Cabrera, Nora, &

Castaneda, 1993), a number of the interventions were aimed specifically at helping studentsrecognize others like them as members of the UMUC community.

Community College Transfer Students’ Transitioning

Transferring from community college has been identified as a high-stress time for students, presenting academic, psychological, and environmental challenges (e.g., Laanan, 2001). Flaga(2006) identified five dimensions of transitioning. These are, learning resources, connecting , familiarity, negotiating , and integration. The first two dimensions deal with the knowledge andskills that students need in order to be successful, whereas the last three dimensions address howthese skills may develop over time.

Learning resources refer to the tools students may use to gain information about the university.Three types of learning resources were specified; these were: (a) formal resources provided bythe university (e.g., orientation information), (b) informal resources provided by individualsknowledgeable about the university but not officially affiliated (e.g. information from alumni),and (c) initiative-based resources that students gather independently (Flaga, 2006). The seconddimension, connecting, refers to the relationships that students are required to form whentransferring to a new institution; including (a) academic connections (e.g., with faculty), socialconnections (e.g., with other students), and physical connects (e.g., with the universityenvironment) (Flaga, 2006).

The third dimension, familiarity, emerges when students become more comfortable with their

new environment. The fourth dimension, negotiating, occurs when students adjust their behaviors to better fit their new environment. Finally, the fifth dimension, integrating, does notalways happen, but involves students shifting their identities to reflect their new institution(Flaga, 2006).

Literature to Support Specific Interventions

Checklist

The Checklist targeted the first two dimensions identified by Flaga (2006) as supporting

students’ transitioning. Specifically, through the checklist, students received supportencouraging them to connect with both formal and informal information resources with the intentof forming academic, social, and physical connections. In completing the activities specified inthe checklist, students had the opportunity to exercise initiative in connecting with resources anddevelop familiarity with UMUC as an institution and an academic community.

In a qualitative study of community college transfer students, one of the recommendationstransfer students proposed as a resource to help their transition was the creation of a transfer



23

checklist (Owens, 2007). Indeed, 27% of students expressed a desire for the introduction of achecklist or guide to aid them in the transfer process (Owens, 2007). In describing the featuresthat would make checklists appeal to them, students expressed desires for ease-of-use, onlineavailability, and comprehensiveness (with information ranging from where to park to how toregister for classes); as well as checklists that break down complex processes in a step-by-step

manner and include necessary contact information (Owens, 2007).

This is a particularly important initiative given that surveys of community college students havedetermined that students have a need for more information (e.g., Harbin, 1997; Andres, 2001)and more assistance (Townsend & Wilson, 2006) as they move to their new institutions.

Community College Mentor

Peer-mentoring for community college students transferring to four-year schools has been under-examined in the literature. However, mentoring interventions have been broadly used as anavenue to promote students’ retention (Good, Halpring, & Halprin, 2000; Hoyt, 2000).

Flaga (2006) suggests that benefits associated with peer mentoring are not only academic;through mentoring, students gain access to informal learning resources and have the opportunityto socially connect with their peers. Likewise, Good et al. (2000) found mentoring to confer psychological and academic benefits to both mentors and mentees.

The mentoring relationship has been identified as supporting three types of outcomes, namely psychosocial , vocational , and role-modeling (Ensher, Heun, & Blanchard, 2003). Psychosocialsupport refers to mentors providing counseling, friendship, and, encouragement to their mentees(Enscher et al., 2003). Vocational support is considered to be support that enhances the professional lives of mentees (Enscher et al., 2003) and can be extended to include the academicsupport provided by mentors to new students. Finally, role-modeling refers to mentorsdemonstrating appropriate behaviors or expectations, either implicitly or explicitly (Enscher etal., 2003). For example, role-models can offer examples of effective study strategies or describeappropriate standards of communication when conferring with professors. Tinto (2001) furthersuggests that peer mentor relationships can address both specific classwork and general skillsassociated with successful college completion. Moreover, these benefits can affect mentees aswell as mentors (Good et al., 2000; Snowden & Hardy, 2012).

Mentoring has been found to be particularly beneficial for minority students (Good et al., 2000;Redmond, 2000). Redmond (2000) suggests that mentoring programs must adopt the followinggoals to meet the needs of diverse students: (a) promote greater student contact, (b) promotestudents’ use of ser vices for support with non-academic problems, (c) intervene quickly whenstudents encounter academic difficulties, and (d) develop culturally-sensitive psychosocialenvironments.

A case study for mentorship in diverse communities is the ALANA (Asian, Latin, Africa, and Native American) mentoring program, targeting minority community college students (Mueller,1993). The stated goals of the ALANA program were to (a) provide social and academicsupport for minority students, (b) engage in role-play to help students critically think through



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challenging situations, and (c) assist students in making time-sensitive decisions (e.g., courseadd/withdrawal). Mentors in the ALANA program seek to maximize social interaction withtheir mentees as a mechanism for relieving students’ anxieties (Mueller, 1993).

Peer mentoring has been shown to benefit students transitioning from two- to four-year

institution and those in distance education programs. For instance, Lenaburg, Auirre, Goodchild,and Kuhn (2012) reported on the impact of a program that oriented community college studentsto a four-year institution. As part of the program, students were provided with peer mentors. Atthe conclusion of the program, participants rated their peer mentor experience very highly,commenting that peer mentors were instrumental in explaining the transfer process, providingsocial support and helping them maintain interest in a four-year institution. Most recent resultssuggest that peer mentors were instrumental in helping students transition from communitycolleges to a four-year university. Peer mentoring has also benefitted students new to onlinelearning contexts (Boyle et al., 2012; Brown, 2011). A study of peer mentoring programs in threedistance education universities, for example, found evidence of improvement in mentees’ course

passage rates, retention, and sense of belonging (Boyle et al., 2012).

Though not evaluated in the empirical literature, the University of California at Berkeley has amentoring program for transferring community college students: the Starting Point MentorshipProgram. Through this program, transferring students are paired with mentors who offer: (a)guidance, (b) motivation, and (c) access to campus and community resources. Specifically, the benefits to mentees are outlined as: advice on study skills, time management and goal-setting,information about the differences in academic and social culture between community college anda four-year institution, encouragement to set and pursue academic goals, and the point-of-view ofa current student.

Despite the likelihood that peer mentoring can mitigate the shock of student transfer — eitherfrom community college to a four-year institution or from face-to-face to online environments — there have been few experimental studies directly assessing peer mentoring programs’ impact on

key student indicators (Boyle et al., 2010). Further, to date, there have been no such studies of peer mentoring for students experiencing the double shock of transferring from a largely face-to-face community college to an online, four-year institution.



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SECTION 3: RESEARCH SCOPE AND DESIGN

In Phase 3, research was undertaken to expand on and validate initial findings from Phases 1 and2. In particular, variables previously identified as potentially predictive of performance and persistence at UMUC, as well as newly introduced factors, were used to predict key outcomes

throughout the path model of students’ academic trajector ies. The path model identifies theacademic milestones along the path to completion for community college students. (See figure1.)

Figure 1. Path model of students’ academic trajectory from community college to UMUC.

Research Questions

Predictive modeling was used to answer the following research questions related to students’

performance, persistence (re-enrollment and retention), and ultimate achievement of a credential(graduation)

Performance

1. To what extent do demographic characteristics, community college course taking behaviors, and community college performance metrics predict earning a successful

first-term GPA (2.0 or above) at UMUC?

Persistence

2. To what extent do demographic characteristics, community college course taking behaviors, community college performance metrics, and UMUC first-term GPA predict re-enrolling at UMUC in a semester immediately following the first semester oftransfer?

3. To what extent do demographic characteristics, community college course taking behaviors, community college performance metrics, and UMUC first-term GPA predict retention at UMUC, or re-enrollment within a 12-month window following the first

semester of transfer?

Graduation

4. To what extent do demographic characteristics, community college course taking behaviors, community college performance metrics, and UMUC first-term metrics predict graduation from UMUC?

5. What are the graduation rates of community college transfer students at UMUC?

Community

College

Data

UMUC First

Term GPA Graduation Retention

Re-

enrollment





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SECTION 4: DATA SOURCES

One of the key achievements of Phase 1 of the Kresge research grant was the development of theKDM, an integrated multi-institutional database that aligns the academic work of transferstudents across institutions. Data for the KDM came from three student information systems:

1. Banner - Montgomery College’s student information system2. Datatel - Prince George’s Community College’s student information system3. PeopleSoft – UMUC’s student information system.

All data were made anonymous to protect students’ information. Demographic, academic,transfer, and enrollment data were collected on each student from each institution. Demographicdata included students’ gender, age, marital status, and race/ethnicity. Enrollment data included

course registration, program of study or major, and student status. Community College academicdata included information about students’ academic history prior to transferring to UMUC, suchas course grades, repeated courses, and remedial coursework. Transfer data included the numberof courses transferred, transfer GPA, and prior degrees earned.

The standardization and alignment of data across institutions was accomplished in Phase 2. Dueto the institution-specific design of each student information system, a data dictionary was usedto document the name, definition, type, range and default value of each element as it existed inits native system as well as its transformation and standardization in the KDM. As research progressed, categorical and derived fields were developed and added to the data dictionary toenable researchers to try different predictive models. For this research, over 300,000 courserecords were collected and aligned across institutions.

In addition, online classroom behavior data from UMUC’s online LMS were added to thedatabase for analysis of student behavior in the online classroom. Classroom behavior is defined by over 30 categories of actions taken in the LMS by a student. Examples of typical actions are:login time, access to various modules within the classroom, and posting of or responding to aconference note. Each action that a student made in the classroom was totaled for each day.Daily actions were aggregated by week, enabling researchers to analyze student activity in aclass as it progressed over time. For this research, over 3 million rows of data were available fordata mining. Advising data from UMUC’s customer relationship management system (CRM),Goldmine, were also added to the database for future analyses.

The KDM served as the primary resource for all the analyses and findings for this research grant.

A model of the data included in the KDM is presented in Figure 2.



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Figure 2. The Kresge Data Mart

After reviewing the initial findings from Phases 1 and 2, the community colleges, in

collaboration with UMUC, agreed to provide additional data elements that would enrich theresearch and analysis. As a result, the data in the database were enhanced, resulting in the seconditeration of the Kresge Data Mart (KDM2). These additional data included: students’ completionof developmental education, whether or not students received financial aid, and students’

ACCUPlacer scores.

A total of 493 source and derived variables were analyzed for inclusion in the dataset. Over 300variables were tested as part of data mining analyses. Forty key variables were examined in predictive modeling.

All data are stored on secured servers and have restricted access for the Institutional Research

office, researchers doing analysis on student success, and developers working on the database.



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SECTION 5: SURVIVAL ANALYSIS: REGISTRATION AND WITHDRAWAL IN THE

ONLINE CLASSROOM

Using social network analysis, Dawson (2010) found that visualizing classroom interaction patterns could provide insights into the nature of interactions for high- versus low-achieving

students completing an online course. Dawson (2010) determined that high-performing students primarily interacted with other high-performing students, and likewise, low-performing studentswere more likely to have interactions with other low-performing students. More importantly, inexamining instructor-student interactions, instructors networked with high-performing students(81.7%) at significantly higher rates than they did with low-performing students (34.61%).Social connections in online learning may result in cognitive and learning gains as well. Rovai(2002) found a correlation between levels of engagement in the classroom community andincreased levels of content learning and understanding; this was especially true for females.When this type of social and academic engagement is not present, students may withdraw fromonline learning.

Additionally, students’ academic withdrawal was analyzed using survival analysis. Analyseswere run on a dataset for this study containing 19,190 undergraduate UMUC students in OL1(Online Session 1) in Fall 2011 in 278 distinct courses

An exploratory survival analysis was carried out using a Kaplan-Meier estimator. Survivalanalysis is a statistical technique that can be used to model ―time-to-event‖ data. In this case,this analysis examines the time it took for a student to withdraw from a particular course (inweeks and days) reflected as a time-to-event. Survival analysis generates a table that indicates ahazard (or withdrawal) rate during the semester. Table 2 presents the withdrawal rate for newand returning students by day. Figure 3 presents the hazard function for withdrawal rates of newand returning students.

Table 2. Withdrawal rate for new and returning students by day

Week Number ofstudents

Number ofStudentWithdrawals

CumulativeProportion ofWithdrawal

Withdrawal rate

1 19,190 407 0.98 0.0031

2 18,783 284 0.96 0.0022

3 18,499 251 0.95 0.002

4 18,248 217 0.94 0.0017

5 18,031 295 0.92 0.0024

6 17,736 132 0.92 0.0011



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Figure 3. Hazard function of withdrawal rates for new and returning students.

Students withdraw at a higher rate in Week 1 compared to any other week in the course session,with the exception of Week 5, which is the academic withdrawal deadline. Student status, new orreturning, may significantly affect student withdrawal rate. New students withdraw at a higherrate than returning students. These findings suggest that interventions targeting new studentswith interventions in Week 1 may be appropriate.

At the conclusion of Phase 1, three goals for the completion of the grant were identified andcompleted in Phase 2:

1.

Validate the predictive models and data mining techniques explored in Phase 1 on anexpanded dataset.

2. Build profiles of successful students and their online learning behaviors.3. Develop interventions to improve the success of students transferring from community

colleges to UMUC.



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SECTION 6: PROFILES OF STUDENTS USING DATA MINING

Data mining models were used to examine community college transfer students’ performance atUMUC. Data mining focused on exploratory analyses identifying potential predictors ofstudents’ success and retention at UMUC. The following questions were considered.

1. Which profiles of students at UMUC can be identified?2. To what extent does community college course taking differentiate each success profile at

UMUC?

Data exploration was initially performed by using IBM Modeler, SPSS, SAS JMP 10 Pro, andExcel. Data were transformed and new variables were created as needed. Transformations were performed in Modeler, JMP, and Excel. A variety of black box algorithms were used to develop profiles of students’ success. The black box algorithms employed were Neural Nets, BoostedTrees, and Random Forests.

Profiles of Student Success

In addition to independently considering these two outcomes of student success – UMUC GPAand retention at UMUC – researchers also examined these two predictors jointly. Thus, profilesof student success at UMUC were determined that classified students based on successful GPAand retention. All combinations of the two attributes were examined. Four quadrants wereformed with students evidencing a high or low GPA, and being retained or not. These fourSuccess Quadrants were named Stars, Strivers, Slippers, and Splitters. (See Figure 4.)

Figure 4. Success Quadrants



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Success Quadrant Percent of population Students in Top CoursesStars 59% 62%Strivers 17% 16%Slippers 15% 13%Splitters 9% 9%

Examining the community college course taking behaviors of students belonging to each of thesefour success profiles yielded a number of key conclusions:

Transfer students who took accounting, economics, or higher-level math classes in

community college were more likely to earn a first-semester GPA of 2.0 or above atUMUC (i.e., classified as Stars or Splitters).

Students who took more classes in history, sociology, psychology, and similar socialsciences were more likely to earn a GPA of less than 2.0 at UMUC.

The two low-GPA groups, Strivers and Slippers were less likely to take courses in subjectareas that Stars took.

The Splitters, the smallest group, did not show a distinct pattern of course taking behavior.

In likelihood analyses (i.e., comparing the general proportion of course enrollment to actualenrollment for each cluster), Strivers and Slippers showed nearly identical preferences. Theaverage numbers of classes students took and passed in each subject area were compared forStrivers and Slippers. On average, Slippers passed fewer classes in all of the subject areas preferred by Stars than did Strivers.

In addition, Slippers are noticeably less likely than Strivers to take courses in several areas:

Developmental English Business/management

Sociology

Psychology

The four student success profiles, Stars, Strivers, Slippers, and Splitters, provided a usefulframework for understanding students at UMUC and introduced a new outcome measure thatcombined performance (first-term GPA) and retention (retention within a 12-month window).Factors from the students’ academic profile at the community college, such as course taking

behavior, course load, and change in GPA between the community college and UMUC, were predictive of which student success profile the student would fall in. These results suggest that

student preparedness, particularly in specific areas (e.g., accounting, economics) is important inattaining success at UMUC. More exploration of additional outcome variables, such as re-enrollment and graduation, are planned for phase 3 of this project.



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Further Findings from Data Mining

Using both predictive models and data mining techniques to understand predictors of studentsuccess at UMUC, a number of conclusions may be drawn based on analyses undertaken inPhase 2. These findings emerged from looking across studies and across student sub-populations

and through the use of varied statistical methods.

1. Student success. Overall, students transferring from MC and PGCC are successful atUMUC. Indeed, 60% of transfer students were classified as Stars, indicating that theywere earning a GPA of 2.0 or above in the first term at UMUC and re-enrolling in asubsequent term. Data indicate that earning high grades at the community collegewas an indicator of successful performance at UMUC.

2. Online Classroom Behavior. Patterns in students’ behaviors in the online classroom

have some value in predicting success. In the analysis of online classroom data,students varied greatly in the extent to which they engaged in course content and

course-related activities, with a substantial percentage of students not accessing thecourse or materials at all. Results from this research have indicated that onlineclassroom activity is tied to course success. Though demographic factors and factorsin students’ community college course-taking backgrounds were predictive of successat UMUC and of students’ behaviors in the online classroom, more robust data areneeded to more fully understand the relationship between academic behaviors andstudent success.

3. Change in GPA. A new factor, the change GPA between the community college andthe first-term GPA at UMUC, was introduced. Many students experienced a decreasein GPA when transferring to UMUC; however, the magnitude of this decrease has

predictive value in determine whether or not students are retained at UMUC. Moreresearch is needed to better understand the tradeoff between the difficulty of coursework and a higher GPA to help determine what strategies community colleges mayemploy to better prepare students for their academic transition.

4. Transitional Period. Transferring from community college to a four-year institutionis a particularly challenging transition for students. For one, students’ GPAs tend to

suffer during the first semester at the four-year institution. The magnitude of thechange in GPA seems to have an effect on students’ retention, differentiating theStrivers and Slippers. For another, indicators of students’ preparation, such as course

efficiency and subject areas, were predictors of success at UMUC. This suggests that

students need to prepare for the rigor of UMUC course work. Finally, the number ofcredits students earned prior to transfer may serve as an indicator of students’ preparedness to pursue their study at UMUC.



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SECTION 7: PREDICTIVE MODELING OF STUDENT SUCCESS

Initial Predictive Modeling

Based on exploratory analyses using data mining, predictive modeling, including cluster analysesand logistic regression, were used to model student success using demographic and communitycollege course taking variables. The following questions were considered.

1. What are the demographic profiles of community college students transferring fromMC and PGCC to UMUC?

2. Which factors from students’ demographic profiles and course-taking backgrounds incommunity college predict success at UMUC overall, and in specific courses?

3. What kinds of online learning behaviors do students transferring to UMUC engagein?

In addition to considering variables used in data mining, a number of possible predictors ofsuccess not previously considered were included. For example, students’ course efficiency in

community college (the ratio of credits completed to credits attempted) and change in GPA (thedifference between students’ community college and UMUC GPA) were used as predictors.

Building upon findings from data mining and particularly exploratory analyses of communitycollege course taking behaviors suggesting that students’ course taking behaviors at the

community college may predict performance at the transfer institution, demographic factors andvariables in students’ community college course-taking backgrounds were examined as predictive of success at UMUC.

Predicting Successful GPA

I ndependent Var iables. Three types of independent variables were considered.Specifically, these were students’ demographic characteristics, community college coursetaking behaviors, and course efficiency.

Course efficiency was introduced as a summative measure of community collegestudents’ course taking that was thought to reflect the real-world cost, both in terms oftime and tuition, of students’ not completing courses as intended.

Dependent Variables. Across models run, a dichotomous outcome variable was used.Students’ first-term GPA at UMUC was the target dependent variable, with a GPA of 2.0or above being indicative of successful first-term GPA and a GPA below 2.0 being

indicative of an unsuccessful first-term GPA. First term referred to students’ firstsemester of transfer at UMUC.

Logistic regression was used to determine which independent variables might be predictors ofsuccess in terms of first-term GPA at UMUC. (See Table 3.) Demographic factors, primarilyage, marital status and race, were found to be significantly related to success at UMUC.Specifically, older or married students were found to have higher GPAs at UMUC. When



35

compared to white students, students self-identifying as African American, Hispanic, or with anunspecified race/ethnicity tended to have a significantly lower GPA at UMUC.

Table 3. Results of Multivariate Logistic Regression Analysis of Success at UMUC (N=7615)

Variable B S.E. Sig. Exp(B)

Age .268 .027 .000 1.308*Gender -.083 .060 .164 .920

Asian Ethnicity -.055 .119 .643 .946

African American -.876 .081 .000 .417*

Hispanic Ethnicity -.380 .113 .001 .684*

Unspecified Race -.470 .104 .000 .625*

Married .422 .085 .000 1.525*

English Course Taken -.187 .081 .021 .829*

Math Course Taken .345 .072 .000 1.413*

Speech Course Taken .078 .070 .269 1.081

Computer Course Taken -.078 .063 .218 .925

Honors Course Taken .467 .166 .005 1.594*Remedial Course Taken .029 .068 .674 1.029

Online Course Taken -.175 .059 .003 .839*

Course Efficiency .241 .012 .000 1.273* Note: White was used as reference category for race/ethnicity variables thus not used in the logistic regression

model.

*Statistically significant

Table 3 also shows that prior coursework was related to success at UMUC. Math courses andhonors courses were related to success at UMUC, while online courses at the community collegelevel were inversely related to success. Finally, course efficiency at the community college was

found to be a significant predictor of success at UMUC.

Predicting Re-enrollment

I ndependent variables. A number of independent variables were used in these analyses:students’ community college GPA, race/ethnicity, gender, and age were used as controlvariables. Then, the predictor of interest, delta GPA, was entered into the model.

Dependent Variables. The outcome of interest in these analyses was retention atUMUC, defined as a student’s enrollment in a course at UMUC within 1-year of theentering semester. A binary coding (0 or 1) was used depending on whether or not

student was retained. Overall, the majority of transfer students (76.35%) were retained at UMUC. After controllingfor demographic factors and community college GPA, students’ change in GPA upon

transferring to UMUC was nonetheless a significant predictor of retention. (See Table 4.)



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Table 4. Results of Multivariate Logistic Regression Analysis of Retention at UMUC (N=12637)

Variable B S.E. Sig. Exp(B)

Age -0.13 .002 0.00 1.19*

Gender 0.15 0.04 0.00 1.17*

Hispanic 0.19 0.08 0.03 1.21*

African American 0.34 0.05 0.00 1.40*Asian 0.43 0.09 0.00 1.54*

Race/EthnicityUnknown

0.17 0.07 0.02 1.19*

Community CollegeGPA

0.65 0.02 0.00 1.91*

Delta GPA 0.64 0.02 0.00 1.89* Note: Excluded from the model were students classified as Non-resident alien, American Indian, Hawaiian/Pacific

Islander, or Two or more ethnicities, as these were not significant predictors in the model.

*Statistically significant

1.

Demographics. In various analyses, students’ age and marital status were repeatedlyfound to be predictors of success at UMUC. Older, married students tended to earnhigher GPAs and be retained. These findings may be indicative of students’ greatermaturity or dedication to their education goals. At the same time, minority status (i.e.,African American or Hispanic) was associated with lower performance at UMUC. Moreinvestigation needs to be done to determine how best to reach these underserved populations and improve success.

2. Community College Courses. Course efficiency in community college was determined

to be a predictor of success at UMUC. The higher the ratio, the more likely the studentwill succeed. Similarly, students who took math or honors courses were more likely to

succeed. These results point to the importance of considering not only quantitativemeasures of students’ course work (e.g., course load) but also qualitative aspects ofstudents’ work (e.g., honors and math).

Updated Predictive Modeling

Expanding on initial predictive modeling, models were enhanced to incorporate new data,introduced as part of a second wave of data sharing with the community colleges. Predictivemodels were further developed and validated in updated predictive modeling. Specifically,models were constructed predicting key milestones in students’ successful completion of a four-year institution after transferring from a community college. These were:

1)

Earning a successful first-term GPA 2) Re-enrollment in the next semester after transfer3) Retention within a 12-month window following4) Graduation with a 4-year, 6-year, and 8-year timeframe.



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Each of the final predictive models is presented in turn. In developing predictive models, over35 demographic, community college course taking behavior, community college performance,and UMUC first-term variables were examined as potentially offering predictive power. Theseare listed in Table 5.

Table 5. Variables considered in predictive modeling

Type of Predictor Listing of Variables Examined

Demographic Characteristics AgeGenderRace/EthnicityMarital StatusReceiving a PELL Grant at the CC

Community College Course

Taking

Math EnrollmentEnglish EnrollmentSpeech Enrollment

Honors EnrollmentDev Education EnrollmentEnrollment in an Online CourseSuccessful Course CompletionSuccessful Math CompletionSuccessful English Completion

Successful Speech CompletionSuccessful Computer-related CrsCompletion

Dev Writing CompletionDev Reading CompletionDev Math CompletionExempt from Dev EnglishExempt from Dev MathRepeating a Course

Community College

Summative Metrics

Community College GPACC Credits EarnedCC Credits AttemptedPercentage of Courses Withdrawn FromReceiving an Associate Degree

UMUC First-term Metrics UMUC First-term GPAUMUC First-term Enrollment Full-Time/Part-TimeUMUC First-term Credits AttemptedUMUC First-term Credits EarnedUMUC First-term Credits Transferred



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Models were constructed to maximize statistical fit while being as parsimonious as possible (i.e.,including as few predictors as possible). Model fit was determined by examining the percentageof variance in the outcome variable explained by predictors in the model as well as byconsidering accuracy of classification (e.g., categorizing students as graduating or not).

In predictive models, hierarchical logistic regression was used. Logistic regression predicts the probability of a dichotomous outcome being achieved. As such, all target outcome variableswere dichotomized – for example, students’ first-term GPA at UMUC was recoded as beingeither successful (≥2.0) or not. In hierarchical regression, variables are entered as blocks or insteps, so that variables entered in at a previous step are controlled for when additional predictorsare added to the model. Across models, order of entry was: demographic characteristics,community college course taking, community college performance summative measures, and ifconsidered, first-term at UMUC indices.

Table 6 presents descriptives of each of the dichotomized outcome variables examined.

Table 6. Descriptives associated with each target outcome variableDependent Variable Population Performance

Successful First-term GPA 76.3% earn a GPA ≥2.0 in their first semester

(n=6151)

Re-enrollment 66.7% of students re-enroll in a subsequentsemester (n=5376)

Retention 79.1% of students are retained within a 12-monthwindow (n=5376)

Graduation to Date (Spring 2014) 52.7% of students have graduated to date (n=5454)

Population

Predictive modeling was run on 8,058 transfer students from MC and PGCC. These werestudents whose first semester of transfer to UMUC occurred between Spring 2005 and Spring2012. Students enrolled in continuing education courses or earning a second bachelor ’s degreewere excluded from these analyses. Demographic characteristics are presented in Table 7.

Table 7. Sample Demographic Characteristics (n=8058)

Age 28.6 years old (SD=8.4)

Gender Female: 57.6% (n=4638)Male: 41.2% (n=3323)

Race/Ethnicity White: 24% (n=1956)

African American: 43.5% (n=3509)Asian: 10.4% (n=839)Hispanic/Latino: 10.2% (n=821)American Indian: 0.9% (n=75)Unspecified: 14.0%

Predictive models for earning a successful first-term GPA, re-enrollment, and retention were run.



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Predicting Earning a Successful First-term GPA

Dependent variable. Successful first-term GPA was used as the dependent variable.

Independent variables. Three types of variables were used to predict successful first-termGPA. These were: (a) students’ demographic characteristics, (b) community college coursetaking behaviors, and (c) summative measures of community college performance.

Among the independent variables of students’ community college course taking behaviorexamined, rate of successful course completion at the community college, both overall and inspecific subject areas was computed. Successful course completion was defined as the ratioof courses students’ completed with a grade of C or above to the total number of courses inwhich students were enrolled. In specific subject areas, successful course completionreferred to the ratio of courses in that subject area in which students earned a grade of C orabove to the total number of courses in that subject area.

While course taking behaviors focused on students’ specific academic experiences in

community college, summative measures (e.g., GPA) looked at students’ community college

careers, overall.

The model was overall significant, X 2(21) = 756.43, p<0.001, correctly classifying 76.8% of

students as earning a successful first-term GPA or not. Cox and Snell’s R 2 suggested that the

model explained 9.1% of variance in earning a first-term GPA, while Nagelkerke’s R 2 suggestedthat 13.7% of variance had been explained. (See Table 8.)

Table 8. Predicting first-term GPA using demographic characteristics, community collegecourse taking behaviors, and summative measures of CC Background

β SE(β) Significance β*

Demographic Characteristics

Gender* 0.12 0.06 0.043 1.13

Age** 0.01 0.00 0.001 1.01

R a c e / E t h n i c i t y :

C o m p a r e d t o

W h i t e S t u d e n t s

Black*** -0.36 0.08 0.000 0.70

Hispanic/Latino -0.10 0.11 0.367 0.91

Asian -0.06 0.11 0.57 0.94

American Indian -0.28 0.27 0.30 0.76

Race Not Specified* -0.23 0.10 0.021 0.79

Marital Status** 0.25 0.08 0.001 1.29PELL Grant Recipient*** -0.30 0.07 0.000 0.74

Community College Course Taking

Successful Course Completion

Overall***

1.63 0.21 0.000 5.08

Successful Math Completion** 0.20 0.06 0.004 1.22

Successful English Completion** 0.18 0.06 001 1.20



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Developmental Math

Completion**

0.27 0.08 0.001 1.31

Developmental WritingCompletion

-0.08 0.10 0.38 0.92

Developmental Reading

Completion

-0.07 0.11 0.48 0.93

Developmental Math Exempt -0.03 0.08 0.747 0.97

Developmental English Exempt -0.11 0.05 0.07 0.89

Repeated Courses -0.27 0.07 0.000 0.76

Summative Measure of CC Background

GPA*** 0.22 0.05 0.000 1.25

Credits Earned -0.001 0.002 0.62 1.00

Associates Received*** 0.39 0.08 0.000 1.47 Note: *sig. at 0.05 level, ** sig. at 0.01 level, *** sig, at 0.001 level

In terms of demographic characteristics, gender, age, and marital status were all significant

predictors in the model. Specifically, students who were female, older, and married weresignificantly more likely to earn a successful first-term GPA at UMUC. At the same time,students’ reporting their race/ethnicity as African American or not designating a race/ethnicity

were less likely to earn a successful first-term GPA. Further, receiving a PELL grant at thecommunity college, as an indicator of financial need, decreased the likelihood of studentsearning a successful first-term GPA.

In examining indicators associated with students’ community college course taking behaviors,students’ overall rate of successful course completion and rate of successful math completion,and successful English completion were all significant predictors in the model. Further,students’ completion of developmental math was a significant predictor in the model.

Looking to summative measures of community college performance, cumulative GPA, creditsearned, and earning an Associate degree were all significant predictors.As can be seen by examining the standardized beta, holding all else constant in the model,students’ overall rates of successful course completion carry the most impact in increasing

students’ probability of earning a successful GPA. Standardized betas may be interpreted as, for

a 1 standard unit increase in successful course completion; students were 5.08 standarddeviations more likely to earn a successful first-term GPA.

Predicting Re-Enrollment

Dependent variable. Re-enrollment was defined as enrolling in a semester immediatelyfollowing the first term of transfer. Re-enrollment was binary coded as re-enrolled (1) or not(0)

Independent variables. Four types of variables were used to predict students’ re-enrollmentand retention. These were: (a) students’ demographic characteristics, (b) community college

course taking behaviors, (c) summative measures of community college performance, and (d)first-semester performance at UMUC. While course taking behaviors focused on students’



41

specific academic experiences in community college, summative measures (e.g., GPA)looked at students’ community college careers, overall.

The overall model for re-enrollment was significant, X 2(19) = 1063.24, p<.001. The model was

able to correctly classify 71.6% of students as re-enrolling or not. Pseudo R 2 measures of effect

size ranged from an estimated 12.5% of variance in re-enrollment explained (Cox & Snell’s R

2

)to 17.4% of variance (Nagelkerke’s R 2) explained. (See Table 9.)

Table 9. Predicting re-enrollment using demographic characteristics, community college course

taking behaviors, summative measures of CC backgrounds, and UMUC first-term indicators



Gender*** 0.20 0.05 0.000 1.22

Age 0.00 0.00 0.638 1.00


C o m p a r e d t o


Black* 0.17 0.07 0.013 1.19

Hispanic/Latino -0.02 0.10 0.83 0.98

Asian 0.07 0.10 0.492 1.07American Indian 0.19 0.27 0.469 1.21

Race Not Specified* 0.05 0.09 0.60 1.05

Marital Status** 0.24 0.07 0.001 1.28

PELL Grant Recipient 0.13 0.07 0.065 1.14


Repeated a Course** 0.17 0.06 0.005 1.19

Enrolled in a Developmental

Course***

0.21 0.06 0.001 1.23

Exempt from Developmental

Math**

0.22 0.08 0.004 1.25

Summative Measures of Community College BackgroundsCommunity College GPA** -0.11 0.04 0.005 0.89

Cumulative Credits Earned at CC -0.00 0.00 0.208 1.00

Earned an Associate Degree -0.13 0.07 0.059 0.88

First Term at UMUC

First-term GPA*** 0.26 0.02 0.000 1.30

First-term Credits Earned*** 0.14 0.01 0.000 1.14

Enrolled Full Time -0.16 0.08 0.054 0.86

Cumulative Credits

Transferred***

0.01 0.00 0.000 1.01

Note: *sig. at 0.05 level, ** sig. at 0.01 level, *** sig, at 0.001 level

Examining demographic characteristics determined that gender and marital status were bothsignificant predictors in the model. Specifically, being female and married increased students’ probability of re-enrolling in a subsequent term at UMUC. Further, unlike with first-term GPA,race/ethnicity designated as African American or unspecified were significantly positive predictors of re-enrollment.



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In examining students’ community college course taking behaviors, different predictors than

those found to be significant in predicting performance were identified. Specifically, students’

likelihood of re-enrollment increased if they either enrolled in a developmental course or wereexempt from developmental math at their community college. Surprisingly, repeating a course atthe community college was found to be a significant, positive predictor of re-enrollment; in other

words, re-taking a course in community college increased the likelihood that students’ would re-enroll at UMUC. While this finding may appear to be counter-intuitive, it may reflect the factthat students willing to retake courses may be more committed to achieving an academiccredential, despite challenges they may experience.

Summative measures of students’ community college background found only community collegeGPA to be a significant predictor in the model. Further, despite being a positive predictor offirst-term GPA, community college GPA was a negative predictor of persistence or re-enrollment. More work is needed to understand why this may be the case. In part, thosestudents earning a high GPA at community college, despite likewise earning a successful GPA atUMUC, may be more averse to ―transfer shock‖ due to the new four-year context and associated

academic demands.

Looking at first-term UMUC indicators, as may be expected, first-term GPA and total number ofcredits earned were significant predictors of re-enrollment. Further, the cumulative number ofcredits transferred was a significant positive predictor in the model. Number of creditstransferred may reflect the pragmatic value of community college course work in helpingstudents’ meet four -year institutional academic requirements. Examining standardized betacoefficients in the model reveals first-term GPA to be the strongest predictor of re-enrollment atUMUC. Indeed, for every standard unit increase in UMUC GPA, the probability of re-enrollment increases by 1.30 standard deviations.

Predicting Retention

Dependent variable. Retention was defined as re-enrolling within a 12-month windowfollowing the first term of transfer. Retention was binary coded as students being retained(1) or not (0)

Independent variables. Four types of variables were used to predict students’ re-enrollmentand retention. These were: (a) students’ demographic characteristics, (b) community college

course taking behaviors, (c) summative measures of community college performance, and (d)first-semester performance at UMUC. While course taking behaviors focused on students’specific academic experiences in community college, summative measures (e.g., GPA)looked at students’ community college careers, overall.

The model was overall significant, X 2(17) = 1271.59. 80.5% of cases were correctly

classified as retained or not. Effect size measures suggest that between 14.8%, according toCox and Snell’s R

2, and 23.1%, according to Nagelkerke’s R

2, of variance in retention wasexplained by the model. (See Table 10.)



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Table 10. Predicting retention using demographic characteristics, community college course

taking behaviors, summative measures of CC backgrounds, and UMUC first-term indicators

Note: *sig. at 0.05 level, ** sig. at 0.01 level, *** sig, at 0.001 level

As with re-enrollment, a number of demographic characteristics proved to be significant. Againthese were gender and marital status, with married females being more likely to persist. AfricanAmericans and those having an Unspecified race/ethnicity were both found to be positive predictors of retention.

Examining students’ community college course taking determined that repeating a course, and

being exempt from or completing developmental math were all three positive predictors in the

model. The presence of math-related variables seems to suggest the important role of the mathsubject area in determining students’ academic preparedness and predicting students’

persistence.

In examining summative measure of community college academic backgrounds, cumulativeGPA at the community college was again found to be a negative predictor in the model. As acontrast, first-term GPA at UMUC was a significant and positive predictor of retention. Thetotal number of credits attempted in the first term, the dichotomized variables part-time or full



Gender*** .180 .063 .004 1.197Age at Transfer -.005 .004 .170 .995


C o m p a r e d t o


Black** .231 .081 .005 1.259

Hispanic/Latino .027 .113 .810 1.028

Asian .017 .115 .883 1.017

American Indian -.206 .291 .480 .814

Race Not Specified* .048 .104 .648 1.049

Marital Status** .246 .090 .006 1.279

PELL Grant Recipient .148 .084 .079 1.159


Repeated a Course** .223 .065 .001 1.249

Completed Developmental

Math*.174 .084 .037 1.191

Exempt from Developmental Math .075 .089 .399 1.078

Summative Measures of Community College Backgrounds

Community College GPA** -.127 .043 .003 .881

First Term at UMUC

First-term GPA*** .590 .022 .000 1.803

First-term Credits

Attempted***.160 .013 .000 1.174

Enrolled Full Time*** -.716 .131 .000 .489

Credits Transferred First-

term***.013 .001 .000 1.013



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time status, and the number of credits transferred from the community college were all found to be significant predictors of retention.

A number of additional factors as predictive of retention were considered. These included,receiving an Associate degree at the community college, credits earned and attempted at the

community college, average community college course load. Further, community college coursetaking behaviors were not significantly predictive of retention – these included enrollments inMath, English, Computer, or Speech courses as well as enrollment in Honors, Developmental,and Online courses. Successful course completion indices were also not found to besignificantly associated with retention at the transfer institution.

Predicting Graduation

Sample. While previous models predicting earning a successful first-term GPA, re-enrollment, and retention were run on the full cohort of MC and PGCC transfer studentsenrolled in their first semester at UMUC between Spring 2005 and Spring 2012 (n=8050),

the graduation model was run on a more limited sub-sample. As we were interested in predicting students’ eight-year graduation rate, only data from cohorts enrolled from Spring2005 – Spring 2006, reflecting six semesters of data, were used. The remaining studentcohorts were not examined as they do not yet have eight years since their first term of entry.The graduation model was run on a reduced sample of 2040 students.

Dependent variable . Graduation was defined as earning a first credential from UMUC (i.e.,Certificate, Associate, Bachelor’s) within an 8-year period, based on cohort of entry. Theeight-year graduation rate was chosen because the population in this study reflected non-traditional students, who may be part-time or stop-out for various personal, family, andfinancial reasons; eight years provides a graduation window which gives students sufficienttime to earn a credential. Graduation was binary coded as students either graduating within 8years (1) or not (0).

I ndependent var iables . Four types of variables were used to predict students’ re-enrollmentand retention. These were: (a) students’ demographic characteristics, (b) community college

course taking behaviors, (c) summative measures of community college performance, and (d)first-semester performance at UMUC. While course taking behaviors focused on students’specific academic experiences in community college, summative measures (e.g., GPA)looked at students’ community college careers, overall.

The model was overall significant, X 2(17) = 1271.59. 69.6% of cases were correctly

classified as retained or not. Effect size measures suggest that between 20.0%, according toCox and Snell’s R 2, and 26.7%, according to Nagelkerke’s R 2, of variance in graduation wasexplained by the model. Table 11 includes a model summary.



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Table 11. Predicting graduation using demographic characteristics, community college

course-taking behaviors, measures of CC experience, and UMUC first-term indicators



Gender .029 .106 .785 1.029

First_Term_Age*** -.023 .007 .000 .977Minority Status -.169 .104 .104 .845

Receiving PELL at CC -.262 .167 .116 .770


Math Enrollment at CC* .329 .135 .015 1.390

Percent Ws at CC -.670 .381 .079 .512

Summative Community College Measures

Receiving AA at CC .127 .129 .325 1.135

CC CUM GPA* .168 .081 .038 1.184

CC Credits Earned .005 .003 .059 1.005

UMUC First-term Indicators

UMUC First-term GPA*** .482 .044 .000 1.619UMUC First-term Credits Earned*** .021 .002 .000 1.022 Note: *sig. at 0.05 level, ** sig. at 0.01 level, *** sig, at 0.001 level

Looking at individual predictors in the model, in terms of demographic traits, only first-term agewhen transferring to UMUC was found to be significant. Further, first-term age was a negative predictor, such that being younger increased students’ likelihood of graduating.

At the community college, enrolling in a course in the Math subject area was a significant predictor of graduation. Although other math-related indices, including completingdevelopmental math and the rate of successful math course completion, were examined as

predictors, the dichotomized variable reflecting whether or not students had enrolled in a mathcourse proved to be a sufficient indicator predicting graduation.

In terms of summative community college course taking indicators, community collegecumulative GPA was a significant positive predictor.

First semester at UMUC indices were all significant and positive predictors. Specifically,students’ GPA in the first semester and the number of credits earned in their first term weresignificant positive predictors. In examining standardized beta coefficients, first-term GPA wasthe strongest predictor of eight-year graduation, followed by students having taken a math courseat the community college. Again, taking a math course could be interpreted as a variable

indicative of students’ academic preparation or of students’ willingness to complete requirementsnecessary for graduation.

Summary of Results from Predictive Modeling

Looking across predictive models, Table 12 presents information regarding which indicatorswere significant predictors across models.



46

Table 12. Significant Predictors for First-term GPA, Re-enrollment, Retention, and Graduation

Predictor First-Term GPA Re-Enrollment Retention Graduation

Demographic Factors

Gender + + +

First-term Age + -Race/Ethnicity Black (-)

Unspecified (-)Black (+)Unspecified (+)

Black (+)Unspecified (+)

Marital Status + + +

PELL Grant -


Overall Successful CourseCompletion

Successful Math Completion

Successful English Completion

Repeated a Course + +

Enrolled in a Developmental Course +Exempt from Developmental Math +

Completed Developmental Math +

Enrolled in Math at CC +

Community College Summative Measures

Community College GPA + - + +

Credits Earned

Associates Degree Received +

UMUC First Term Factors

First-term GPA N/A + + +

First-term Credits Earned N/A + +

Cum Credits Transferred N/A + +

Enrolled Full Time N/A +

First-term Credits Attempted N/A +

Looking across the models helped identify a number of predictors which seem to be associatedwith both performance (e.g., first-term GPA) and persistence (i.e., re-enrollment, retention, andgraduation). Females and those who were married were more likely to both earn a successfulfirst-term GPA, as well as to persist to graduation. Interestingly, African American status,though negatively associated with performance in the first semester, was positively associatedwith persistence. This may suggest that although minority status has typically been consideredto be an at-risk factor for students’ success (Greene, Marti, McClenney, 2008), some studentsmay benefit from the flexibility offered by an online institution.

Repeating a course was significantly associated with persistence measures – re-enrollment andretention. This may indicate that while students struggle, being persistent in completingnecessary course work is associated with persistence. Further, a variety of factors related tostudents’ math course taking behavior (e.g., being exempt from developmental math, completing



47

math at the community college, enrolling in math) were found to be associated with persistenceas well as graduation.

Examining students’ overall community college performance, community college GPA was

significantly associated with both performance and persistence. At UMUC, first-term GPA was

associated with re-enrollment, retention, and graduation. Likewise, the number of credits earnedin the first term was associated with re-enrollment and graduation, as they may reflect students’commitment to educational goals and credential completion; likewise, being enrolled full-timewas associated with retention. Altogether, students’ volume of course taking in the first semester

(i.e., credits attempted, credits earned, full-time enrollment) were associated with persistence.

While this section presented results from predictive modeling for first-term GPA, re-enrollment,retention, and graduation, the subsequent section delves into specific aspects of students’

experience at UMUC. Specifically, Section 8 examines (a) the relation between students’ onlineclassroom engagement and course performance as well as (b) students’ motivational and self -regulatory profiles.



48

SECTION 8: GRADUATION RATES

In addition to predicting graduation, graduation rates for students in our target cohort (i.e., whosefirst semester of transfer to UMUC was between Spring 2005 to Spring 2012) were examined.Students were divided into cohorts of entry depending on their first semester of enrollment at

UMUC. Cohorts were determined by fiscal year, including the Summer, Fall, and Spring termsof a given year (e.g., Summer 2005, Fall 2005, and Spring 2006).

Graduation rates were examined for students earning a first-time bachelor’s degree from UMUC.

Term of graduation was likewise determined by fiscal year, and graduation rates for 1 to 8 year periods were calculated. Not all starting cohorts at UMUC had been enrolled for a full eightyears; graduation rates were computed for as many years as students were at UMUC.

Rates of transfer were computed for students, overall, as well as separately for students comingfrom each of the community colleges. Specifically, graduation rates were computed for studentstransferring from Prince George’s Community College (n=3220) and from Montgomery College

(n=4724).

Table 13. Graduation rates for MC and PGCC transfer students, FY 06 – FY 12

Notes: N=7944; Bachelor Graduates for the entire population to Spring 2014 = 3051 (38%)

Examining graduation rates determined that, overall, community college transfer students weresuccessful in earning a credential at UMUC. The eight-year graduation rate was 49%, while the6-year graduation rates ranged from 44% to 40% of students in each cohort graduating. Theseare impressive numbers compared to national rates as well as to UMUC overall rate.

FY Cohorts Graduation Rates by Subsequent Fiscal Year

Year # Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8

FY06 1017 61 6% 217 21% 305 30% 378 37% 424 42% 456 45% 481 47% 498 49

FY07 1164 59 5% 189 16% 293 25% 366 31% 439 38% 481 41% 506 43%

FY08 1138 49 4% 210 18% 316 28% 386 34% 428 38% 460 40%

FY09 1212 80 7% 240 20% 378 31% 463 38% 507 42%

FY10 1333 77 6% 264 20% 407 31% 478 36%

FY11 1300 97 7% 288 22% 416 32%

FY12 780 79 10% 186 24%



49

SECTION 9: EXAMINING LEARNER BEHAVIOR IN THE ONLINE CLASSROOM

In addition to using predictive modeling to predict key academic outcomes, data mining wasused to examine the relation between students’ online classroom engagement and performance.

The online classroom activities come from the LMS. The LMS data came from a proprietary

classroom management system call WebTycho. WebTycho was replaced with Desire2Learn(D2L) in 2014. However, for the population of students in this study, WebTycho provided thedata on student interactions in the classroom between Spring 2011 and Fall 2013.

Data mining techniques included Neural Nets, Boosted Trees, and Bootstrap Forest.A model’s misclassification rate (the proportion of wrong predictions) was used to evaluate theeffectiveness of the models. For neural nets, R-squared levels were recorded for both the trainingsubset (on which the model was developed) and the validation subset (on which the model wastested).

The analytical focus was on undergraduate students who had transferred to UMUC from

Montgomery College or Prince George’s Community College. The LMS dataset containedapproximately 2.3 million rows, each one representing a unique student/class/term/daycombination.

The data were examined at two levels: (a) course work and (b) student-level. While at the coursework level each student’s enrollment was treated as a unique record, such that a student couldhave been listed in the course work file multiple times, the student level file ensured that therewas one record per student.

The dataset included only 8-week undergraduate courses only and provided week-by-week totalsof each action taken by students in the online classroom. Students’ online classroom activitieswere then matched to records in the KDM.

There were a total of 30 different online classroom behaviors, 22 of these behaviors were notwell represented in the student data. The remaining eight online classroom behaviors wereevaluated to determine which had the most variability and seemed to be key indicators of studentengagement. Four online classroom behaviors were selected for examination:

Open classroom

Create a response note

Launch a conference

Read a conference

These actions also served to differentiate students’ course performance. Although 8-weekcourses were examined, the scope of analysis was restricted to the first 3 weeks of a class, asthere was limited variation throughout the remainder of the course duration and prediction ofengagement-related factors early in the course facilitated the possibility of intervention.



50

The focus was further narrowed to include only student/class/term enrollments that earned agrade of A, B, C, D or F. (See Figure 5.) Grades of AU, P, S (audit), FN (non-attendance),Incomplete, and Withdraw were excluded.

Figure 5. Distribution of course grades

All models were run on the final dataset using four classroom activities during weeks 1 – 3 of an8-week course where the final grade earned was A, B, C, D or F, and only for students who werefound in the KDM. The number of rows in this dataset is 28,021 and the number of uniquestudents is 4,277. Table 14 presents the averages for these unique student/course/sessioncombinations.

Table 14. Mean and median values for actions in the online classroom

Action Median Mean

Open Class 25 30.67Create Response Note 10 12.63Launch Conferencing 22 27.52Read Conference Note 206 310.36

The distribution of the aggregate online classroom behaviors (compared to the median of allstudents) across 8 weeks by grade received are presented in Figure 6.

Figure 6. Overall level of online classroom engagement for students by grade.

35.1%

32.0%

17.6%

5.8%

9.5%

0%

5%

10%

15%

20%

25%

30%

35%

40%

A B C D F



51

Four sets of findings will be discussed. First, the relation between students’ online classroom behaviors, at the course level, and course performance was examined. Second, the association between online classroom behaviors and course performance at the student level, across all oftheir enrollments was analyzed. Third, the relation between an overall online classroomengagement measure and performance was considered. Fourth, modeling the potential relation between online classroom performance and re-enrollment was considered; however, did not prove to be a fruitful avenue of investigation.

Online Classroom Behaviors and Class Performance

Because these LMS actions have uneven distributions, the median was chosen as a representationof the average value rather than the mean. In each row, ―≥ med‖ flags were generated indicatingwhether the values of the key LMS actions were at or above the median or below the median.

Figure 7 displays the grades distributions at the course level for students based on whether theironline classroom engagement, across the four actions, was above or below the median.

-4

-3

-2

-1

0

1

2

3

4

1 2 3 4 5 6 7 8

E n g a g e m e n t I n d e x

Week

Level of Student Engagement by Grade

A grades

B grades

C grades

D grades

F grades



52

Figure 7. Distribution of grades above and below the median level of engagement

As can be seen in the figures above, there are almost twice as many ―A‖ grades in the ―above

median‖ category as in the ―below median‖ category, and almost three times as many ―F‖ grades

in the ―below median‖ category as in the ―above median‖ category.

Higher counts of key LMS actions are associated with the higher grades. Similarly, the lowestgrades are typically found along with the lowest LMS counts. In order to meaningfully measurehow far above or below the median a LMS action value is, however, the values needed to beindexed to a consistent scale to compensate for the differing ranges. A median difference index

(MAD) was created to capture the deviation between a student’s behaviors in the onlineclassroom and the median number of such behaviors manifest in the overall sample.

Both absolute values and MAD (i.e., median difference) indices were used in predictive models.Model summary information is presented below; however, model fit was modest with a highmisclassification rate. (See Table 16.)

Table 16. Model fit information for predicting successful course completion.Response

Variable

Predictors

(Weeks 1-3, Grades ABCDF)

Model Type Model Performance

Validation Set Results

Course Grade Open Class (OC)

Create Response Note (CRN)Launch Conferencing (LC)Read Conference Note (RCN)

Bootstrap

Forest

R-squared: 0.164

Misclassification rate: 59.6%

Course Grade OC ≥ median CRN ≥ median LC ≥ median RCN ≥ median

Neural net R-squared: 0.126Misclassification rate: 61.9%

15%

5%

8%

4%

22%

14%

31%

33%

25%

45%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Below median Above median

A

B

C

D

F



53

Response

Variable

Predictors

(Weeks 1-3, Grades ABCDF)

Model Type Model Performance

Validation Set Results

Course Grade OC difference-from-median indexCRN difference-from-median indexLC difference-from-median indexRCN difference-from-median index

Neural net R-squared: 0.178Misclassification rate: 59.5%

Course Grade Sum of median difference indexes Neural net R-squared: 0.155Misclassification rate: 59.7%


Bootstrapforest

R-squared: 0.251Misclassification rate: 56.5%

Course Grade Open Class (OC)Create Response Note (CRN)Launch Conferencing (LC)Read Conference Note (RCN)

Neural net R-squared: 0.155Misclassification rate: 60%

Course Grade Open Class (OC)

Create Response Note (CRN)Launch Conferencing (LC)Read Conference Note (RCN)

Bootstrap

Forest

R-squared: 0.162

Misclassification rate: 60%


Course level ≥ 300

BootstrapForest



Subject area

BootstrapForest


Student Level Online Classroom Behaviors and Course Performance

In the next set of analyses, the analysis shifted from class-level to student-level. Thestudent/course/term dataset (28,021 rows) was rolled up to yield 4,277 rows, each representing aunique student.

In the rolled-up dataset, the four key online classroom actions were represented by the mean oftheir values across that particular student’s entries in the previous dataset. Similarly, each

student’s grades from each class were averaged. A ―0/1‖ flag was created to indicate if theaverage grades were ≥ 2.0. This value was used as the response variable for most of the predictive models. Because this variable was categorical and not continuous, each model’s

accuracy calculations could also show misclassification rates.

The engagement calculations for this dataset followed the same procedure as the course levelcalculations, except with student-level figures.



54

Different combinations of the variables were tested as predictors of student success. The primarymodeling methods used were Bootstrap Forest, Boosted Tree, and Neural Net. (See Table 17.)

Table 17. Data mining results for online classroom activities and course performance Response variable

Predictors (3-wk LMS rolled up +

addl )

Model

type

Results - validation set

Grades_Mean >=2.0

OPENCLASS_MeanCREATERESPONSENOTE_MeanLAUNCHCONFERENCING_MeanREADCONFERENCENOTE_Mean

Bootstrapforest

R-squared: 0.300Misclassification rate: 0.196

Grades_Mean >=2.0


Boostedtree


Grades_Mean >=

2.0


Neural net

R-squared: 0.304

Misclassification rate: 0.210

Grades_Mean >=2.0

Difference index - OCDifference index - CRNDifference index - LCDifference index - RCN

Bootstrapforest


Grades_Mean >=2.0


Boostedtree


Grades_Mean >=2.0

Difference index - OC

Difference index - CRNDifference index - LCDifference index - RCN

Neural net R-squared: 0.309Misclassification rate: 0.199

Grades_Mean >=2.0

Difference index - OCDifference index - CRNDifference index - LCDifference index - RCNCC_GRADE_POINT_AVERAGE

Bootstrapforest


Grades_Mean >=2.0


CC_GRADE_POINT_AVERAGE

Boostedtree

R-squared:0.370

Misclassification rate:0.199

Grades_Mean >=2.0

Difference index - OCDifference index - CRNDifference index - LCDifference index - RCNCC_GRADE_POINT_AVERAGE

Neural net

R-squared:0.371

Misclassification rate:0.188

Grades_Mean >=2.0

Sum of difference indexesBootstrap

forestR-squared: 0.098




55

Response variablePredictors (3-wk LMS rolled up +

addl )

Model

typeResults - validation set

Grades_Mean >=2.0

Sum of difference indexesBoosted

treeR-squared: 0.301


Grades_Mean >=2.0 Sum of difference indexes Neural net R-squared: 0.192Misclassification rate: 0.200

Grades_Mean >=2.0

Sum of difference indexesCC_GRADE_POINT_AVERAGE

Bootstrapforest


Grades_Mean >=2.0


Boostedtree


Grades_Mean >=2.0


Neural netR-squared: 0.356


Grades_Mean >=

2.0

OPENCLASS_Mean

CREATERESPONSENOTE_Mean

Bootstrap

forest

R-squared: 0.330


Grades_Mean >=2.0

Difference index - OCDifference index - CRN

Bootstrapforest


Grades_Mean >=2.0

Difference index - OCDifference index - CRNCC_GRADE_POINT_AVERAGE

Bootstrapforest


Grades_Mean >=2.0

OPENCLASS_MeanCREATERESPONSENOTE_MeanLAUNCHCONFERENCING_MeanREADCONFERENCENOTE_MeanCC_GRADE_POINT_AVERAGE

Bootstrapforest


Grades_Mean >=2.0


Boostedtree


Grades_Mean >=2.0


Neural netR-squared: 0.354


These findings suggest that indexed measures of student engagement based on key onlineclassroom actions, along with a student’s community college GPA, can help predict whether ornot that student will achieve course success at UMUC.





57

Figure 8. Student engagement by course performance.

Examining overall engagement, students earning high grades also demonstrated higherengagement and vice versa.

Modeling Retention

Models examining LMS behaviors as associated with retention did not prove to be fruitful predictors. This was likely because the majority of students were retained within a 12-monthwindow, limiting variance. (See Figure 9.)

Figure 9. Retention of community college transfer students.



58

Key conclusions from data mining work examining online classroom data were as follows:

1. Four behaviors (i.e., open class, create response note, launch conference, read conferencenote) were found to differentiate students’ course performance

2. Students’ improved course performance was associated with higher engagement3. Students could be profiled based on their overall online classroom engagement and

performance4. A model using means of four online classroom behaviors and community college GPA

was able to explain 31.3% - 33.7% of variance in course performance, overall

93.1%

6.9%

0%

20%

40%

60%

80%

100%

YES NO

Retention (1st year)

for unique ID/class/term rows



59

SECTION 10: STUDENT MOTIVATION AND SELF-REGULATION

In addition to examining online classroom engagement, transfer students’ motivational and self -regulatory characteristics were examined as they were associated with and course performance.The goals of the study included:

1. Examine the motivation and self-regulation psychosocial profiles of communitycollege transfer students to UMUC.

2. Explore the relationship between psychosocial characteristics and students’ GPA at

UMUC.3. Determine the extent to which psychosocial factors differ by socio-demographic

profiles, specifically, family structure and employment status.

Motivation may be defined as the cognitive and affective components driving students’ behavior(Ames, 1992), whereas self-regulation is defined as the ―self -directed processes and self-beliefsthat enable learners to transform their mental abilities…into an academic performance skill‖

(Zimmerman, 2008, p. 166).

Population

The population consisted of 344 community college transfer students enrolled at UMUC inSpring 2014. Participants were on average 39 years old with 45 % female (45%). The samplewas racially and ethnically diverse: 51% White, 27% African American, 7% Hispanic, and 7%identifying as two or more races. The remaining participants’ did not report race/ethnicity.

Methodology

A survey was developed with three primary parts: (a) a motivation scale, (b) a self-regulationscale, and (c) family structure and employment status questionnaire. The motivation and self-regulation scales were based on the Motivation and Self-Regulated Learning questionnaire(MSLQ, Pintrich, 1991) and adapted for the online learning context (e.g., Barnard et al., 2009;Levy, 2007).

Motivation scale. The motivation scale included three subscales: (a) a six-item locus of control subscale (e.g., ―The good grades I receive are the direct result of my efforts,‖ Cronbach’s α =0.50); (b) a two-item intrinsic motivation subscale (e.g., ―I prefer course materials that really

challenge me so that I can learn new things,‖ Cronbach’s α = 0.62); and (c) a five-item self-

efficacy scale (e.g., ―I expect to do well in my classes,‖ Cronbach’s α = 0.84). Cronbach’s α is astatistical measure of reliability. Overall, the reliability for the 13-item motivation scale was0.80.

Self-Regulation scale. The self-regulation scale included six sub-scales, considered to be particularly pertinent to online learning (Barnard et al., 2009). Specifically, it included: (a) fiveitems on goal-setting (e.g., ―I set standards for my assignments in online courses,‖ Cronbach’s α = 0.83); (b) four items on environment structuring (e.g., ―I find a comfortable place to study,‖Cronbach’s α = 0.85); (c) four items on task strategies (e.g., ―I prepare my comments before



60

joining in conferences and discussions,‖ Cronbach’s α = 0.85); (d) three items on time

management (e.g., ―I allocate extra study time for my online courses because I know they are

time demanding,‖ Cronbach’s α = 0.65); (e) three items on help-seeking (e.g., ―I am persistent ingetting help from the instructor through email,‖ Cronbach’s α = 0.69); and (f) four items on self-evaluation (e.g., ―I communicate with my classmates to find out how I am doing in my classes,‖

Cronbach’s α = 0.79). Overall, the reliability of the 23-item self-regulation scale was 0.92.

Socio-demographic factors. In the third part of the survey, participants were asked to report avariety of factors associated with their family structure and employment status. Specifically, participants reported whether they were single or married/in a domestic partnership and whetherthey had children under 18 who lived with them. Further, participants reported whether theywere employed in Spring 2014, the average number of hours per week they worked, and thefinancial sources they used to finance their education.

The survey was sent to a random sample of undergraduate students (N=2,690), enrolled atUMUC during Spring 2014 semester, and who had previously transferred from a community

college. The survey had a 12.8% response rate. For those completing the survey, demographicand performance data (i.e., cumulative GPA) were identified based on student records. Nostatistically significant demographic differences were found between those students whocompleted the survey and those who did not.

Results

Motivational and self-regulatory profile-. Table 18 includes participants mean scores on eachmotivation and self-regulation sub-scale. Overall, students reported both moderately high levelsof motivation and self-regulation. Students had the highest scores on the self-efficacy sub-scaleon the motivation scale and the goal-setting subscale on the self-regulation scale.

Table 18. Motivation and Self-Regulation Scores by SubscaleScale Mean Standard Deviati on

Motivation 3.86 0.48

Locus of Control 3.50 0.55Intrinsic Motivation 4.04 0.71

Self-Efficacy 4.22 0.59

Self-Regulation 3.76 0.57

Goal Setting 4.30 0.62EnvironmentManagement

4.26 0.66

Task Strategies 3.43 0.76Time Management 3.86 0.78Help Seeking 3.30 0.89

Self-Evaluation 3.16 0.83

Self-regulation and motivation associated with GPA- A series of Pearson’s correlations were

conducted. Although GPA was not significantly associated with motivation and self-regulation



61

overall, it was correlated with a variety of self-regulatory subscales: goal setting, task strategyuse, help-seeking, and self-evaluation.

Table 19 displays students’ motivational and self -regulatory profiles by whether they earned ahigh GPA (i.e., 3.0 or above) or not. A GPA of 3.0 or above was selected as a cut off becausethe majority of students responding to the survey had a high GPA.

Table 19. Motivation and Self-Regulation Scores by GPAScale GPA 3.0 or ≥

(n=243) GPA < 3.0

(n=101)





Goal Setting 4.35* 4.18*Environ Mgmt 4.29 4.16

Task Strategies 3.36* 3.59*Time Management 3.86 3.86

Help Seeking 3.25* 3.42*Self-Evaluation 3.10 3.30

Motivation and self-regulation and socio-demographic profiles- Table 20 presents descriptiveinformation regarding students’ reported status in various socio-demographic categories.

Table 20. Descriptives for socio-demographic dataEmployment Status: (Are you currently employed?)

Yes, FullTime

Yes, PartTime

No, I amseeking

employment

No, and I am notseeking

employment

I have served in themilitary/been amilitary spouse

67.7%(n=233)

7.6%(n=26)

7.8%(n=27)

3.8%(n=13)

7.6%(n=26)

Avg. GPA 3.30 3.23 3.10 3.58 3.35

Payment Method: (Check all that apply)

I usedscholarships

My work provided tuition

assistance

I took outloans

I receivedfinancial aid

I paid forUMUCmyself

I used MilitaryBenefits or the

GI Bill

18.6%(n=64)

29.9%(n=103)

27.0%(n=93)

39.0%(n=134)

42.4%(n=146)

23.3%(n=80)

Avg. GPA 3.49 3.28 3.20 3.29 3.34 3.32

Parental Status: (Check all that apply) I have children under 18who live with me

I have children under 18who do not live with me

I have childrenover 18

I have nochildren

43.9%(n=151)

5.2%(n=18)

23.5%(n=81)

33.4%(n=115)

Avg. GPA 3.32 3.09 3.25 3.31

Independent sample t-tests determined that those students working full-time or in the militaryreported significantly lower levels of goal setting; time management; and environmental



62

management than did those working part- time or not working. Further, those students notworking full-time had significantly higher self-regulation scores overall than did workingstudents. Table 21 presents students’ mean motivation and self -regulation by employment status.

Table 21. Motivation and Self-Regulation Scores by Employment StatusScale Working Full Time

(n=259) Other

(n=66)




Self-Regulation 3.72* 3.90*

Goal Setting 4.27* 4.45*Environment Management 4.22* 4.40*

Task Strategies 3.39 3.58Time Management 3.82* 4.04*

Help Seeking 3.26 3.45

Self-Evaluation 3.14 3.26GPA 3.31 3.25

Note: Employment status determined based on students’ responses to the question, ―Are you currently employed?‖ (Responses: Working full time & Military =1; Working part time, Not Employed & Seeking employment=0;)

Likewise, learners’ differed in self-regulatory profiles according to the way they paid for theireducation. Those students reporting that they paid for UMUC themselves reported significantlylower levels of time management; and self-evaluation, while having significantly higher GPAs.Table 22 presents mean motivation and self-regulation levels for students by payment method.

Table 22. Motivation and Self-Regulation Scores by Payment MethodScale Paid for UMUC Myself

(n=146)

Other

(n=198)Motivation 3.88 3.84




Goal Setting 4.33 4.28Environment Management 4.29 4.23

Task Strategies 3.36 3.48Time Management 3.76* 3.94*

Help Seeking 3.31 3.29Self-Evaluation 3.05* 3.25*

GPA 3.34* 3.19* Note: Payment method determined based on a yes/no coding of students’ endorsement to the item, ―I paid for

UMUC myself‖ (No significant differences were found in students’ endorsements of, ― I used scholarships to pay for

UMUC ‖; ― My work provided tuition assistance to help pay for UMUC ‖; ― I took out loans to pay for UMUC ‖; ― I

received financial aid to pay for UMUC ‖ and ― I used military benefits or the GI Bill to pay for UMUC ‖)

Those reporting having children had significantly higher intrinsic motivation than did studentsreporting having no children. (See Table 23.)



63

Table 23.Motivation and Self-Regulation Scores by Parental StatusScale Parents

(n=229) Non-Parents

(n=115)


Locus of Control 3.50 3.52Intrinsic Motivation 4.10* 3.92*



Goal Setting 4.32 4.26Environment Management 4.27 4.24

Task Strategies 3.49 3.32Time Management 3.91 3.76

Help Seeking 3.28 3.32Self-Evaluation 3.19 3.10

GPA 3.23 3.31 Note: Parental status determined based on a yes/no coding of students’ endorsement to the item, ―I have no

children‖ (No significant differences were found in students’ endorsements of, ― I have children under 18 who live

with me‖; ― I have children under 18 who do not live with me‖; ― I have children over 18‖)

No significant differences in motivation and self-regulation were found between studentsmarried and not, although married students did have a significantly higher GPA.

Key Findings

Students’ GPA at UMUC was positively associated with goal setting.

There was a negative correlation between students’ GPA and task -strategy use, help-seeking, and self-evaluation. Students with lower academic abilities (i.e., lower GPAs)may be more reliant on these self-regulatory approaches as a compensatory factor.

Psychosocial characteristics (i.e., motivation and self-regulation) differed based on

students’ socio-demographic profiles (i.e., employment status and payment method).o Those students working full-time, as compared to not, reported significantly lower

levels of goal-setting, environmental management, and time management as wellas lower overall self-regulation.

o Those students paying for UMUC by themselves, at least partially, hadsignificantly higher GPAs while still reporting lower levels of time managementand self-evaluation.

o Those students reporting having children had significantly higher levels ofintrinsic motivation.



64

SECTION 11: INTERVENTION IMPLEMENTATION AND EVALUATION

Based on Kresge research, insights from the literature, and through discussion with communitycollege partners, a number of interventions were conceived, implemented, and evaluated.Collectively these interventions aimed to offer students’ academic and social support through a

variety of mediums and targeted the unique issues faced by online, non-traditional learners.Each intervention undertaken is briefly described below.

Student Resource Checklist- First-term community-college transfer students were randomlyassigned to a control (n=100) or test group (n=240). Students in the test group were sent aStudent Resource Checklist by their advisors. The goal of the checklist was to orient students tothe academic and social support resources available from the university, both online and face-to-face. To complete the checklist, students had to use the university online resources to findinformation about advisors, discipline-specific academic tutoring, writing assistance, and libraryresources.

College Success Mentoring Program- First-term community college students, transferring from

MC and PGCC, were randomly assigned to a control (n=33) and test group (n=90). Students inthe test group were each paired with a peer mentor, who had transferred from their samecommunity college and had been successful at the UMUC. Mentors sent weekly emails tomentees with advice and study tips and supported new students throughout their first semester.

JumpStart Summer- Jumpstart was a course offered free to all new students, intended to serveas an orientation to online learning and to specifically address the needs of adult, career-orientedstudents. As a part of the course, students completed academic diagnostic measures, developedschool- and career-related goals, a course plan, and were taught to use a variety of online tools,including a course planner and resume-builder. In addition to examining the efficacy of theJumpstart course, we were interested in examining the effects of Jumpstart compared to and in

combination with mentoring. Students were randomly assigned to one of four conditions:

1. Control (n=44): Students received no intervention2. Jumpstart (n=74): Students were assigned to complete the four-week Jumpstart course3. Mentoring (n=74): Students were assigned a mentor for 8-weeks, parallel to the

College Success Mentoring Program4. Jumpstart Summer (n=74): These were students were assigned to complete the four-

week JumpStart course as well as assigned a mentor for 8-weeks.

Accounting 220 and Accounting 221- An online tutoring intervention implemented in twointroductory accounting courses at UMUC, ACCT 220 and ACCT 221, during the Fall 2013

term. To support students’ success in these challenging courses, an online live tutoring programwas developed and offered by course instructors. Tutoring was offered during a three-month period and students could choose to attend any number of sessions during that time.

A description and results from each intervention are presented, in turn. Across interventions,three key outcome measures were considered.



65

Checklist

Participants

Participants in the Checklist intervention were identified through a data pull of students enrolled

at UMUC during the Spring semester of 2014; the data pull occurred in January 10

th

of 2014.All Maryland community college transfer students, excluding those from MC and PGCC whowere participating in another intervention, enrolled in their first semester at UMUC wereisolated. From these, 241 were randomly selected to receive the Checklist and 103 wererandomly selected to serve as control participants.

Results

No significant differences in GPA and successful course completion were found betweenstudents receiving the checklist and the control group. Table 24 compares the performance ofthe test group (i.e., those receiving the checklist) and the control group.

Table 24. Average GPA and Successful Course Completion for Checklist Completers

TestReceived the

Checklist(n=240)

Completed theChecklist

(n=59)

ControlDid Not Receive the

Checklist(n=103)

Term GPA 2.87 3.00 2.91Successful Course Completion 73% 77% 77%Re-Enrollment 67% 72% 67%

No significant differences in GPA and successful course completion were found between

students who completed the checklist and the control group.

Further, an evaluative survey was sent to all students receiving the checklist, both completing itand not.

Of those who did complete the checklist, 42.37% responded (n=25), whereas only 4 students(2.21%) who received the checklist but did not complete it, responded to the evaluation survey.The overall response rate was 12.08%.

Of students responding, 85% of students reported that they would recommend completing thechecklist to other students.

Anecdotally, the goals of the checklist in familiarizing new UMUC transfer students with

resources and social support at UMUC proved to be successful. As one student explained, ―ithelped me compile information and learn how to use UMUC’s website.‖ This type of navigation

may be particularly important in helping to familiarize students with online resources at UMUC.Another respondent reported, ―I had all my instructors emails listed on one sheet.‖ Indeed, a

goal of the checklist was to better connect students with both their instructors and advisors.



66

Finally, one student reported, ―it helped me get back into school after being out for 6 years,‖

getting at the ultimate goal of the checklist – to ease students’ transition to a four -year onlineuniversity.

College Success Mentoring

Participants

Participants in the study included mentor, mentee and control participants, all of whom hadtransitioned to UMUC from MC or PGCC.

A total of 80 mentors and 761 control participants were included in the study. Selection andrecruitment of mentors is described in the Procedures section. In all but one case, mentorsretained for analysis were identified in the mentor-mentee matching phase of the program (n =79). One additional mentor was added during remediation. The control participants were thoseindividuals who were recruited for the mentoring program, but were not selected as mentors.

A total of 90 mentees and 24 control participants were included in the study. Mentees were thosewho received the mentoring treatment, while control participants were those that didnot. Selection and recruitment of mentees and control participants is described in the Proceduressection.

Results

No significant differences in GPA and successful course completion were found betweenstudents in receiving mentoring and the control group. Table 25 compares the performance ofmentees in the test group and the control group, not receiving mentoring.

Table 25. Average GPA and Successful Course Completion for Mentoring Groups

Mentees

Test

(n=90)

Control

(n=34)

GPA 2.70 2.66Successful Course Completion 78% 69%

Re-Enrollment 74% 75%

Table 26 compares the performance of mentors to a comparison group of students, eligible toserve as mentors and invited to do so, who nonetheless elected not to participate. Althoughstudents eligible to serve as mentors were overall successful, those who indeed served as mentorshad significantly higher cumulative GPA and rates of successful course completion. Further,while term GPA, corresponding to the semester in which students served as mentors, did notsignificantly differ across the test and control groups, mentors did have a 0.20 point higher GPA.



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Table 26. Average GPA and Successful Course Completion for Mentor Test and Control Group

Mentors

Test

Served as mentors(n=70)

Control

Invited but did not serve asmentors(n=117)

GPA 3.56 3.34Successful Course Completion 95% 89%

Mentees- The mentoring evaluation survey was sent to both mentor and mentees. Amongmentees, 20% (n=18) responded to the survey. Of those responding, 82% reported that theywould recommend the mentoring program to other students.

Mentees reported receiving both academic and social support from their mentors. For example,one mentee reported, ―They had previous experience with the format of UMUC classes; gave

insight to what [the classes] would be like.‖ This suggests that mentors offered support adjusting

to idiosyncratic aspects of UMUC’s courses, including content delivered online and an 8-week

compressed schedule. Mentees also connected with their mentors, ―She is very caring and verydown to earth. She made it very easy to communicate with her.‖ Indeed, part of the goal of thementoring program was to connect students with peer support at the transfer institution.

From an institutional perspective, the mentoring program supported students adjusting to UMUCculture. As one student explained, ―He helped me the most in getting accustomed to the 8 week

sessions and how to set up my schedule throughout the week to be successful.‖ It is hoped thatspecific skill building, like teaching mentees to set up a schedule, will support students’

performance in subsequent semesters at UMUC.

Finally, mentees discussed the importance of having role models of success who shared their

community college background. One student reported, ―Having someone that went through thesame process helped me get one step closer to my goal.‖ In this way, mentoring may have

promoted student success not only in the first semester but beyond.

Mentors- For the mentors, 48% (n=43) responded to the evaluation survey. Among themesexplored, was the benefit mentors gained from serving as role models and leaders. One studentexplained, ―It put me in a responsible position. Not only did I have to help [him] succeed, I have

to [prove] to him that what I’m teaching him is working by passing myself.‖ This suggests anintersection between the ways in which mentors and mentees viewed the program, as providingstudents with role models of success. Another student further expanded, ―What I found to be

most valuable is my ability to learn more about myself as a leader and being able to improve my

communication skills.‖ As such, the mentoring program may have provided benefits to bothmentees and mentors in terms of skill development.

Serving as a mentor also served to increase mentors’ connections to UMUC. A mentor reported,―Having the opportunity to give back to UMUC and have others learn from my experience,‖ as a

key benefit of the mentoring program. This type of institutional commitment may be difficult tofoster at online universities. Another student discussed the motivational benefits of helping their



68

peers, ―I like the idea of helping others. College is not always easy and the idea and act of

helping others is highly motivating.‖

Jumpstart Summer

Participants

All students transferring from community college to UMUC in Summer 2014 were targeted.Students were randomly assigned to one of four groups:

a. Control (n=44) b. Jumpstart (n=75)c. Mentoring (n=75)d. Jumpstart Summer (n=74)

The control group received no interventions. The Jumpstart group was assigned to take the four-week Jumpstart onboarding course. The Mentoring group received 8-weeks of mentoringthrough the College Success program. The Jumpstart Summer group both participated in thefour-week onboarding course and received eight weeks of mentoring.

Those students who did not want to take the Jumpstart course were allowed to opt-out of participation; however, these students are still included in group comparison.

Results

Students’ average GPA and percentage of courses successfully completed will first be presentedacross each of the four conditions. (See Table 27.)

Table 27. Comparing four conditions on GPA and successful course completion

Control(n=44)

MentoringProgram

(n=75)

Jumpstart(n=75)

Jumpstart +Mentoring

(n=74)

GPA 2.46 2.16 2.13 2.52

Successful Course Completion 75% 74% 64% 73%

A number of students elected to withdraw from the Jumpstart course. In the Jumpstart condition,27 students withdrew (36.0%); in the Jumpstart Summer condition 22 students withdrew(29.7%). Analyses were run excluding those students withdrawing from the Jumpstart course.(See Table 28.)

Table 28. Comparing four conditions, excluding those students who dropped Jumpstart Control(n=44)

Mentoring

Program(n=75)

Jumpstart(n=48)

Jumpstart +

Mentoring(n=52)

GPA 2.46 2.16 2.23 2.40

Successful Course Completion 75% 74% 59% 67%



69

Accounting 220 and Accounting 221

Participants

In two sessions of Fall 2013, 1,191 students enrolled in Accounting 220 or Accounting 221.

These students were divided into two groups:

Test Group: Students who participated in at least one live tutoring session.Control Group: Students who did not attend any live tutoring sessions.

Sixty-seven students were placed into the test group because they attended the online tutoringsessions and were registered for either ACCT 220 or ACCT 221. Sixteen students participated intutoring but were not matched with the course records for the two courses and were removedfrom the analysis. The remainder of the students was placed in the control group. Demographicsof each group were examined. In addition, a standard T-test was conducted to determine if the performance between the test and control groups were significantly different.

Results

Successful course completion, term GPA, change in GPA, and re-enrollment in the subsequentterm were compared as outcomes for the test and control groups. Change in GPA refers to thedifference between students’ GPA in the semester prior as compared to the GPA at the end of thecurrent semester. Table 29 provides results for both the test and control groups.

Table 29. Test and Control group performance on target outcome variables

Accounting 220 & Accounting 221

Test Control

Successful Course Completion 72%* 58%*Term GPA 2.52* 2.10*

Reenrollment 78% 72%

Change in GPA 0.31 0.07* Indicates statistically significant differences between the test and control groups.

Key Findings

Students participating in tutoring (test group) had a significantly higher rate of successfulcourse completion when compared to those who did not participate (control group).

Students in the test group had a significantly higher term GPA than students in thecontrol group.

The re-enrollment rate of the test group was six percentage points higher than the control

group, but this was not statistically significant.

The change in cumulative GPA was .24 points higher for the test group than the controlgroup. While the difference in the change in GPA was not statistically significant, the testgroup did demonstrate a greater increase in GPA than the control group.



70

SECTION 12: DISSEMINATION

A number of pathways have been taken to share results of the research and interventionsconducted for the PASS project. In particular, four types of initiatives were undertaken: (a) presentations at conferences, (b) publications, (c) the Learner Analytics Summit, and (d)

development of the Student Success Calculator.

Presentations at Conferences

An ambitious conference schedule was adopted to disseminate findings of the research grant aswell as results based on the Kresge Grant overall. A summary of the presentations is included inTable 30.

Publications

A number of publications are in-process or planned. The abstract of two of the manuscripts are presented below.

1. Bridging the Great Divide: Examining Predictors of First-Term GPA for

Community College Students Transferring to a Four-Year Online University

While a variety of individual factors (e.g., age, gender) have been considered in predicting first-term university GPA of community college transfer students, little has been done to considerhow students’ community college backgrounds may impact post-transfer success. In part,community college factors, beyond GPA, have been neglected in the research literature due tolimitations in available data tracking students’ progress from community college to university.In the present study, students’ demographic characteristics and community college course taking

behaviors (e.g., enrollment in math courses) are examined as predictive of first-term university

GPA. Further, a new variable, course efficiency, or the ratio of credits earned to creditsattempted, is introduced as a summative index of community college course taking and as predictive of first-term university GPA.

2. Predictors of Retention for Community College Students Transferring to a Four-

Year Online University

This paper takes a longitudinal approach to modeling students’ continued educational enrollmentfrom community college to a four-year university. While much work has examined models predicting transfer students’ retention at a four-year university, limited work has considered howfactors in students’ community college backgrounds may impact their retention upon transfer.The present study seeks to inform these gaps by using demographic factors and community

college course taking behaviors (e.g., enrollment in math courses) to predict retention at a four-year university. Further, course efficiency, or the ratio of credits earned to credits attempted, isincluded as a summative index of students’ community college course taking behaviors and as predictive of students’ retention at a four -year university.



71

Learner Analytics Summit

UMUC hosted a two-day convening to bring together leaders and practitioners of data analyticsto discuss issues facing both two-year and four-year institutions.

Session topics featured as part of the Learner Analytics Summit include:

A Review of the Data Analytics Toolkit

The Rise of Learner Success Scientists

Approaches to Predicting College Student Success

Using Analytics to Support Organization Change

Developing Institutional Capacity to Support Learner Analytics

As part of the Learner Analytic Summit, findings from the collaborations undertaken as a part ofthe Kresge grant were presented. An abstract of the presentation is below:

This presentation will feature a panel discussion from administrators and researchers at UMUCand partnering community colleges, MC and PGCC. The presentation will focus on three aspectsof the Kresge partnership: data sharing, research, and intervention development. Specifically,the development of the memorandum of understanding and the Kresge Data Mart will bediscussed as will key research findings regarding the associations between students’ communitycollege course taking behaviors and performance at a four-year institution. Finally, findingsfrom interventions undertaken at UMUC and at the two partnering community colleges,undertaken to promote transfer student success will be introduced. The presentation willconclude with a description of the value added for each institution with time for questions and panel discussion.

Table 30. Summary of Conference Presentations DeliveredConference Description2013

AACRAO Multi-Institutional Data Predicting Transfer Student SuccessA multi-institutional data base was developed to track the progress and success ofstudents who transferred from a community college to a 4-year institution. The studyidentified risk factors through data mining.

Association for

Institutional

Research

Integrating Multi-Institutional Data for Predicting Student SuccessIntegrating multi-institutional data using detailed variable examination, data mining,and statistical modeling predict student success and develop actionable interventions.

WCET Using Learner Analytics Across InstitutionsOverview of Kresge grant, partnerships, integrated data, factors that predict coursesuccess, first-term GPA and retention, success quadrants, likelihood of communitycollege subject choices, possible interventions.

AACRAO’s

Technology &

Transfer Conference

Mining for Success: A community college and four-year joint project on student

successReview the goals of the study, results to date and plans for the future. Research so farhas included survival analysis, predictive models and clustering algorithms. The



72

results of this research will help this collaborative team to identify student successinitiatives that will be piloted and evaluated in Fall 2013.

2014

AACRAO’s

Technology &

Transfer Conference

Interventions to Promote Community College Transfer Student Success at a

Four-Year, Online UniversityPresentation on the effectiveness of two interventions aimed at promoting communitycollege students’ success when transferring to a four -year primarily online university(i.e., Checklist, Mentoring).

SLOAN-CBlended LearningConference

Blended Interventions to Aid Transfer Students’ Transitioning from Face-To-

Face to Online CoursesPresentation on the effectiveness of four interventions, delivered through variousmediums, in helping community college students transition to a four-year, onlineuniversity (i.e., Checklist, Mentoring, Jumpstart, CUSP)

Learning AnalyticsSummit

Cross Institutional Collaborations: Building Partnerships for Student Success This panel presentation with MC and PGCC will have three purposes: 1) present keygoals for the Kresge partnership; 2) share research and intervention outcomes initiatedthrough the grant; 3) consider lessons learned and future directions for work on promoting community college students’ success and persistence.

UPCEA Mid-Atlantic

Project Jumpstart: A Systemic Approach to Onboarding Adult StudentsThis presentation offers insights into the development and evaluation of ProjectJumpstart, an academic readiness course offered to new students at UMUC. Threesemesters of program implementation and improvements based on feedback fromadministrators, teachers, and students are presented.

SLOAN-CInternationalConference onOnline Learning

Jumpstart to Success: Creating a Personal Learning Plan to Improve Retention

and Success for Adult Students

Development, implementation, and evaluation of JumpStart Mentoring program aimedat improving on-boarding and promoting academic planning for community collegetransfer students.

Examining the Relations Between Online Learning Classroom Behavior and

Student Success We present descriptive and trend analysis of community college transfer students’online classroom behaviors. The relation between patterns in online classroom behaviors and course success and persistence will be examined.

NortheastAssociation forInstitutionalResearch

Community College Transfer Student Success at an Online University:

Conclusions from a Kresge Foundation Project

This presentation introduces an overview of Kresge grant key goals, research andintervention initiatives, and future directions in promoting community college transferstudent success at a four year university.

Decision SciencesInstitute

Online Live Tutoring Enhances Student SuccessThis research presents results from an evaluation of an online live tutoringintervention implemented in two introductory accounting courses at a 4-year onlineuniversity.

http://sloanconsortium.org/conference/2014/blended/welcome






73

Success Calculator

The Success Calculator was developed as an advising tool, based on predictive modeling ofstudent success. Two calculators were developed: 1) success in the first semester at UMUC, and2) graduation from UMUC. The First Term Success Calculator uses students’ demographic data

and course taking behaviors to predict the probability of earning a GPA of 2.0 or above in theirfirst semester at UMUC. This calculator is intended to be used as an advising tool to supportstudents’ successful transition to UMUC.

The Graduation Calculator used models predicting the 8-year completion of community collegetransfer student data. Predictors included demographic factors, community college factors, and performance at UMUC in the first semester. An image of the Graduation Calculator is presentedin Figure 10.

Figure 10. Image of success calculator

The calculator was developed in an Excel application. The community college partnersexpressed an interest in piloting the calculator. An initial pilot was conducted with an advisor atPrince George’s Community College. In order to better disseminate the calculator, a password protected website has been developed to present the calculator for the community colleges touse.

UMUC intends to adapt the calculator for each community college. In addition, the process forthe development of the calculator will be shared with 4-year and community colleges that areinterested in creating a similar collaboration.

Gender Female

Age At Transfer 25

Race/Ethnicity Asian

PELL Grant Recipient No

Math at CC No

Percentage of Courses Withdrawn From 30%

Received an Associated Degree No

CC Cum GPA 3.5

CC Cum Credits Earned 60

First Term GPA at UMUC 2.5

UMUC First Term Credits Earned 12

Probability of Graduating in Eight-Year Period 55%

UMUC Success Calculator

Student Information

Predicting Graduation

Calculate



74

SECTION 13: FINANCIAL SUPPORT

The Kresge Foundation awarded UMUC a $1.2 million grant to explore ways to improve studentsuccess for transfer students by partnering with community colleges to track student progress.

The grant provided funding to build an integrated database, explore data mining techniques, build predictive models of student success, implement and evaluate intervention strategies thatare designed to improve student success, and disseminate the results of this research to nationalconstituents.

In Phase 1 of the research study, approximately 41% of total grant funds were expended on purchasing hardware and software for the development of the database, collecting data from thecommunity colleges, and hiring a data mining specialist and a graduate assistant. Additional staffresources were provided in kind by UMUC. In Phase 2, funds were expended for additional datacollection, data mining consulting, and conference presentations. In the final stages of the grant,expenses spent on collecting additional data from the community colleges, data mining

consultation, implementing interventions, and hiring an intervention coordinator. All taskswithin the grants were completed as planned. Any additional funds were used to support anational convening on learner analytics.



75

SECTION 14: CONCLUSIONS

Work completed as part of Kresge Data Mining grant satisfied and exceeded the goals outlinedfor the grant. Specifically, there were three stated goals for the grant:

1.

To build an integrated database tracking students across institutions, fromcommunity college to UMUC.

2. To use predictive statistical models and data mining techniques to track and modelstudents’ progress across institutions.

3. To identify factors predictive of students’ success at UMUC that may inform thedevelopment of interventions aimed to improve outcomes for undergraduate studentstransferring from community colleges to UMUC or other four-year institutions.

To build an integrated database tracking students across institutions, from community

college to UMUC

Two iterations of the Kresge Data Mart (KDM) have been developed including data from thecommunity college partners as well as from UMUC’s student information system, customer -relationship management (CRM) advising system, and online classroom learning managementsystem (LMS). Two base extractions of data from the community colleges have been completedand matched to UMUC students’ records.

To use predictive statistical models and data mining techniques to track and model

students’ progress across institutions

Predictive modeling was used to build models associated with key milestones in students’academic trajectories including (a) earning a successful first-term GPA, (b) re-enrollment, (c)retention, and (d) graduation. Across models demographic factors (gender, marital status), mathtaking at the community college, CC GPA were all predictors of first-term GPA, re-enrollment,retention, and graduation. First-term GPA at UMUC was also a significant predictor of re-enrollment, retention, and graduation.

Data mining methods were used to identify patterns in students’ online classroom behaviors inthe LMS. Students’ were profiled based on course performance and level of engagement in the

LMS. Additionally, a predictive model using community college GPA and four onlineclassroom behaviors (i.e., opening a classroom, launching a conference, reading a conferencenote, creating a response note) were found to predict successful course completion at the studentlevel. Data mining proved to be a fruitful technique for exploring the complexity of the variablesincluded in the KDM.



76

To identify factors predictive of students’ success at UMUC that may inform the

development of interventions aimed to improve outcomes for undergraduate students

transferring from community colleges to UMUC or other four-year institutions

Based on research, literature reviews, and collaborative partnerships, six interventions were

developed, implemented, and evaluated at UMUC and at the community colleges. Collectively,the interventions targeted student academic achievement and social and institutional integration.A number of interventions targeted community college students in the first-semester of transferto aid with the transition as well as acclimation to a four-year and online climate.

In addition, this research provided the opportunity to develop the Success Calculator, anapplication tool predicting students’ probability of earning a successful first-term GPA atUMUC. This tool represents a real-world extension of the research.

Sharing and disseminating research findings are being disseminated through conference presentations and publications.

Future Directions

Future directions include expanding the research study to include other community colleges.This would allow for the validation of models developed based on a larger and more diversesample and for the examination into how various predictors function across institutions. Inaddition, the MC and PGCC are committed to continuing the data collection and analysis to getfeedback on how their students are performing at UMUC. This information has informed andwill continue to inform practices and policies at both the community colleges and at UMUC.

Future research directions include the following:

Evaluate long term effects of students participating in the interventions to determine ifthe interventions influence retention or completion.

Evaluate the math performance of transfer students by facilitating meetings betweencurriculum designers, program directors, and instructors to better align curriculum acrossinstitutions and to ensure students’ academic preparedness.

Evaluate the accounting performance of transfer students by determine the extent towhich accounting courses across institutions are aligned and students are academically prepared.

Evaluate the developmental math curriculum and transfer performance. Both MC andPGCC are now offering modularized developmental math courses. UMUC will evaluatethe performance of these students and compare their performance with students who didnot receive the modularized math courses.

Share the UMUC Success Calculator with other institutions interested in developingsimilar data sharing partnerships.



77

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