Educational Data Mining in Program Evaluation: Lessons Learned

© 2016 Boise State University 1

Kerry Rice, Jui-Long Hung, Yu-Chang Hsu, Brett E. SheltonDepartment of Educational Technology

Boise State University

Educational Data Mining in Program Evaluation: Lessons Learned

AECT 2016, Las Vegas


METEd. D. Ed. S. Graduate Certificates:

Online Teaching Technology Integration SpecialistSchool Technology Coordinator

K-12 Online Teaching EndorsementCollege of Education


Go Broncos!


Decision Tree Analysis (performance prediction)

Cluster Analysis (engagement)

Sequential Association Analysis (path analysis)

Educational Data Mining Applications

Time Series Analysis (future performance prediction)


Study #1: Teacher Training Workshops 2010

• Survey Data + Data Mining + Student Outcomes

• Research Goal: – Program

improvement– Satisfaction – Impact on practice

• Blackboard• 103 participants• 31,417 learning logs• Cluster Analysis;

Sequential Association Analysis; Decision Tree Analysis;


Study #2: Online Graduate Teacher Education 2010• Data Mining + Student

Outcomes (no demographic data)

• Research Goal: – Identify struggling students– Adjust teaching strategies– Improve course design– Data Visualization

• Study Design– Comparative (between and

within courses)– Random course selection

• Moodle• Two graduate courses

(X and Y)• Each with two sections

– X1 (18 students)– X2 (19 students)– Y1 (18 students)– Y2 (22 students)

• 2,744,433 server logs• Cluster Analysis; Sequential

Association Analysis; Decision Tree Analysis; Data Visualization


• Data mining + Demographics + Survey Data + Student Outcomes

• Research Goal: Large scale program evaluation– Support decision making at the

course and institutional level – Identify key variables and

relationships between teacher and course satisfaction, student behaviors, and performance outcomes

Study #3: End of Year K-12 Online Program Evaluation 2012

• Blackboard LMS• 7500 students • 883 courses• 23,854,527 learning logs

(over 1 billion records)• Cluster Analysis; Decision

Tree Analysis


Study #4: End of Year K-12 Blended Program Evaluation 2012

• Blackboard LMS• 255 Enrollments • 33 course sections• 17 unique courses• Satisfaction Survey• Data from 2011 pilot

study• Cluster Analysis;

Decision Tree Analysis

• Demographics + Survey Data + Data Mining + Student Outcomes

• Research Goal: Test Framework in Blended Learning– Support decision making at the

course and institutional level? – Identify key variables and

relationships between teacher and course satisfaction, student behaviors, and performance outcomes


Study #5: Online Graduate Teacher Education, 2014

• Moodle LMS• 509 Enrollments • 25 course sections• 12 unique courses• 431,708 records• Time Series Analysis• 34 original and derived

variables–static (demographic)–dynamic (engagement)

• Demographics + Data Mining + Student Outcomes

• Research Goal: – Could we identify at-risk

students in real time?– When?


Study #6: Online Graduate Teacher Education, 2015

• Moodle LMS• 661 Enrollments • 31 course sections• 18 unique courses• 546,965 records• Time Series Analysis• 34 original and derived

variables–static (demographic)–dynamic (engagement)

• Demographics + Data Mining + Student Outcomes

• Research Goal: – Did the model developed in

Study #5 work with new semester data?

– Were the predictive (timing) results the same?

– Were frequency data more predictive?


Variables - actual and derived

Engagement


Cluster Analysis

Student clustering which describes shared characteristics of students who passed or failed their courses


Cluster Analysis: Relative Participation Levels and Final Grades

• Average Time Spent• Average Days Participated• Average Frequency of Mouse Clicks • Average Time Spent per Session• Average Frequency of Mouse Clicks per Session

Participation Variables (Engagement)


Cluster Analysis: Student Characteristics

Cluster 1 – Low-High, 119 students: Low average participation and higher performance levels.

Cluster 2 – High-High, 60 students: High average participation and high performance levels.

Cluster 3 – Low-Low, 76 students (46% remedial): Low average participation and low performance levels.


Cluster Distributions in CoursesHigh percentage of Low-Low students in Chemistry CP, CP Pre-Calculus, English II, English III, and Pre-Calculus (H)


Decision Tree Analysis

Perception and performance predictions which identify key predictors of course satisfaction, instruction satisfaction, and final grade


Decision Tree Analysis: Predictors of Student Performance

Decision Tree Analysis• Identified at risk• Number of Courses Taken• Average Clicks per Week• Average Time Spent per

Week, and • Average Time Spent per

Session

Other factors• Gender • Ethnicity• Reason for taking a course


Sequential Association Analysis

Course X Course Y

Does the design of the course (path to learning) predict learner outcomes?


Sequential Association Analysis


Time Series Analysis


Time Series Analysis – course access

A

B

F

Week 10 Spring Break


Time Series Analysis – DB Replies

A

B

F

Week 10 Spring Break


Overall Analysis• Students who took fewer courses performed significantly better than

those who took more courses. • Engagement is a significant factor. High-engaged students performed

better than low-engaged students. • Students identified as at risk performed differently than all other students. • Type of engagement matters. Students who accessed their courses more

often performed better than those who had more interactions within the course. Consistent interaction over time is a better predictor of performance. (higher ed only)

• Advanced courses - High-engagement and high performance (K-12)• Entry level courses - Low performance regardless of engagement (K-12)• Gender and ethnicity (higher ed) were identified as significant factors• Satisfaction did not always equate to higher performance


Characteristics of successful students• Female (k-12)• Younger (k-12)• Were enrolled in advanced courses (k-12)• Took fewer courses• Were more engaged overall • Were consistently engaged


Characteristics of at-risk students• Male (K-12)• Older (K-12)• Took entry-level courses (K-12)• Took a greater number of courses • Were low engaged overall• Were inconsistent in their engagement


Data Collection Challenges• Bb activity accumulator grouped wide ranging behaviors into

only five useful categories• Missing data (empty fields – ex. internal handler)• Mismatched data fields/data stored in the wrong fields• Inconsistent data collection (i.e. failure to track every forum

reply)• Partial or missing timestamp (needed for sequential analysis)• Course or student ID not linked to survey • Demographic data not linked to course or program• Inconsistent course models (blended)


Educational Data MiningSpecial Challenges• Learning behaviors are complex• Target variables (learning

outcomes/performance) require wide range of assessments and indicators

• Goal of improving teaching and learning is hard to quantify

• Limited number of DM techniques suitable to meet educational goals

• Only interactions that occur in the LMS can be tracked through data mining

• Still a very intensive process to identify rules and patterns


References•Hung, J. L., Rice, K., & Saba, A. (2012). An educational data mining model for online teaching and learning. Journal of Educational Technology Development and Exchange, 5(2), 77-94.•Hung, J. L., Hsu, Y.-C., & Rice, K. (2012). Integrating data mining in program evaluation of K-12 online education. Educational Technology & Society, 15(3), 27-41.•Rice, K., & Hung. J. (2015). Data mining in online professional development program: An exploratory case study. International Journal of Technology in Teaching and Learning, 11(1), 1-20. •Shelton, B., Hung, J. L., & Baughman, S. (2015). Online graduate teacher education: Establishing and EKG for student success intervention. Technology, Knowledge and Learning. •Rice, K., & Hung, J. L. (2015). Identifying variables important to the success of K-12 students in blended learning. Paper presented at the Northern Rocky Mountain Educational Research Association Conference, Boise, Idaho. •Shelton, B. E., Hung, J. L.., & Lowenthal, P. (under review). Predicting student success by modeling student interaction in online courses.

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