Journal Education of Data Mining.pdf

8/13/2019 Journal Education of Data Mining.pdf

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A survey of

ABSTRACT

Educational data miningmining tools and techniques to eeducational data to develop modinstitutional effectiveness. A litetopics such as student retention aand how data mining can be useliterature and opportunities for f

Keywords: educational data min

effectiveness

Research in Higher

Educational data-mining

educational data-mining researc

Richard A. HuebnerNorwich University

(EDM) is an emerging discipline that focuses oucationally related data. The discipline focusesls for improving learning experiences and imp

rature review on educational data mining follo nd attrition, personal recommender systems wit

to analyze course management system data. Grther research are presented.

ng, academic analytics, learning analytics, insti

ducation Journal

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applying dataon analyzingovings, which covershin education,ps in the current

tutional


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INTRODUCTION

There is pressure in highinstitutional effectiveness (C. Roaccountable for student success (

finding new ways to apply analyEven though data mining (DM)application of DM to educationafound that they can apply data mmanagement systems such as Aneducational data mining (EDM)solve educationally related probl

The recent literature relatdata mining is an emerging discito educationally related data (Baranging from using data mining

improving student learning procmining, so this paper will focussuccess and processes directly reretention, personalized recommemanagement systems (CMS) are

Researchers interested inData Mining(2009) and a yearlyliterature draws from several refvisualization, machine learning aworks are published in the ConfeInternational Journal of Artifici

is a large part of data mining, whartificial intelligence related pub The purpose of this papeSpecific applications of educatioand attrition, personal recommemanagement systems. The paperrecommendations for further res

BACKGROUND OF DATA

Big data is a term that deorganization and the potential tobig data spans three different di2012). Organizations have a chalsolutions to do so. Data mining corder to guide decision-making (mining is a series of tools and teamong data (Dunham, 2003). Daprocess, where organizations wa

Research in Higher


r educational institutions to provide up-to-datemero & Ventura, 2010). Institutions are also inCampbell & Oblinger, 2007). One response to t

ical and data mining methods to educationallyas been applied in numerous industries and secl contexts is limited (Ranjan & Malik, 2007). Rining to rich educational data sets that come frogel, Blackboard, WebCT, and Moodle. The emxamines the unique ways of applying data min

ems.ed to educational data mining (EDM) is present

pline that focuses on applying data mining toolsker & Yacef, 2009). Researchers within EDM fo improve institutional effectiveness to applyin

sses. There is a wide range of topics within eduxclusively on ways that data mining is used tolated to student learning. For example, studentnder systems, and evaluation of student learninall topics within the broad field of educationaleducational data mining established theJournainternational conference that began in 2008. Trence disciplines including data mining, learnind psychometrics (Baker & Yacef, 2009). Somrence on Artificial Intelligence in Education, al Intelligence in Education. Interestingly, artifiich is why we see early educational data mininlications.is to provide a survey of educational data mini

nal data mining are delineated, which include stder systems, and other data mining studies withconcludes with identifying gaps in the current larch.

INING

scribes the growth of the amount of data that isdiscover new insights when analyzing the data.ensions, which include volume, velocity, and vlenge of sifting through all of that information,an assist organizations with uncovering useful iKiron, Shockley, Kruschwitz, Finch, & Haydochniques for uncovering hidden patterns and relta mining is also one step in an overall knowlednt to discover new information from the data in

ducation Journal

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information onreasingly heldhis pressure is

elated data.ors, thesearchers have

m courserging field of

ing methods to

ed. Educationaland techniquescus on topics

g data mining in

cational datamprove studentuccess andwithin courseata mining.

l of Educational

e EDMg theory, dataof the earliest

d theial intelligencepapers in

g research.udent retentionin courseiterature and

vailable to anIBM suggestsariety (IBM,and neednformation ink, 2012). Datationshipsge discoveryorder to aid in


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decision-making processes. Knodecision-making and organizatiodata analytics community to esta The Cross Industry Standfor developing and analyzing dat

important because it gives specifbusiness data through deploymeinclude business understanding,deployment (Leventhal, 2010).software vendor neutral, and pro2010). The model also includes teducational data mining studiesbut may not be explicitly stated

Data mining has its rootsand statistics (Dunham, 2003). Tapproaches, such as clustering, c

approaches can be used to quantipatterns. Data mining is an expl(Berson, Smith, & Thearling, 20in that data mining is highly explconfirmatory.

While data mining has beretail, and banking, data mining& Malik, 2007). Educational datto solve educationally-related prpractitioners discover new wayseducational data.

BACKGROUND OF EDUCA

There are different ways(2007) defined academic analytithat will help faculty and advisoresponding accordingly. In thisretention. Academic analytics foand university level. This type oso it can be said that academic aconsidered a sub-field of educati

Baker and Yacef (2009)developing methods for explorinand using those methods to bette(Baker & Yacef, 2009, p. 1). Thopen to exploring and developinrelated data. Also, many educatoneed to make it easy for educatothem (such as online CMS data,

Research in Higher


ledge discovery and data mining can be thougnal effectiveness. The complexity of data minin

blish a standard process for data mining activiti ard Process for Data Mining (CRISP-DM) is a

a mining models (Leventhal, 2010). The CRIS

ic tips and techniques on how to move from unt of a data mining model. CRISP-DM has six p ata understanding, data preparation, modeling,

he benefits of CRISM-DM are that it is non-prvides a solid framework for guidance in data miemplates to aid in analysis. This process is usedLuan, 2002; Vialardi et al., 2011; Y.-h. Wangs such.in machine learning, artificial intelligence, comhere are a variety of different data mining technlassification, and association rule mining. Each

tatively analyze large data sets to find hiddenratory process, but can be used for confirmator11). It is different from other searching and analoratory, where other analyses are typically pro

en applied in a variety of industries, governmeas not received much attention in educational

a mining is a field of study that analyzes and apblems. Applying data mining this way can hel

to uncover patterns and trends within large amo

IONAL DATA MINING

that educational data mining is defined. Campbs as the use of statistical techniques and data ms become more proactive in identifying at-riskay, the results of data mining can be used to iuses on processes that occur at the department,analysis does not focus on the details of each ialytics has a macro perspective. Academic anal

onal data mining.efined EDM as an emerging discipline, conce

g the unique types of data that come from educr understand students, and the settings which thir definition does not mention data mining, leaother analytical methods that can be applied t

rs would not know how to use data mining tools to conduct advanced analytics against data thtc.). One of the advantages to their research is

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t of as tools forg has led thes.ife cycle process-DM process is

erstanding thehases, whichevaluation, andprietary andning (Leventhal,in a number ofLiao, 2011),

puter science,iques andof these

eaning andinvestigationsysis techniqueslem-driven and

t, military,ontexts (Ranjanlies data miningresearchers and

unts of

ll and Oblingerining in waystudents andprove studentunit, or collegedividual course,ytics can be

rned withtional settings,y learn ining researcherseducationally, thus there is at pertains tohat it provides a


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broad representation of the EDHowever, their research used theEDM. Perhaps future research cgrowth.

In evaluating the above t

focuses on nearly any type of daspecific to data related to institutthe discipline relies on several regrowth in the interdisciplinary nrefine the scope and definitionsthorough taxonomy of the differhas already been established byYacefs taxonomy (2009) is thatPerhaps future research could ex

The scope of educationalexample, mining course content

later in this paper). Other areasadmissions, alumni relations, anmining techniques such as webstatistics are also key techniques2012). These data mining methoprediction and forecasting of leacan be used for modeling individthose differences thus improve sdo institutions adopt educational

In order for educational dwarehousing strategy. Guan et alinformation available for decisioto get the information that decisidrivers of initiating data warehoincreased responsibilities of repolegislators and community leade

Educational data miningOrganizational data mining (ODcompetitive advantage (Nematithat ODM relies on organizationOrganizations that transform theefficiently, should gain tremendcompetitiveness, and potential fifield draws upon organizationalfocus of research within EDM csocietal, organizational, unit, or i

The type of research donis necessary because data minintechniques. Many of the studiesmining projects were done at a s

Research in Higher


field so far by discussing the prominent papersnumber of article citations as a way to evaluaten used a broader perspective when evaluating t

o definitions, educational data mining is a bro

a in educational institutions, while academic anional effectiveness and student retention issues.ference disciplines and in the future, there willture of EDM. As the discipline grows, researchf EDM. At this early stage, it would be helpfulnt areas of study within EDM, even though a besearchers (Baker & Yacef, 2009). One drawbait does not address aspects of the clustering datpand on the clustering aspects of EDM.data mining includes areas that directly impactand the development of recommender systems (

ithin EDM include analysis of educational procourse selections. Furthermore, applications oining, classification, association rule mining, aapplied to educationally related data (Calderss are largely exploratory techniques that can bning and institutional improvement needs. Alsual differences in students and provide a way tudent learning (Corbett, 2001). Although, onedata mining to improve institutional effectiven

ata mining to be successful, it is critical to have. (2002) discussed how important it is to haven-makers within higher educational institutions.on makers need quickly and efficiently. Some ose projects include increased competitive landsrting to external stakeholders such as parents, bs (Guan, Nunez, & Welsh, 2002).an draw upon ideas from organizational data) focuses on assisting organizations with sustBarko, 2004). The key difference between D

al theory as a reference discipline (Nemati & Br data into useful information and knowledge, aus benefits such as enhanced decision-making,

nancial gains (Nemati & Barko, 2004). Therefoheory as well. This is an important relationshipn examine phenomena at different levels of anandividual level.within EDM focuses primarily on quantitativeemploys statistics, machine learning, and artifiresented in this literature review are case studiecific institution, with a single institutions dat

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in the field.growth ofis disciplines

der term that

alytics isAs noted earlier,e additional

ers will need toto have a moresic taxonomy

ck to Baker andmining task.

students. Forto be discussed

esses includingspecific datand multivariate

Pechenizkiy,used for

, the techniquesrespond touestion is howss?a solid dataeaningfulIt is a challenge

f the primarycape, andoard members,

ining.ining

and ODM isrko, 2004).nd do soincreasede, the EDMbecause thelysis, from

analyses, whichial intelligences where data

a. Qualitative


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techniques such as interviews anEDM. The dominant research papredictions, clusters or classificacase studies is that the results arethat the results are highly associ

EDM should examine ways forAPPLICATIONS OF DATA

A review of related literamining is used for improving stuEducational data mining researc(CMS) data can be mined to proand staff with improving learnininstitutional effectiveness.

Student Retention and AttritioResearch has shown that

institutions become much more2002). Luan (2002) applied dataout of school, and then return to(C&RT) a specific data miningstudents are unlikely to return toqualitative research techniques tbecause it demonstrated the succretention efforts. As noted earlieare not generalizable. However,generalized and used in other comay not be generalized.

In a related study, Lin (2efforts. Lin (2012) was able to gmodels were able to provide shobenefit from student retention prmachine learning algorithms can

Researchers at Bowie Stasupports and improves retentioninstitution identify and respond tEDM literature because it demoTheir work is highly representatiand is quantitative. Chacon et al.mining to student retention issueresults. The work by Chacon et aresearchers were able to developBowie State University uses the

Research in Higher


d document analysis are also used to support caradigm is quantitative, with results coming in thions, or associations. The drawback with somenot necessarily generalizable to other institutioted with a specific institution at a specific time.

ata mining results to be more generalizable.INING

ture in educational data mining follows. It focusdent success and processes directly related to st

examines different ways that course managemide new patterns of student behavior. Results cand supporting educational processes, which i

n

data mining can be used to discover at-risk studroactive in identifying and responding to thosemining as a way to predict what types of studenschool later on. He applied classification and retechnique to educational data in order to preschool. In this case study, Luan applied both quncover student success factors. This research

essful application of data mining tools to assist, the case study method for EDM may often prhe process by which researchers apply the datatexts. It is simply the results of the data mining

12) applied data mining as a way to improve stnerate predictive models based on incoming stt-term accuracy for predicting which types of s

ograms on campus. The research study found thprovide useful predictions of student retentionte University developed a system based on data(Chacon, Spicer, & Valbuena, 2012). Their syso at-risk students. Their research contributes mstrates a successful implementation and use ofve of the discipline in that it follows a strict dats (2012) research supports other work done ins, such as Lin (2012) and Luan (2012), all withl. goes one step further than Lin and Luan, becand implement their solution in a production e

system to aid in student retention efforts.

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e studies ine form ofof the existingns. This meansResearch in

es on how datadent learning.

ent systemsn assist faculty

n turn improve

ents and helpstudents (Luan,ts would dropression treesict whichantitative andis importantin studentduce results thatmining can bemodels that

udent retentiondents data. Theudents wouldat certainLin, 2012).mining thatem helps theaningfully to theata mining.mining processpplying data

successfuluse thevironment.


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Data mining was used tostudent achievement and studentMurray, 2010). Their work demprocess, i.e., the writing center, iapproach used a combination of

approach to data mining was helcan be used in an actual implemfound students who attend writinYeats et al. (2010) took a differeconnection between writing centstudent retention issues, but a futconcepts: writing center attenda

In another study, three dipredictors of student retention.classification trees, multivariateeducational data which resulted i

elements in retention efforts (Yuresearch, they also discovered thcoast counterparts do.

Academic performance atechniques. One research team uthey could in the academic yearincluded low-risk, medium risk,techniques including neural netrisk group had a high probabilityimportant in that they give facultway, because once a student dewith Director of Institutional Eff

In a related study, researstudents had any influence on thappeared inconclusive, potentiallfield of educational data miningdata mining methods. Yorke et adata. The problem with this apprdata mining techniques. It is cleaphrase data mining, especially wdrawback with the research Yorclassification, regression, or othethat researchers can still conductmislead the reader when describidifferent research team noted thastudent satisfaction or success (Tfindings related to student satisfthere are significantly more factthus far.

Research in Higher


assess the efficacy of a writing center in an effoprogress to the next grade (Yeats, Reddy, Whenstrated the ability to assess a specific education an effort to improve institutional effectivenesuantitative work and case study analysis. The

ful in understanding much more about the wayntation. Their research results were not surprisig centers tend to do better in their classes. Thent approach to analyzing student achievement ir attendance and student grades. It did not mak

ure study could examine the relationship betwece, student grades, and retention.ferent data mining techniques were used to detu, DiGangi, Jannesch-Pennell and Kaprolet (20

adaptive regression splines (MARS), and neuraln finding transferred hours, residency, and ethn

, DiGangi, Jannasch-Pennell, & Kaprolet, 2010 at east coast students tend to stay enrolled longe

nd student success can be predicted by using daed data mining to classify students into three gVandamme, Meskens, & Superby, 2007). Theand high-risk students. The authors used severaorks, random forests, and decision trees. The st

of failing or dropping out of school. These typy and staff a way to identify the at-risk studentsides to leave, it is hard to convince them to sta

ctiveness at Norwich University).hers examined whether the demographic backgir performance (Yorke et al., 2005). Results froy because of the type of analysis they did. Interis concerned with analytical methods, and not nl. (2005) used Microsoft Excel for their analysioach is that they discuss mining the data withour that researchers should exercise more cautionhen they are not referring to data mining technie et al. (2005) used these phrases, but never apr data mining technique. This particular researcdata analyses by using Excel, but researchers sng their approach. Contrary to the Yorke et al. (t demographic characteristics are not significanhomas & Galambos, 2004). The results seem toction or the prediction of student success. Oners that influence students success than what ha

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rt to analyzeler, Senior, &

nal support. Their researchixed-methods

s data miningng in that itesearch bythat it made the

e the link ton these three

rmine10) appliednetworks to

icity as critical

. Through thisr than their west

a miningoups as early ashree groupsdata miningdent in the highs of studies arein a proactive (discussion

round ofm the studystingly, thecessarily justand mining

t really applyingwhen using theues. Thelied any

h demonstratesould not2005) study, apredictors ofreport differentan concludes been studied


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Personal Learning Environme

Personal learning enviroalso directly relate to educationaproviding the various tools, servi

learning needs on the fly (Mdrisystems is quantitative and is wirecommender systems in order tRecommendations display relateemploys recommender systems tprobably like.

Recommender systemsbecause the recommendations shis not possible to apply existingare highly domain dependent (Sawith respect to applying recomm

attempt to understand or determifaculty members to control recoExisting recommender systems iconcerns, which open up additio

How can researchers andperformance? One research teameffort to improve student predictSchmidt-Thieme, 2010). This paarticles, probably more approprialgorithms and methods to imprthat it provides an analysis of wstudent performance.

Recommendations for fubrowsing behavior and improveannotated browsing events withrecommendations specifically foshowed that data mining can delihistory of student achievement.move through the material at thebrowsing model is much more e

Data mining was used inmultimedia learning systems. Thinto four main groups based on t& Liu, 2009). Although the resethat computer experience as a faof factors might influence preferexamine additional factors or degender, or ethnicity.

Data mining was used into help them learn more effectiv

Research in Higher


ts and Recommender Systems

ments (PLEs) and personal recommendation syl data mining. Personalized learning environmeces, and artifacts so that the system can adapt t

scher, 2010). Much of the work done related toely used in eCommerce. For example, Amazoncustomize the browsing experience for each uproducts that a consumer might purchase. Net

o help its subscribers find the types of movies t

ust be adapted when they are used in educationould coincide with educational objectives. Theecommender systems directly to educational dantos & Boticario, 2010). There are two significender systems in an educational context. First, t

ne the needs of learners. Second, there should bmendations for their learners (Santos & Botican the educational domain typically do not addreal research opportunities for the EDM researcheducational administrators use data mining to pexamined this issue by applying recommender

ion results (Thai-Nghe, Drumond, Krohn-Grimrticular research study is one of the more quantite for computer science study, because it focusve recommender systems. However, the valueich analytical methods are more accurate when

ther learning exercises were made based on a sstudent achievement. A data mining model waontextual factors, to produce new individualize

r course management systems (F.-H. Wang, 20ver highly personalized content, based on browhis also improved student learning because stu

ir own pace. The researchers also discovered thfective than using association rule mining mod

one study as a way to analyze users preferencee data mining clustering technique was used toheir preferences and computer experience (Chrrchers used student preferences as a variable ator that influences preferences, it is unknownnces in an online learning environment. Futureographics that contribute to student preference

another study to provide learners with many re ly and efficiently. A methodology called frequ

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stems (PRS)ts focus onstudents

recommender.com useser.flix alsoat they will

al contextseason is that itta because theynt challenges

he system must

e some way forrio, 2010).ss thesecommunity.

redict studentsystems in anerghe, &tatively rigorouss on underlyingf this study ispredicting

udents webs established thatd content8). The resultssing history andents couldt the contextualls.s in interactivelace students

sostomou, Chen,d determinedhat other typesresearch coulds, such as age,

ommendationsnt itemset


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mining was used to mine learnerlearners with different levels of rother recommender systems (Huproviding them with highly indi

A newer stream of resear

Tseng, Lin, and Chen (2011) aplearning content to mobile users.content than standard PCs and was network conditions, hardwarethis particular study is extremelycan benefit from data mining.

EDM AND COURSE MANA

A large number of researand how they can be improved t

research team developed a simplmanagement system and allows(Garca, Romero, Ventura, & decollaborate with each other and smining tools are complicated anprocesses, statistics, and machinprocess, thus it is quantitative. Tthen an application of specific dresearch and application contribdata mining activities. It is clearmining tools more accessible to

Course management systfind interesting patterns and trendata mining techniques to MoodlGarca, 2008). The benefit to misuch as testing, quizzes, reading,importance of pre-processing thetechniques to Moodle data. Theidata, even if a reader does not haand Weka as their data mining sare built on the Java language, s

Data mining can be usedindividual student. Data miningthrough a course on English langstatic course content, the courseat his or her own pace. This wasfor each student, and was a succwhere students begin a course wcourse.

Research in Higher


behavior patterns in an online course and subseecommendations rather than single ones that arang, Chen, & Cheng, 2007). This system assisteidualized recommendations for improved learnch focuses on mobile learning environments. A

lied data mining to help provide fast, dynamic,Mobile devices have very different requiremeneb browsers (Su, Tseng, Lin, & Chen, 2011). Tcapabilities, and the users preferences from thtechnical, it demonstrates how mobile learning

EMENT SYSTEMS

hers within EDM focus directly on course mansupport student learning outcomes and student

ified data mining toolkit that operates within thon-expert users to get data mining informationCastro, 2011). In addition, a toolkit allows teachare results. This research is important becauserequire deep expertise in data mining tools, mlearning algorithms. This study follows a typie data mining process usually follows a pre-prta mining techniques, and then a post-processintions will allow non-technical faculty to engagthat additional is needed in this area to make edon-technical users.ms such as open source Moodle can be mineds in student online behavior. A systematic met

e usage data was established (Cristbal Romering usage data is that it contains data about eveand discussion posts. Romero et al. (2008) discdata and then discuss specifics on how to applresearch results demonstrated how straightfor

ve much experience in this area. The authors alftware packages. These software programs arethey are extendable as well.

in such a way as to customize learning activitieas used to adapt learning exercises based on st

uage instruction (Y.-h. Wang & Liao, 2011). Inadapts to student learning, taking him or her thran effort to create significant and optimal learniss. This research could be applied to other typeth varying levels of competency, e.g., a compu

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quently, provideproduced from

d learners byng efficiency.study by Su,

personalizedts for managingey use data suchir device. Whileenvironments

agement systemssuccess. One

coursefor their coursesers tomost datathods andal data miningcessing phase,g phase. Thein educationalcational data

or usage data tood for applying, Ventura, &ry user activity,uss thedata miningard it is to mineo use both Keel

open source and

for eachudents progressstead of havingugh the course

ng experiencess of courseser programming


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Data mining was used toprogramming assignment. Blikstprogramming behaviors in an oneach student completed them. Tcourse. This quantitative data mi

used by students, and developedmode, and self-sufficients (BliksIn many online courses,

experience. One research team udiscussion forums because it waeach student (Dringus & Ellis, 2kind of information is embeddedto assess student progress in antechnical faculty would not knothere is a need to create tools tha

Like Blikstein (2011), D

data mining techniques. While texamines discussion board behaactivity. For example, the DM agoing to be different than the Dusually very specific and is usedfind ways of applying data minianalyzing a single aspect of their

In an online educationalstudent success. Students engagmining techniques to determine iThere were several factors thatdisengagement, which included tlength of time spent on pages. Alogon to an online course, their bhow to use the course environmetype of behavior when producin

One potential drawback tcan manipulate the system and acircumvent properties of the syst(Muldner, Burleson, Van de Sancan be done to minimize gamingal. (2011) used data mining techthat students, rather than the assiprovided numerous recommendasupplemental exercises, or the usdetected within the system.

Research in Higher


assess complex student behaviors with respect tein (2011) found results that showed different tline course. These log files contained different t

e events included coding and non-coding activining research helped discover different progra

three programming behavior profiles: copy-andtein, 2011).iscussion board posts are an important part of t

sed data mining as a strategy for assessing asynchallenging to manually assess the quality of t05). Their research attempts to answer the quesin online discussion groups. The data mining r

nline course. One drawback with this approachhow to apply data mining to get results for the

t are accessible to non-technical faculty membeingus and Ellis (2005) analyze student behavio

e former examines programming activity behaior. The analysis is different based upon the tyalysis programming tasks in a course managemanalysis for discussion boards. Each data mini

with a specific data set. However, may be more g to examine students behavior in a broader se

behavior within the CMS.nvironment, learner engagement is an importament with the course content can be analyzed

f there are disengaged learners (Cocea & Weibere revealed that contribute to predicting studehe speed at which students read through the pa

ditionally, their study also determined that whehavior is quite erratic, probably because the stnt itself. Therefore, an analysis should take intodata mining models.

o the use of online course management systemsoid learning. Gaming is the idea that students

em in order to make progress, while avoiding lee, & Vanlehn, 2011). Some researchers are inv

, and to make sure that students continue learniiques including Bayesian methods (Nave Bay

gnment or problem, was a better predictor of gations for discouraging gaming. These include se of an intelligent agent that displays disapprov

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o a three-weekpes of studentypes of events asties in the onlineming strategies

-pasters, mixed- e learning

hronouse postings bytion of whatsults were usedis that non-

ir students, thusrs.by applying

ior, the lattere of task orent system isng task isimportant to

nse, rather than

t aspect ofsing datalzahl, 2009).tes, and then students firstdent is learningaccount this

is that studentsttempt toarningestigating whatg. Muldner ets) and foundming. They alsopplying extra or

al if gaming is


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CONCLUSION AND FUTUR

Educational data miningand practitioners. This field assisways to improve institutional eff

for helping organizations enhancamong a large amount of data. Ain EDM was presented, from apattrition to finding new ways ofindividual student. Many opportanalysis to individual course-levresearch is extremely technical.continues to grow with the introannual conference. These were estill in its infancy. It will be exci

Bienkowski, Feng, and

data mining and learning analyticompelling avenues for further r a focus on usability and i development of decision

instructor intervention; development of tools for

data mining; and development of models t

Researchers have not addPlagiarism is a topic that facultypredictive capability in plagiaris

Future research can examight be. Currently, it appears textent of how institutions mightimproving related educational pradopt EDM or any initiatives whwould be interesting to determinEDM initiatives. There are a fewenrollment, but further work neemainstream EDM work.

REFERENCES

Baker, R., & Yacef, K. (2009).Future Visions.Journal

Berson, A., Smith, S., & ThearliRetrieved November 28,http://www.thearling.co

Research in Higher


WORK

(EDM) is an area full of exciting opportunitiests higher educational institutions with efficientectiveness and student learning. Data mining is

e decision making and analyzing new patternsbroad sense of the types of research currentlylying data mining for understanding student retaking personalized learning recommendations

nities exist to study EDM from an organizationls of analysis. Some work is strategic in natureverall, EDM draws upon several reference dis

uction of the Journal of Educational Data Ministablished only in 2008, which indicates that thing to see how EDM develops over the comingeans (2012) presented a thorough report on ho

s can enhance teaching and learning. The authsearch. These included:mpact of presenting learning data to instructors;support systems and recommendation systems t

protecting individual privacy while still advanc

at can be used in multiple contexts.ressed how data mining can be applied to plagibecome quite concerned with. Thus, it behoov

-related issues.

ine how widespread the adoption of educationahat research in this area is isolated and we do nbe using data mining for enhancing student learocesses. Furthermore, we do not know if thereere institutions are considering adopting an ED

if there are barriers that prevent institutions frcase studies on how EDM is applied to admiss

s to be done because those case studies seem i

he State of Educational Data mining in 2009:f Educational Data Mining, 1(1).g, K. (2011). An Overview of Data Mining Te

2011, from/text/dmtechniques/dmtechniques.htm

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esearch, Page 10

or researchersand effectivea significant tool

nd relationshipseing conductedntion andto eachal unit ofand some of theiplines andg and its relateddiscipline isyears.educational

rs outlined

hat minimize

ng educational

rism detection.s us to develop

l data miningt know the exacting orre intentions to

strategy. Itm establishingons andolated from the

Review and

hniques


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Blikstein, P. (2011). Using learnprogramming tasks. PapeConference on Learning

Calders, T., & Pechenizkiy, M. (mining. SIGKDD Explor.

Campbell, J., & Oblinger, D. (20Chacon, F., Spicer, D., & ValbuSuccess (Research BulletResearch.

Chrysostomou, K., Chen, S. Y.,Interactive Multimedia LLearning Environments,

Cocea, M., & Weibelzahl, S. (20environments. User Mod10.1007/s11257-009-906

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C o p y r i g h t o f R e s e a r c h i n H i g h e r E d u c a t i o n J o u r n a l i s t h e p r o p e r t y o f A c a d e m i c & B u s i n e s s

R e s e a r c h I n s t i t u t e a n d i t s c o n t e n t m a y n o t b e c o p i e d o r e m a i l e d t o m u l t i p l e s i t e s o r p o s t e d t o a

l i s t s e r v w i t h o u t t h e c o p y r i g h t h o l d e r ' s e x p r e s s w r i t t e n p e r m i s s i o n . H o w e v e r , u s e r s m a y p r i n t ,

d o w n l o a d , o r e m a i l a r t i c l e s f o r i n d i v i d u a l u s e .

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