Towards a learning analytics reference framework to predict at-risk students at the Faculty of Military Science

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dc.contributor.advisorKhoza, Lindiwe Mhakaen_ZA
dc.contributor.advisorDalton, Wayne Owenen_ZA
dc.contributor.authorPretorius, Andreen_ZA
dc.contributor.otherStellenbosch University. Faculty of Military Science. School for Geospatial Studies and Information Systems.en_ZA
dc.date.accessioned2022-08-19T12:44:36Z
dc.date.accessioned2023-01-16T12:39:29Z
dc.date.available2022-08-19T12:44:36Z
dc.date.available2023-01-16T12:39:29Z
dc.date.issued2022-12
dc.descriptionThesis (MMil)--Stellenbosch University, 2022.en_ZA
dc.description.abstractENGLISH ABSTRACT: Learning analytics (LA) is a relatively new field of application in the Analytics domain. Its main aim is to analyse teaching and learning (T&L) data from various sources to provide users with insights towards improving T&L. One of these T&L improvements is a greater focus on student success and more accurate methods of limiting student failure. This process starts with the identification of students at risk of failure (so-called “at-risk” students) through a prediction methodology which commonly falls within the knowledge sphere of Artificial Intelligence (AI), more specifically Machine Learning (ML). In contemporary information systems, the supporting platform for this is provided by an LA information system (LAIS) that relies on an underlying virtual learning environment (VLE), which in turn uses T&L data from a learning management system (LMS). A reference framework (RF) establishes a common foundation for future implementation of a system for developers and users. It provides appropriate guidance to users in a specific field of knowledge. Guidance is, however, generic in nature to secure reusability. This research focussed on developing an RF to implement LA in the Faculty of Military Science (FMS) of Stellenbosch University (SU) for at-risk student identification. The RF is supported by five models and one framework, namely, (1) a pedagogical model, (2) a model for effective VLEs, (3) a model for LA implementation, (4) a model for at-risk student identification and (5) a framework for the ethical use of LA. It is the conclusion of the study that the RF for LA in the FMS will provide suitable guidance for future implementation of LA in the faculty to effect timely identification of at-risk students and fitting remedial actions towards greater throughput may be implemented. It is envisioned that this RF be validated in the FMS in the near future and that future research in the use of ML be extended to identify suitable indicators of at-risk students more accurately.en_ZA
dc.description.abstractAFRIKAANSE OPSOMMING: Leeranalitiek (LA) is ‘n relatief nuwe studieveld wat sy toepassing binne die domein van Analitiek vind. Dit het ten doel om Leer- en Onderrigdata wat onttrek word uit ‘n verskeidenheid van bronne te ontleed ten einde gebruikers van nuwe insigte ter verbetering van L&O te voorsien. Een sodanige L&O-verbetering is ‘n groter fokus op verbetering van studentesukses, en gepaardgaande groter akkuraatheid met metodes om studentemislukking te bekamp. Hierdie proses begin met ‘n identifisering van studente wat die risiko loop om te misluk (sogenaamde “risikostudente”) deur middel van ‘n voorspellingsmetodiek wat normaalweg binne die kennisveld van Kunsmatige Intelligensie (KI) val, naamlik Masjienleer (ML). In kontemporere informasiestelsels (IS) word die steunplatform hiervoor deur ‘n Leeranalitika-informasiestelsel (LAIS) verskaf. LAIS steun op ‘n onderliggende virtuele leeromgewing (VLO) wat op sy beurt L&O-data onttrek uit ‘n leerbestuurstelsel (LBS). ‘n Verwysingsraamwerk (VR) vestig ‘n gemeenskaplike basis vir toekomstige implimentering van ‘n stelsel vir toekomstige ontwikkelaars en gebruikers. Dit verskaf gepaste riglyne aan gebruikers op ‘n besondere kennisgebied. Die riglyne is egter generies van aard om herbruikbaarheid te verseker. Hierdie studie se fokus was die ontwikkeling van ‘n VR om LA in die Fakulteit Krygskunde (Faculty of Military Sciences, FMS) van Stellenbosch Universiteit vir die tydige identifisering van risikostudente aan te wend. Die VR word ondersteun deur vier modelle, en een raamwerk, naamlik: (1) ‘n pedagogiese model, (2) ‘n model vir effektiewe VLOs, (3) ‘n model vir LA-toepassing, (4) ‘n model vir identifisering van risikostudente, en (5) ‘n raamwerk vir die etiese benutting van LA. Die gevolgtrekking van hierdie navorsing is dat die VR vir LA in die Fakulteit Krygskunde gepaste riglyne behoort te voorsien vir toekomstige implimentering van LA in hierdie Fakulteit vir die tydige identifisering van risikostudente, sodat gepaste remedierende optrede tot voordeel van hoer deurvloei verseker kan word. Die navorser voorsien dat die VR in die Fakulteit Krygskunde in die afsienbare toekoms bekragtig sal word; dat toekomstige navorsing in die benutting van ML uitgebrei sal word om gepaste aanwysers van risikostudente meer akkuraat te identifiseer.af_ZA
dc.description.versionMastersen_ZA
dc.format.extentxxi, 256 pages : illustrationsen_ZA
dc.identifier.urihttp://hdl.handle.net/10019.1/125878
dc.language.isoen_ZAen_ZA
dc.publisherStellenbosch : Stellenbosch Universityen_ZA
dc.rights.holderStellenbosch Universityen_ZA
dc.subjectLearning analyticsen_ZA
dc.subjectMachine Learningen_ZA
dc.subjectElectronic discussion groupsen_ZA
dc.subjectAt-risk youth -- Study and teachingen_ZA
dc.subjectStellenbosch University. Faculty of Military Scienceen_ZA
dc.subjectUCTD
dc.titleTowards a learning analytics reference framework to predict at-risk students at the Faculty of Military Scienceen_ZA
dc.typeThesisen_ZA
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