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Towards a learning analytics reference framework to predict at-risk students at the Faculty of Military Science
Thesis (MMil)--Stellenbosch University, 2022.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2022
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| _version_ | 1867613815930093568 |
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| access_status_str | Open Access |
| author | Pretorius, Andre |
| author2 | Khoza, Lindiwe Mhaka |
| author_browse | Khoza, Lindiwe Mhaka Pretorius, Andre |
| author_facet | Khoza, Lindiwe Mhaka Pretorius, Andre |
| author_sort | Pretorius, Andre |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MMil)--Stellenbosch University, 2022. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/125878 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:42:07.859Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/125878 Towards a learning analytics reference framework to predict at-risk students at the Faculty of Military Science Pretorius, Andre Khoza, Lindiwe Mhaka Dalton, Wayne Owen Stellenbosch University. Faculty of Military Science. School for Geospatial Studies and Information Systems. Learning analytics Machine Learning Electronic discussion groups At-risk youth -- Study and teaching Stellenbosch University. Faculty of Military Science UCTD Thesis (MMil)--Stellenbosch University, 2022. ENGLISH 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. AFRIKAANSE 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. Masters 2022-08-19T12:44:36Z 2023-01-16T12:39:29Z 2022-08-19T12:44:36Z 2023-01-16T12:39:29Z 2022-12 Thesis http://hdl.handle.net/10019.1/125878 en_ZA Stellenbosch University xxi, 256 pages : illustrations application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Learning analytics Machine Learning Electronic discussion groups At-risk youth -- Study and teaching Stellenbosch University. Faculty of Military Science UCTD Pretorius, Andre Towards a learning analytics reference framework to predict at-risk students at the Faculty of Military Science |
| title | Towards a learning analytics reference framework to predict at-risk students at the Faculty of Military Science |
| title_full | Towards a learning analytics reference framework to predict at-risk students at the Faculty of Military Science |
| title_fullStr | Towards a learning analytics reference framework to predict at-risk students at the Faculty of Military Science |
| title_full_unstemmed | Towards a learning analytics reference framework to predict at-risk students at the Faculty of Military Science |
| title_short | Towards a learning analytics reference framework to predict at-risk students at the Faculty of Military Science |
| title_sort | towards a learning analytics reference framework to predict at risk students at the faculty of military science |
| topic | Learning analytics Machine Learning Electronic discussion groups At-risk youth -- Study and teaching Stellenbosch University. Faculty of Military Science UCTD |
| url | http://hdl.handle.net/10019.1/125878 |
| work_keys_str_mv | AT pretoriusandre towardsalearninganalyticsreferenceframeworktopredictatriskstudentsatthefacultyofmilitaryscience |