A COMPARISON OF THE UTILITY OF DATA MINING ALGORITHMS IN AN OPEN DISTANCE LEARNING CONTEXT.
The use of data mining within the higher education context has, increasingly, been gaining traction. A parallel examination of the accuracy, robustness and utility of the algorithms applied to data mining is argued as a necessary step toward entrenching the use of EDM. This article provides a compar...
| Publicado en: | South African Journal of Higher Education Vol. 32; no. 4; pp. 81 - 96 |
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| Autores principales: | , |
| Formato: | Artículo |
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Sabinet Online Limited
2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=131470041&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 131470041 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 10113487 SG3 jtl: South African Journal of Higher Education issn: 10113487 maglogo: N pubinfo: dt: 2018 vid: 32 iid: 4 pid: 31334 pub: Sabinet Online Limited artinfo: ui: 131470041 10.20853/32-4-2473 ppf: 81 ppct: 15 formats: tig: atl: A COMPARISON OF THE UTILITY OF DATA MINING ALGORITHMS IN AN OPEN DISTANCE LEARNING CONTEXT. aug: au: Fynn, A. Adamiak, J. affil: Department of Psychology Student Success Unit su: Distance education Data mining Algorithms Educational technology Academic achievement sug: subj: Distance education Data mining Algorithms Educational technology Academic achievement keyword: Educational Data Mining J48 learning analytics logistic regression student success WEKA ab: The use of data mining within the higher education context has, increasingly, been gaining traction. A parallel examination of the accuracy, robustness and utility of the algorithms applied to data mining is argued as a necessary step toward entrenching the use of EDM. This article provides a comparative analysis of various classification algorithms within an Open Distance Learning institution in South Africa. The study compares the performance of the ZeroR, OneR, Naïve Bayes, IBk, Simple Logistic Regression and the J48 in classifying students within a cohort over an eightyear time span. The initial results appear to show that, given the data quality and structure of the institution under study, the J48 most consistently performed with the highest levels of accuracy. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2018 holdings: @attributes: islocal: N |
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