Predicting Students' Progression in Higher Education by Using the Random Forest Algorithm.
This paper proposes the use of data available at Manchester Metropolitan University to assess the variables that can best predict student progression. We combine virtual learning environment (VLE) and management information systems student records datasets and apply the Random Forest (RF) algorithm...
| Publicado en: | Systems Research & Behavioral Science Vol. 30; no. 2; pp. 194 - 204 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Wiley-Blackwell
Mar/Apr2013
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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=ssf&AN=86367252&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 86367252 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10927026 2SN jtl: Systems Research & Behavioral Science issn: 10927026 maglogo: Y pubinfo: dt: Mar/Apr2013 vid: 30 iid: 2 pid: 480 pub: Wiley-Blackwell artinfo: ui: 86367252 10.1002/sres.2130 ppf: 194 ppct: 10 formats: tig: atl: Predicting Students' Progression in Higher Education by Using the Random Forest Algorithm. aug: au: Hardman, Julie Paucar‐Caceres, Alberto Fielding, Alan affil: Manchester Metropolitan University, Business School, Manchester UK Manchester Metropolitan University, School of Science and Environment, Manchester UK su: Manchester Metropolitan University Higher education Classroom environment Achievement gains (Education) Courseware Management information systems Computer software sug: subj: Higher education Classroom environment Manchester Metropolitan University Computer and software stores Software publishers (except video game publishers) Computer and Computer Peripheral Equipment and Software Merchant Wholesalers Computer, computer peripheral and pre-packaged software merchant wholesalers Achievement gains (Education) Courseware Management information systems Computer software keyword: evaluation management information systems Random Forest student progression virtual learning environment evaluation management information systems Random Forest student progression virtual learning environment ab: This paper proposes the use of data available at Manchester Metropolitan University to assess the variables that can best predict student progression. We combine virtual learning environment (VLE) and management information systems student records datasets and apply the Random Forest (RF) algorithm to ascertain which variables can best predict students' progression. RF was deemed useful in this case because of the large amount of data available for analysis. The paper reports on the initial findings for data available in the period 2007-2008. Results seem to indicate that variables such as students' time of day usage, the last time students access the VLE and the number of document hits by staff are the best predictors of student progression. The paper contributes to VLE evaluation and highlights the usefulness of RF, a technique initially developed in the field of biology, in evaluating an educational and learning environment. Copyright © 2012 John Wiley & Sons, Ltd. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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