In search for the most informative data for feedback generation: Learning analytics in a data-rich context.
Learning analytics seek to enhance the learning processes through systematic measurements of learning related data and to provide informative feedback to learners and teachers. Track data from learning management systems (LMS) constitute a main data source for learning analytics. This empirical cont...
| Publicado en: | Computers in Human Behavior Vol. 47; pp. 157 - 168 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Elsevier B.V.
Jun2015
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| Materias: | |
| 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=101498567&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 101498567 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 07475632 JC4 jtl: Computers in Human Behavior issn: 07475632 maglogo: N pubinfo: dt: Jun2015 vid: 47 pid: 2410 pub: Elsevier B.V. artinfo: ui: 101498567 10.1016/j.chb.2014.05.038 ppf: 157 ppct: 11 formats: tig: atl: In search for the most informative data for feedback generation: Learning analytics in a data-rich context. aug: au: Tempelaar, Dirk T. Rienties, Bart Giesbers, Bas affil: Maastricht University, School of Business and Economics, PO Box 616, 6200 MD Maastricht, Netherlands Open University UK, Institute of Educational Technology, UK Rotterdam School of Management, Erasmus University, Netherlands su: Alternative education Educational technology Rating of students Quantitative research Teaching methods Problem-based learning User interfaces Behavioral objectives (Education) sug: subj: Alternative education Educational technology Rating of students Quantitative research Teaching methods Problem-based learning User interfaces Behavioral objectives (Education) keyword: Blended learning Dispositional learning analytics e-Tutorials Formative assessment Learning dispositions Blended learning Dispositional learning analytics e-Tutorials Formative assessment Learning dispositions ab: Learning analytics seek to enhance the learning processes through systematic measurements of learning related data and to provide informative feedback to learners and teachers. Track data from learning management systems (LMS) constitute a main data source for learning analytics. This empirical contribution provides an application of Buckingham Shum and Deakin Crick’s theoretical framework of dispositional learning analytics: an infrastructure that combines learning dispositions data with data extracted from computer-assisted, formative assessments and LMSs. In a large introductory quantitative methods module, 922 students were enrolled in a module based on the principles of blended learning, combining face-to-face problem-based learning sessions with e-tutorials. We investigated the predictive power of learning dispositions, outcomes of continuous formative assessments and other system generated data in modelling student performance of and their potential to generate informative feedback. Using a dynamic, longitudinal perspective, computer-assisted formative assessments seem to be the best predictor for detecting underperforming students and academic performance, while basic LMS data did not substantially predict learning. If timely feedback is crucial, both use-intensity related track data from e-tutorial systems, and learning dispositions, are valuable sources for feedback generation. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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