Literaturnachweis - Detailanzeige
| Autor/inn/en | Gardner, Josh; Brooks, Christopher; Li, Warren |
|---|---|
| Titel | Learn from Your (Markov) Neighbour: Co-Enrollment, Assortativity, and Grade Prediction in Undergraduate Courses |
| Quelle | In: Journal of Learning Analytics, 5 (2018) 3, S.42-59 (18 Seiten)Infoseite zur Zeitschrift
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| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
| ISSN | 1929-7750 |
| Schlagwörter | Markov Processes; Classification; Undergraduate Students; Grade Point Average; State Universities; Longitudinal Studies; Networks; Predictor Variables; Comparative Analysis; Academic Achievement; Models; Enrollment; Network Analysis |
| Abstract | In this paper, we evaluate the complete undergraduate co-enrollment network over a decade of education at a large American public university. We provide descriptive and exploratory analyses of the network, demonstrating that the co-enrollment networks evaluated follow power-law degree distributions similar to many other large-scale networks; that they reveal strong performance-based assortativity; and that network-based features can improve GPA-based student performance predictors. We model the university-wide undergraduate co-enrollment network as an undirected graph, and implement multiple network-augmented approaches to student grade prediction, including an adaption of the structural modelling approach from (Getoor, 2005; Lu & Getoor, 2003a). We compare the performance of this predictor to traditional methods used for grade prediction in undergraduate university courses, and demonstrate that a multi-view ensembling approach outperforms both prior "flat" and network-based models for grade prediction across several classification metrics. These findings demonstrate the usefulness of combining diverse approaches in models of student success, and demonstrate specific network-based modelling strategies that are likely to be most effective for grade prediction. (As Provided). |
| Anmerkungen | Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: http://learning-analytics.info/journals/index.php/JLA/ |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2020/1/01 |
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