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Descripción
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| This article analyses and reports empirical data from five different social tools pertaining to an actual MOOC to characterize top contributors and provide some insights aimed at facilitating their early detection. The results of this analysis show that top contributors have better final scores than the rest. In addition, there is a moderate positive correlation between participants' overall performance (measured in terms of final scores) and the number of posts submitted to the five social tools. This article also studies the effect of participants' gender and scores as factors that can be used for the early detection of top contributors. The analysis shows that gender is not a good predictor and that taking the scores of the first assessment activities of each type (test and peer assessment in the case study) results in a prediction that is not substantially improved by adding subsequent activities. Finally, better predictions based on scores are obtained for aggregate contributions in the five social tools than for individual contributions in each social tool. | |
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Internacional
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Si |
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JCR del ISI
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Si |
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Título de la revista
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Journal of Computer Assisted Learning |
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ISSN
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0266-4909 |
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Factor de impacto JCR
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1,023 |
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Información de impacto
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Datos JCR del año 2013 |
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Volumen
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32 |
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DOI
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Número de revista
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3 |
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Desde la página
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232 |
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Hasta la página
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243 |
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Mes
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JUNIO |
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Ranking
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