Literaturnachweis - Detailanzeige
| Autor/inn/en | Gardner, Josh; Yang, Yuming; Baker, Ryan S.; Brooks, Christopher |
|---|---|
| Titel | Modeling and Experimental Design for MOOC Dropout Prediction: A Replication Perspective [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (12th, Montreal, Canada, Jul 2-5, 2019). |
| Quelle | (2019), (10 Seiten)
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| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | Online Courses; Large Group Instruction; Prediction; Models; Long Term Memory; Short Term Memory; Artificial Intelligence; Dropouts; Generalization; Learning Analytics |
| Abstract | Replication of machine learning experiments can be a useful tool to evaluate how both "modeling" and "experimental design" contribute to experimental results; however, existing replication efforts focus almost entirely on modeling alone. In this work, we conduct a three-part replication case study of a state-of-the-art LSTM dropout prediction model. In our first experiment, we replicate the original authors' methodology as precisely as possible in collaboration with the original authors. In a second experiment, we demonstrate that this initial experiment likely overestimates the generalization performance of the proposed model due to the design of its validation. In a third experiment, we attempt to achieve the previously-reported performance in a more difficult, but more relevant, hold-out set design by exploring a large space of model regularization configurations. We demonstrate that we can reduce overfitting and improve generalization performance of the model, but cannot achieve the previously-reported level of performance. This work demonstrates the importance of replication of predictive modeling experiments in education, and demonstrates how experimental design and modeling decisions can impact the extent to which mode [For the full proceedings, see ED599096.] (As Provided). |
| Anmerkungen | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2020/1/01 |
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