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Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems

, , и . Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale, стр. 51--59. New York, NY, USA, ACM, (2017)
DOI: 10.1145/3051457.3051470

Аннотация

The current study introduces a model for measuring student diligence using online behaviors during intelligent tutoring system use. This model is validated using a full academic year dataset to test its predictive validity against long-term academic outcomes including end-of-year grades and total work completed by the end of the year. The model is additionally validated for robustness to time-sample length as well as data sampling frequency. While the model is shown to be predictive and robust to time-sample length, the results are inconclusive for robustness in data sampling frequency. Implications for research on interventions, and understanding the influence of self-control, motivation, metacognition, and cognition are discussed.

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