Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems
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| Title: | Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems |
|---|---|
| Language: | English |
| Authors: | Steven Dang, Michael Yudelson, Kenneth R. Koedinger |
| Source: | Grantee Submission. 2017. |
| Peer Reviewed: | Y |
| Page Count: | 9 |
| Publication Date: | 2017 |
| Sponsoring Agency: | Institute of Education Sciences (ED) |
| Contract Number: | R305B150008 |
| Document Type: | Speeches/Meeting Papers Reports - Research |
| Education Level: | Junior High Schools Middle Schools Secondary Education |
| Descriptors: | Student Behavior, Intelligent Tutoring Systems, Educational Technology, Academic Achievement, Self Control, Executive Function, Middle School Students, Algebra, Mathematics Education, Suburban Schools, Student Motivation, Metacognition |
| Assessment and Survey Identifiers: | Motivated Strategies for Learning Questionnaire |
| DOI: | 10.1145/3051457.3051470 |
| Abstract: | 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. [This paper was published in: "Proceedings of the Fourth ACM Conference on Learning @ Scale (L@S 2017)," ACM, 2017.] |
| Abstractor: | As Provided |
| IES Funded: | Yes |
| Entry Date: | 2025 |
| Accession Number: | ED664038 |
| Database: | ERIC |
| Abstract: | 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. [This paper was published in: "Proceedings of the Fourth ACM Conference on Learning @ Scale (L@S 2017)," ACM, 2017.] |
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| DOI: | 10.1145/3051457.3051470 |
