Difference between revisions of "Self-regulated Learning"
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(Created page with "Zhang et al. (in press) [pdf] * Four detectors (i.e., numerical representation, contextual representation, outcome orientation, and data transformation) relating to two cognitive operations (assembling and translating) were built to detect middle school students' use of self-regulated learning in mathematical problem-solving process. * Detectors were built using XGBoost with labels coded from text replays and features distilled from log data and textual responses. * Com...") |
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* Four detectors (i.e., numerical representation, contextual representation, outcome orientation, and data transformation) relating to two cognitive operations (assembling and translating) were built to detect middle school students' use of self-regulated learning in mathematical problem-solving process. | * Four detectors (i.e., numerical representation, contextual representation, outcome orientation, and data transformation) relating to two cognitive operations (assembling and translating) were built to detect middle school students' use of self-regulated learning in mathematical problem-solving process. | ||
* Detectors were built using XGBoost with labels coded from text replays and features distilled from log data and textual responses. | * Detectors were built using XGBoost with labels coded from text replays and features distilled from log data and textual responses. | ||
* | * In each detector, relatively small differences in AUC were observed across gender and racial/ethnic groups, and no student group (either gender or racial/ethnic group) consistently had the best-performing detectors | ||
Revision as of 12:44, 1 June 2022
Zhang et al. (in press) [pdf]
- Four detectors (i.e., numerical representation, contextual representation, outcome orientation, and data transformation) relating to two cognitive operations (assembling and translating) were built to detect middle school students' use of self-regulated learning in mathematical problem-solving process.
- Detectors were built using XGBoost with labels coded from text replays and features distilled from log data and textual responses.
- In each detector, relatively small differences in AUC were observed across gender and racial/ethnic groups, and no student group (either gender or racial/ethnic group) consistently had the best-performing detectors