Self-regulated Learning - Revision history

Ryan at 10:15, 10 June 2022

2022-06-10T10:15:09Z

← Older revision		Revision as of 06:15, 10 June 2022
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	Zhang et al. (in press) https://www.upenn.edu/learninganalytics/ryanbaker/EDM22_paper_35.pdf]		Zhang et al. (in press) [https://www.upenn.edu/learninganalytics/ryanbaker/EDM22_paper_35.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.		* 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		* 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

Ryan: added citation

2022-06-10T10:14:57Z

added citation

← Older revision		Revision as of 06:14, 10 June 2022
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	Zhang et al. (in press) [pdf]		Zhang et al. (in press) https://www.upenn.edu/learninganalytics/ryanbaker/EDM22_paper_35.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.		* 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		* 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

Joyce at 16:44, 1 June 2022

2022-06-01T16:44:05Z

← Older revision		Revision as of 12:44, 1 June 2022
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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.
	* ~~Comparing the AUC across student groups~~, relatively small differences were observed, and no student group (either gender or racial/ethnic group) consistently had the best-performing detectors		* 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

Joyce: 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..."

2022-06-01T15:57:42Z

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..."

New page

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.
* Comparing the AUC across student groups, relatively small differences were observed, and no student group (either gender or racial/ethnic group) consistently had the best-performing detectors