Difference between revisions of "Task/Activity Quit Prediction"
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Rzepka et al. (2022) [https://www.insticc.org/node/TechnicalProgram/CSEDU/2022/presentationDetails/109621 pdf] | Rzepka et al. (2022) [https://www.insticc.org/node/TechnicalProgram/CSEDU/2022/presentationDetails/109621 pdf] | ||
* Models predicting whether student will quit spelling learning activity without completing | * Models predicting whether student will quit spelling learning activity without completing | ||
* Multiple algorithms have slightly better false positive rates for second-language speakers than native speakers, | * Multiple algorithms have slightly better false positive rates for second-language speakers than native speakers, but equivalent performance on multiple other metrics. | ||
but equivalent performance on multiple other metrics. | * Multiple algorithms have slightly better false positive rates and AUC ROC for students with at least one parent who graduated high school, but equivalent performance on multiple other metrics. | ||
* Multiple algorithms have slightly better false positive rates and AUC ROC for students with at least one parent who graduated | * Multiple algorithms have slightly better false positive rates and AUC ROC for male students than female students, but equivalent performance on multiple other metrics. | ||
high school, but equivalent performance on multiple other metrics. | |||
* Multiple algorithms have slightly better false positive rates and AUC ROC for male students than female students, | |||
but equivalent performance on multiple other metrics. | |||
Latest revision as of 23:00, 20 June 2022
Rzepka et al. (2022) pdf
- Models predicting whether student will quit spelling learning activity without completing
- Multiple algorithms have slightly better false positive rates for second-language speakers than native speakers, but equivalent performance on multiple other metrics.
- Multiple algorithms have slightly better false positive rates and AUC ROC for students with at least one parent who graduated high school, but equivalent performance on multiple other metrics.
- Multiple algorithms have slightly better false positive rates and AUC ROC for male students than female students, but equivalent performance on multiple other metrics.