Difference between revisions of "Short-term Performance and Learning Gains Prediction"

Latest revision as of 05:23, 19 May 2022

Ogan et al. (2015) pdf

Multi-national models predicting learning gains from student's help-seeking behavior
Models built on only U.S.or combined data sets performed extremely poorly for Costa Rica
Models performed better when built on and applied for the same country, except for Philippines where model built on that country which was outperformed slightly by model built on U.S. data

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 Ogan et al. (2015) [https://link.springer.com/content/pdf/10.1007/s40593-014-0034-8.pdf pdf]
-*Multi-national model predicting learning gains from student's help-seeking behavior
+*Multi-national models predicting learning gains from student's help-seeking behavior
-*Both U.S. and combined model performed extremely poorly for Costa Rica
+*Models built on only U.S.or combined data sets performed extremely poorly for Costa Rica
-*U.S. model outperformed for Philippines than when trained with its own data set
+*Models performed better when built on and applied for the same country, except for Philippines where model built on that country which was outperformed slightly by model built on U.S. data
-Yudelson et al. (2014) [https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.659.872&rep=rep1&type=pdf pdf]
-* Models discovering generalizable sub-populations of students across different schools to predict students' learning with Carnegie Learning’s Cognitive Tutor (CLCT)
-*Models trained on schools with a high proportion of low-SES student performed worse than those trained with medium or low proportion
-*Models trained on schools with low, medium  proportion of SES students performed similarly well for schools with high proportions of low-SES students