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

From Penn Center for Learning Analytics Wiki
Jump to navigation Jump to search
Line 3: Line 3:
*Models built on only U.S.or combined data sets performed extremely poorly for Costa Rica
*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 dataset, except for Philippines which was outperformed slightly by U.S. model
*Models performed better when built on and applied for the dataset, except for Philippines which was outperformed slightly by U.S. model
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

Revision as of 08:26, 18 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 dataset, except for Philippines which was outperformed slightly by U.S. model
Retrieved from "https://www.pcla.wiki/index.php?title=Short-term_Performance_and_Learning_Gains_Prediction&oldid=271"