Student Knowledge Modeling

Yudelson et al. (2014) 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

Almoubayyed et al. (2023) pdf

• Models discovering generalization of the performance for reading comprehension ability in the context of middle school students’ usage of Carnegie Learning’s ITS for mathematics instruction
• Model trained on smaller data achieves greater fairness in prediction for male/female as well as white/non-white students
• For model trained on a larger dataset, prediction is more accurate for white and female students than non-white and male students.