Difference between revisions of "Student Knowledge Modeling"
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*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 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 | *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) [https://educationaldatamining.org/EDM2023/proceedings/2023.EDM-long-papers.18/2023.EDM-long-papers.18.pdf 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 dataset achieves greater fairness in prediction for male/female as well as white/non-white students | |||
* For model trained on larger dataset, prediction is more accurate for white and female students than non-white and male students. | |||
Latest revision as of 13:06, 17 August 2023
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 dataset achieves greater fairness in prediction for male/female as well as white/non-white students
- For model trained on larger dataset, prediction is more accurate for white and female students than non-white and male students.