Difference between revisions of "Student Knowledge Modeling"
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Yudelson et al. (2014) [https:// | Yudelson et al. (2014) [https://www.yudelson.info/pdf/EDM2014_YudelsonFRBNJ.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 | |||
Almoubayyed et al. (2023) [https://educationaldatamining.org/EDM2023/proceedings/2023.EDM-long-papers.18/2023.EDM-long-papers.18.pdf pdf] | |||
Models | * 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 12:06, 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.