Difference between revisions of "Student Knowledge Modeling"
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(Created page with "Yudelson et al. (2014) pdf Models structuring schools into reliably discernible groups 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") |
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Yudelson et al. (2014) pdf | 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.