Gender: Male/Female - Revision history

Shruti at 04:13, 28 November 2023

2023-11-28T04:13:55Z

← Older revision		Revision as of 00:13, 28 November 2023
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	Cock et al.(2023) [[https://dl.acm.org/doi/abs/10.1145/3576050.3576149?casa_token=6Fjh-EUzN-gAAAAA%3AtpRMYzSAVoQFYNzwY5gwSsrnzHIlI0tUjMq6okwgdcCUmuBMVZEtn8eLO52dCtIYUbrHBV_Il9Sx pdf]]		Cock et al.(2023) [[https://dl.acm.org/doi/abs/10.1145/3576050.3576149?casa_token=6Fjh-EUzN-gAAAAA%3AtpRMYzSAVoQFYNzwY5gwSsrnzHIlI0tUjMq6okwgdcCUmuBMVZEtn8eLO52dCtIYUbrHBV_Il9Sx pdf]]
	* Paper investigates biases in models designed to early identify middle school students at risk of failing in flipped-classroom course and open-ended exploration environment (TugLet)		* Paper investigates biases in models designed to early identify middle school students at risk of failing in flipped-classroom course and open-ended exploration environment (TugLet)
	* Model performs worse for males in open-ended environment (FNR=0.70 for males and FNR of 0.53 for females)		* Model performs worse for males in open-ended environment (FNR=0.70 for males and FNR=0.53 for females)
	* Model performs worse for females in flipped classrooms(FNR=0.56 for females and FNR of 0.43 for males)		* Model performs worse for females in flipped classrooms(FNR=0.56 for females and FNR=0.43 for males)

Shruti at 04:00, 28 November 2023

2023-11-28T04:00:07Z

← Older revision		Revision as of 00:00, 28 November 2023
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	*Model detects interaction with the ASSISTments system		*Model detects interaction with the ASSISTments system
	*Model performs better for males (AUC =0.641 for RFPS; AUC =0.571 for LR) than female students (AUC = 0.492 for RFPS; AUC=0.535 for LR).		*Model performs better for males (AUC =0.641 for RFPS; AUC =0.571 for LR) than female students (AUC = 0.492 for RFPS; AUC=0.535 for LR).



			Cock et al.(2023) [[https://dl.acm.org/doi/abs/10.1145/3576050.3576149?casa_token=6Fjh-EUzN-gAAAAA%3AtpRMYzSAVoQFYNzwY5gwSsrnzHIlI0tUjMq6okwgdcCUmuBMVZEtn8eLO52dCtIYUbrHBV_Il9Sx pdf]]
			* Paper investigates biases in models designed to early identify middle school students at risk of failing in flipped-classroom course and open-ended exploration environment (TugLet)
			* Model performs worse for males in open-ended environment (FNR=0.70 for males and FNR of 0.53 for females)
			* Model performs worse for females in flipped classrooms(FNR=0.56 for females and FNR of 0.43 for males)

Shruti at 04:45, 1 October 2023

2023-10-01T04:45:35Z

← Older revision		Revision as of 00:45, 1 October 2023
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	*Model trained on smaller dataset achieves greater fairness in prediction for male and female students		*Model trained on smaller dataset achieves greater fairness in prediction for male and female students
	* For model trained on larger dataset, prediction is more accurate for female students than male students.		* For model trained on larger dataset, prediction is more accurate for female students than male students.


	Chiu (2020) [https://files.eric.ed.gov/fulltext/EJ1267654.pdf pdf]		Chiu (2020) [https://files.eric.ed.gov/fulltext/EJ1267654.pdf pdf]

Shruti: Addition

2023-10-01T04:45:06Z

Addition

← Older revision		Revision as of 00:45, 1 October 2023
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	*Model trained on smaller dataset achieves greater fairness in prediction for male and female students		*Model trained on smaller dataset achieves greater fairness in prediction for male and female students
	* For model trained on larger dataset, prediction is more accurate for female students than male students.		* For model trained on larger dataset, prediction is more accurate for female students than male students.

			Chiu (2020) [https://files.eric.ed.gov/fulltext/EJ1267654.pdf pdf]
			*Model identifies affective states (boredom, concentration, confusion, frustration, off task and gaming) of middle school students’ online mathematics learning in predicting their choice to study STEM in higher education.
			*Model detects interaction with the ASSISTments system
			*Model performs better for males (AUC =0.641 for RFPS; AUC =0.571 for LR) than female students (AUC = 0.492 for RFPS; AUC=0.535 for LR).

Shruti at 17:07, 17 August 2023

2023-08-17T17:07:34Z

← Older revision		Revision as of 13:07, 17 August 2023
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	Almoubayyed et al. (2023)[https://educationaldatamining.org/EDM2023/proceedings/2023.EDM-long-papers.18/2023.EDM-long-papers.18.pdf pdf]		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		* 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 and female students		*Model trained on smaller dataset achieves greater fairness in prediction for male and female students
	* For model trained on a larger dataset, prediction is more accurate for female students than male students.		* For model trained on larger dataset, prediction is more accurate for female students than male students.

Shruti at 16:57, 17 August 2023

2023-08-17T16:57:36Z

← Older revision		Revision as of 12:57, 17 August 2023
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	* Models developed to detect attributes of student feedback for other students’ mathematics solutions, reflecting the presence of three constructs:1) commenting on process, 2) commenting on the answer, and 3) relating to self.		* Models developed to detect attributes of student feedback for other students’ mathematics solutions, reflecting the presence of three constructs:1) commenting on process, 2) commenting on the answer, and 3) relating to self.
	* Models have approximately equal performance for males and females.		* Models have approximately equal performance for males and females.


			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 data achieves greater fairness in prediction for male and female students
			* For model trained on a larger dataset, prediction is more accurate for female students than male students.

Shruti: Addition

2023-06-29T01:07:46Z

Addition

← Older revision		Revision as of 21:07, 28 June 2023
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	Permodo et al. (2023) [~~http~~://~~Prediction%20is%20more%20accurate%20for%20students%20with%20Disabilities%20than%20students%20without%20Disabilities~~ pdf]		Permodo et al. (2023) [https://www.researchgate.net/publication/370001437_Difficult_Lessons_on_Social_Prediction_from_Wisconsin_Public_Schools pdf]
	* Paper discusses system that predicts probabilities of on-time graduation		* Paper discusses system that predicts probabilities of on-time graduation
	* DEWS prediction is comparable for ~~Males~~ and ~~Females~~		* DEWS prediction is comparable for males and females


			Zhang et al. (2023) [https://learninganalytics.upenn.edu/ryanbaker/ISLS23_annotation%20detector_short_submit.pdf pdf]
			* Models developed to detect attributes of student feedback for other students’ mathematics solutions, reflecting the presence of three constructs:1) commenting on process, 2) commenting on the answer, and 3) relating to self.
			* Models have approximately equal performance for males and females.

Shruti: Addition

2023-06-29T00:39:04Z

Addition

← Older revision		Revision as of 20:39, 28 June 2023
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	Kai et al. (2017) [https://www.upenn.edu/learninganalytics/ryanbaker/DLRN-eVersity.pdf pdf]		Kai et al. (2017) [https://www.upenn.edu/learninganalytics/ryanbaker/DLRN-eVersity.pdf pdf]
	* Models predicting student retention in an online college program		* Models predicting student retention in an online college program
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	Zhang et al. (~~in press~~)		Zhang et al. (2022)
	* Detecting student use of self-regulated learning (SRL) in mathematical problem-solving process		* Detecting student use of self-regulated learning (SRL) in mathematical problem-solving process
	* For each SRL-related detector, relatively small differences in AUC were observed across gender groups.		* For each SRL-related detector, relatively small differences in AUC were observed across gender groups.
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	* Predicting whether course grade will be above or below 0.5		* Predicting whether course grade will be above or below 0.5
	* Better prediction for female students in some courses, better prediction for male students in other courses		* Better prediction for female students in some courses, better prediction for male students in other courses


			Permodo et al. (2023) [http://Prediction%20is%20more%20accurate%20for%20students%20with%20Disabilities%20than%20students%20without%20Disabilities pdf]
			* Paper discusses system that predicts probabilities of on-time graduation
			* DEWS prediction is comparable for Males and Females

Ryan: Added Deho et al 2023

2023-02-23T13:08:09Z

Added Deho et al 2023

← Older revision		Revision as of 09:08, 23 February 2023
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	* Automated essay scoring models inferring text evidence usage		* Automated essay scoring models inferring text evidence usage
	* All algorithms studied have less than 1% of error explained by whether student is female and male		* All algorithms studied have less than 1% of error explained by whether student is female and male


	Sha et al. (2022) [https://ieeexplore.ieee.org/abstract/document/9849852]		Sha et al. (2022) [https://ieeexplore.ieee.org/abstract/document/9849852]
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	* A range of over-sampling methods tested		* A range of over-sampling methods tested
	* Regardless of over-sampling method used, course pass/fail performance was moderately better for males, dropout performance was slightly better for males, and forum post relevance performance was moderately better for females.		* Regardless of over-sampling method used, course pass/fail performance was moderately better for males, dropout performance was slightly better for males, and forum post relevance performance was moderately better for females.


			Deho et al. (2023) [https://files.osf.io/v1/resources/5am9z/providers/osfstorage/63eaf170a3fade041fe7c9db?format=pdf&action=download&direct&version=1]
			* Predicting whether course grade will be above or below 0.5
			* Better prediction for female students in some courses, better prediction for male students in other courses

Ryan: added Sha et al 2022

2022-08-31T20:22:22Z

added Sha et al 2022

← Older revision		Revision as of 16:22, 31 August 2022
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	* Automated essay scoring models inferring text evidence usage		* Automated essay scoring models inferring text evidence usage
	* All algorithms studied have less than 1% of error explained by whether student is female and male		* All algorithms studied have less than 1% of error explained by whether student is female and male

			Sha et al. (2022) [https://ieeexplore.ieee.org/abstract/document/9849852]
			* Three data sets and algorithms: predicting course pass/fail (random forest), dropout (neural network), and forum post relevance (neural network)
			* A range of over-sampling methods tested
			* Regardless of over-sampling method used, course pass/fail performance was moderately better for males, dropout performance was slightly better for males, and forum post relevance performance was moderately better for females.