Other NLP Applications of Algorithms in Education - Revision history

Shruti at 01:10, 29 June 2023

2023-06-29T01:10:26Z

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 Naismith et al. (2018) [http://d-scholarship.pitt.edu/40665/1/EDM2018_paper_37.pdf pdf]
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 * A range of over-sampling methods tested
 * Regardless of over-sampling method used, forum post relevance performance was moderately better for females.

Shruti: edit

2023-06-28T22:00:14Z

edit

← Older revision		Revision as of 18:00, 28 June 2023
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	Zhang et al.(2023) [https://learninganalytics.upenn.edu/ryanbaker/ISLS23_annotation%20detector_short_submit.pdf pdf]		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 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 versus females and for African American, Hispanic/Latinx, and White students.		* Models have approximately equal performance for males versus females and for African American, Hispanic/Latinx, and White students.

Shruti: addition new paper

2023-06-28T21:59:53Z

addition new paper

← Older revision		Revision as of 17:59, 28 June 2023
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			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 versus females and for African American, Hispanic/Latinx, and White students.

	Naismith et al. (2018) [http://d-scholarship.pitt.edu/40665/1/EDM2018_paper_37.pdf pdf]		Naismith et al. (2018) [http://d-scholarship.pitt.edu/40665/1/EDM2018_paper_37.pdf pdf]

Ryan: added Sha et al 2022

2022-08-31T20:25:58Z

added Sha et al 2022

← Older revision		Revision as of 16:25, 31 August 2022
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	* ABROCA varied from 0.03 to 0.08 for non-native speakers of English versus native speakers		* ABROCA varied from 0.03 to 0.08 for non-native speakers of English versus native speakers
	* Balancing the size of each group in the training set reduced ABROCA values		* Balancing the size of each group in the training set reduced ABROCA values


			Sha et al. (2022) [https://ieeexplore.ieee.org/abstract/document/9849852]
			* Predicting forum post relevance to course in Moodle data (neural network)
			* A range of over-sampling methods tested
			* Regardless of over-sampling method used, forum post relevance performance was moderately better for females.

Ryan at 16:22, 4 July 2022

2022-07-04T16:22:42Z

← Older revision		Revision as of 12:22, 4 July 2022
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	* Models predicting a MOOC discussion forum post is content-relevant or content-irrelevant		* Models predicting a MOOC discussion forum post is content-relevant or content-irrelevant
	* MOOCs taught in English		* MOOCs taught in English
	* Some algorithms achieved ABROCA under 0.01 for female students versus male students,		* Some algorithms achieved ABROCA under 0.01 for female students versus male students, but other algorithms (Naive Bayes) had ABROCA as high as 0.06
	but other algorithms (Naive Bayes) had ABROCA as high as 0.06
	* ABROCA varied from 0.03 to 0.08 for non-native speakers of English versus native speakers		* ABROCA varied from 0.03 to 0.08 for non-native speakers of English versus native speakers
	* Balancing the size of each group in the training set reduced ABROCA values		* Balancing the size of each group in the training set reduced ABROCA values

Ryan: Added Sha et al (2021)

2022-07-04T16:22:21Z

Added Sha et al (2021)

← Older revision		Revision as of 12:22, 4 July 2022
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	* Model trained on urban students (authenticity: 0.62, uptake: 0.60) performed with similar accuracy when tested on non-urban students (authenticity: 0.62, uptake: 0.62)		* Model trained on urban students (authenticity: 0.62, uptake: 0.60) performed with similar accuracy when tested on non-urban students (authenticity: 0.62, uptake: 0.62)
	* Model trained on non-urban (authenticity: 0.61, uptake: 0.59) performed with similar accuracy when tested on urban students (authenticity: 0.60, uptake: 0.63)		* Model trained on non-urban (authenticity: 0.61, uptake: 0.59) performed with similar accuracy when tested on urban students (authenticity: 0.60, uptake: 0.63)


			Sha et al. (2021) [https://angusglchen.github.io/files/AIED2021_Lele_Assessing.pdf pdf]
			* Models predicting a MOOC discussion forum post is content-relevant or content-irrelevant
			* MOOCs taught in English
			* Some algorithms achieved ABROCA under 0.01 for female students versus male students,
			but other algorithms (Naive Bayes) had ABROCA as high as 0.06
			* ABROCA varied from 0.03 to 0.08 for non-native speakers of English versus native speakers
			* Balancing the size of each group in the training set reduced ABROCA values

Seiyon at 12:25, 18 May 2022

2022-05-18T12:25:05Z

← Older revision		Revision as of 08:25, 18 May 2022
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	Naismith et al. (2018) [[http://d-scholarship.pitt.edu/40665/1/EDM2018_paper_37.pdf pdf]]		Naismith et al. (2018) [http://d-scholarship.pitt.edu/40665/1/EDM2018_paper_37.pdf pdf]

	* a model that measures L2 learners’ lexical sophistication with the frequency list based on the native speaker corpora		* a model that measures L2 learners’ lexical sophistication with the frequency list based on the native speaker corpora
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	Samei et al. (2015) [[https://files.eric.ed.gov/fulltext/ED560879.pdf pdf]]		Samei et al. (2015) [https://files.eric.ed.gov/fulltext/ED560879.pdf pdf]

	* Models predicting classroom discourse properties (e.g. authenticity and uptake)		* Models predicting classroom discourse properties (e.g. authenticity and uptake)
	* Model trained on urban students (authenticity: 0.62, uptake: 0.60) performed with similar accuracy when tested on non-urban students (authenticity: 0.62, uptake: 0.62)		* Model trained on urban students (authenticity: 0.62, uptake: 0.60) performed with similar accuracy when tested on non-urban students (authenticity: 0.62, uptake: 0.62)
	* Model trained on non-urban (authenticity: 0.61, uptake: 0.59) performed with similar accuracy when tested on urban students (authenticity: 0.60, uptake: 0.63)		* Model trained on non-urban (authenticity: 0.61, uptake: 0.59) performed with similar accuracy when tested on urban students (authenticity: 0.60, uptake: 0.63)

Seiyon at 07:17, 17 February 2022

2022-02-17T07:17:55Z

← Older revision		Revision as of 03:17, 17 February 2022
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	* Level 5 Arabic-speaking learners are unfairly evaluated to have similar level of lexical sophistication as Level 4 learners from China, Japan, Korean and Spain .		* Level 5 Arabic-speaking learners are unfairly evaluated to have similar level of lexical sophistication as Level 4 learners from China, Japan, Korean and Spain .
	* When used on ETS corpus, “high”-labeled essays by Japanese-speaking learners are rated significantly lower in lexical sophistication than Arabic, Japanese, Korean and Spanish peers.		* When used on ETS corpus, “high”-labeled essays by Japanese-speaking learners are rated significantly lower in lexical sophistication than Arabic, Japanese, Korean and Spanish peers.


			Samei et al. (2015) [[https://files.eric.ed.gov/fulltext/ED560879.pdf pdf]]

			* Models predicting classroom discourse properties (e.g. authenticity and uptake)
			* Model trained on urban students (authenticity: 0.62, uptake: 0.60) performed with similar accuracy when tested on non-urban students (authenticity: 0.62, uptake: 0.62)
			* Model trained on non-urban (authenticity: 0.61, uptake: 0.59) performed with similar accuracy when tested on urban students (authenticity: 0.60, uptake: 0.63)

Seiyon at 09:20, 3 February 2022

2022-02-03T09:20:58Z

← Older revision		Revision as of 05:20, 3 February 2022
Line 3:		Line 3:
	* a model that measures L2 learners’ lexical sophistication with the frequency list based on the native speaker corpora		* a model that measures L2 learners’ lexical sophistication with the frequency list based on the native speaker corpora
	* Arabic-speaking learners are rated systematically lower across all levels of English proficiency than speakers of Chinese, Japanese, Korean, and Spanish.		* Arabic-speaking learners are rated systematically lower across all levels of English proficiency than speakers of Chinese, Japanese, Korean, and Spanish.
	* ~~When New General Service List(NGSL) is used on Pitt English Language Institute Corpus(PELIC),~~ Level 5 Arabic-speaking learners are unfairly evaluated to have similar level of lexical sophistication as Level 4 learners from China, Japan, Korean and Spain .		* Level 5 Arabic-speaking learners are unfairly evaluated to have similar level of lexical sophistication as Level 4 learners from China, Japan, Korean and Spain .
	* When used on ETS corpus, “high”-labeled essays by Japanese-speaking learners are ~~rater~~ significantly lower in lexical sophistication than Arabic, Japanese, Korean and Spanish peers.		* When used on ETS corpus, “high”-labeled essays by Japanese-speaking learners are rated significantly lower in lexical sophistication than Arabic, Japanese, Korean and Spanish peers.

Seiyon: Created page with "Naismith et al. (2018) http://d-scholarship.pitt.edu/40665/1/EDM2018_paper_37.pdf pdf * a model that measures L2 learners’ lexical sophistication with the frequency list based on the native speaker corpora * Arabic-speaking learners are rated systematically lower across all levels of English proficiency than speakers of Chinese, Japanese, Korean, and Spanish. * When New General Service List(NGSL) is used on Pitt English Language Institute Corpus(PELIC), Level 5 Ar..."

2022-02-02T21:18:56Z

Created page with "Naismith et al. (2018) http://d-scholarship.pitt.edu/40665/1/EDM2018_paper_37.pdf pdf * a model that measures L2 learners’ lexical sophistication with the frequency list based on the native speaker corpora * Arabic-speaking learners are rated systematically lower across all levels of English proficiency than speakers of Chinese, Japanese, Korean, and Spanish. * When New General Service List(NGSL) is used on Pitt English Language Institute Corpus(PELIC), Level 5 Ar..."

New page

Naismith et al. (2018) [[http://d-scholarship.pitt.edu/40665/1/EDM2018_paper_37.pdf pdf]]

* a model that measures L2 learners’ lexical sophistication with the frequency list based on the native speaker corpora
* Arabic-speaking learners are rated systematically lower across all levels of English proficiency than speakers of Chinese, Japanese, Korean, and Spanish.
* When New General Service List(NGSL) is used on Pitt English Language Institute Corpus(PELIC), Level 5 Arabic-speaking learners are unfairly evaluated to have similar level of lexical sophistication as Level 4 learners from China, Japan, Korean and Spain .
* When used on ETS corpus, “high”-labeled essays by Japanese-speaking learners are rater significantly lower in lexical sophistication than Arabic, Japanese, Korean and Spanish peers.