Difference between revisions of "Other NLP Applications of Algorithms in Education"

Latest revision as of 21:10, 28 June 2023

Naismith et al. (2018) 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.
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.

Samei et al. (2015) 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)

Sha et al. (2021) 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

Sha et al. (2022) [1]

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.

Zhang et al.(2023) pdf

Models developed to detect attributes of student feedback for other students’ mathematics solutions, reflecting the presence of three constructs:1) commenting on the process, 2) commenting on the answer, and 3) relating to self.
Models have approximately equal performance for males and females and for African American, Hispanic/Latinx, and White students.

@@ Line 17: / Line 17: @@
 * 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,
+* 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
 * 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.
+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 the process, 2) commenting on the answer, and 3) relating to self.
+* Models have approximately equal performance for males and females and for African American, Hispanic/Latinx, and White students.