Other NLP Applications of Algorithms in Education

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.