Other NLP Applications of Algorithms in Education

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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.