Discipline-general academic vocabulary (AWL use) supports cognitive engagement identification and may aid generalization across courses¶

Claim · All claims
Evidence · 1 study · design r2 · q2
Currency · newest study 2022 · not yet checked against its sources · not reviewed by a person · overdue for a recheck since 2025: tool and model accuracy changes quickly

Subclaims¶

q2 i? The authors report that use of academic vocabulary from the AWL, which is not discipline-specific, supports identification of cognitive engagement and should be included in future models. → Gorgun 2022

Evidence¶

Gorgun 2022¶

Gorgun, G., Yildirim-Erbasli, S. N., & Demmans Epp, C. (2022). Predicting cognitive engagement in online course discussion forums. Proceedings of the 15th International Conference on Educational Data Mining. https://doi.org/10.5281/zenodo.6853149

q2 · i? · design · r2

Authors' interpretation in the discussion of their feature importance results, building on prior studies that captured vocabulary through other means (e.g., LIWC). The AWL count feature was not included in those earlier studies.

"Our findings indicate that the use of academic vocabulary, like those in the AWL, that are not specific to a discipline (words such as hypothesis, conclude) support the identification of cognitive engagement. Moreover, their more general nature means that they should support generalization across courses at similar academic levels."

Discussion¶

Learner Variables¶

• Vocabulary — predictor: learners who differ on it differ in outcomes

Related Claims¶

• Academic word list count, word count, and Flesch-Kincaid grade level are the most important features for predicting cognitive engagement in discussion posts — related
• All three trained classifiers outperformed the zero-rule baseline (28.4% accuracy) for classifying cognitive engagement in discussion posts
• A support vector machine classifier outperformed decision tree and random forest models in predicting cognitive engagement levels of online discussion posts
• Feature-based model predictive power derives from a large and varied set of features rather than a few dominant variables — related