Exploring the Limits of Lexicon-based Natural Language Processing Techniques for Measuring Engagement and Predicting MOOC’s Certification

Esther Félix (1) , Nicolas Hernandez (2) , Issam Rebaï (3, 4)

1 LS2N - Laboratoire des Sciences du Numérique de Nantes
2 LS2N - équipe TALN - Traitement Automatique du Langage Naturel
3 Lab-STICC_MOTEL - Equipe MOTEL
4 IMT Atlantique - INFO - Département Informatique

We address the problem of assessing the contributions of lexicon-based Natural Language Processing (NLP) techniques to measure learner affective and cognitive engagement and thus predict certification in Frenchspeaking MOOCs. Interest in these approaches comes from the fact they are explainable. Our investigation protocol consists of applying machine learning techniques to determine the relationships between lexiconbased engagement indicators and learning outcomes. The lexicon-based approach is compared with trace log features, and we distinguish between specialised linguistically-based approaches with dedicated lexicon resources and more general but deeper text representations. Language quality and its impact on the task are discussed. We investigate this issue in MOOCs imposing or not the use of the forum in their learning activities.

Mots clés: MOOC; Forum Discussions; Engagement; Text Mining; Lexicon-based approach; Learning outcome prediction

Domaines: Informatique et langage [cs.CL]

Esther Félix, Nicolas Hernandez, Issam Rebaï. Exploring the Limits of Lexicon-based Natural Language Processing Techniques for Measuring Engagement and Predicting MOOC’s Certification. CSEDU 2022: 14th International Conference on Computer Supported Education, Apr 2022, Online Streaming, France. pp.95-104, ⟨10.5220/0011085300003182⟩. ⟨hal-03667269⟩