Partially correct predictions on the AgreementSet were mostly Exploration-labelled messages, with Integration more often the alternative predicted label than Triggering event¶

Claim · All claims
Evidence · 1 study · design r2 · q3
Currency · newest study 2023 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q3 i? Among the partially correct predictions of the best-performing multi-label classifier, most messages had the manual label of Exploration, and Integration appeared more often than Triggering event as the alternative label. → Yuanyuan Hu 2023

Evidence¶

Yuanyuan Hu 2023¶

Yuanyuan Hu. (2023). Can Multi-Label Classifiers Help Identify Subjectivity? A Deep Learning Approach to Classifying Cognitive Presence in MOOCs. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-022-00310-5

q3 · i? · design · r2

Qualitative and scatter-plot analysis (Figs. 7-11, Table 8) of the partially correct predictions from the best multi-label classifier with DA. "Most of the partially correct predictions had the manual label of Exploration", and the alternative label was more often Integration than Triggering event.

"Most of the partially correct predictions had the manual label of Exploration (Fig. 9). They had more of the alternatively predicted label of Integration than Triggering event."

Discussion¶

Related Claims¶

• Tested on coder-disagreement messages, multi-label classifiers labelled most messages into two adjacent phases with one predicted label matching the manual label, and partially correct predictions exceeded half of the set — related
• Multi-label classifiers predicted MOOC messages as one label or two adjacent cognitive presence phases only, while expert coders produced non-adjacent and three-phase labels on almost 4% of messages — related