#Confused and beyond
| Autor: | David R. Karger, Shay A. Geller, Avi Segal, Marc T. Facciotti, Nicholas Hoernle, Kobi Gal, Michele M. Igo, Amy X. Zhang |
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| Rok vydání: | 2020 |
| Předmět: |
05 social sciences
Judgement 050301 education 02 engineering and technology Data science Annotation Resource (project management) Test set Scale (social sciences) ComputingMilieux_COMPUTERSANDEDUCATION 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Disengagement theory Set (psychology) 0503 education Classifier (UML) |
| Zdroj: | LAK |
| Popis: | Students' confusion is a barrier for learning, contributing to loss of motivation and to disengagement with course materials. However, detecting students' confusion in large-scale courses is both time and resource intensive. This paper provides a new approach for confusion detection in online forums that is based on harnessing the power of students' self-reported affective states (reported using a set of pre-defined hashtags). It presents a rule for labeling confusion, based on students' hashtags in their posts, that is shown to align with teachers' judgement. We use this labeling rule to inform the design of an automated classifier for confusion detection for the case when there are no self-reported hashtags present in the test set. We demonstrate this approach in a large scale Biology course using the Nota Bene annotation platform. This work lays the foundation to empower teachers with better support tools for detecting and alleviating confusion in online courses. |
| Databáze: | OpenAIRE |
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