Authors:
Mouna Selmi
1
;
Hicham Hage
2
and
Esma Aïmeur
1
Affiliations:
1
Université de Montréal, Canada
;
2
Notre Dame University, Lebanon
Keyword(s):
e-Learning, Peers’ Interaction, Peer Affective Feedback, Classification, Machine Learning, Natural Language Processing, Sentiment Analysis, Opinion Mining.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Communication, Collaboration and Information Sharing
;
e-Business
;
Education/Learning
;
e-Learning
;
Enterprise Information Systems
;
Human-Computer Interaction
;
Knowledge Management and Information Sharing
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
Peer feedback has become increasingly popular since the advent of social networks, which has significantly changed the process of learning. Some of today’s e-learning systems enable students to communicate with peers (or co-learners) and ask or provide feedback. However, the highly variable nature of peer feedback makes it difficult for a learner who asked for help to notice and benefit from helpful feedback provided by his peers, especially if he is in emotional distress. Helpful feedback in affective context means positive, motivating and encouraging feedback while an unhelpful feedback is negative, bullying and demeaning feedback. In this paper, we propose an approach to predict the helpfulness of a given affective feedback for a learner based on the feedback content and the learner’s affective state. The proposed approach uses natural language processing techniques and machine learning algorithms to classify and predict the helpfulness of peers’ feedback in the context of an Engl
ish learning forum. In order to seek the best accuracy possible, we have used several machine learning algorithms. Our results show that Naïve-Bayes provides the best performance with a prediction accuracy of 87.19%.
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