ICALT '01 Proceedings of the IEEE International Conference on Advanced Learning Technologies
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Automatic detection of learner's affect from conversational cues
User Modeling and User-Adapted Interaction
Early Prediction of Student Frustration
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Can e-Learner's emotion be recognized from interactive Chinese texts?
CSCWD '09 Proceedings of the 2009 13th International Conference on Computer Supported Cooperative Work in Design
SemEval-2007 task 14: affective text
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
On the dynamic adaptation of computer assisted assessment of free-text answers
AH'06 Proceedings of the 4th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Extracting Emotions from Texts in E-Learning Environments
CISIS '12 Proceedings of the 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS)
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Learning diaries are instruments through which students can reflect on their learning experience. Students' sentiments, emotions, opinions and attitudes are embedded in their learning diaries as part of the process of understanding their progress during the course and the self-awareness of their goals. Learning diaries are also a very informative feedback source for instructors regarding the students' emotional well-being. However the number of diaries created during a course can become a daunting task to be manually analyzed with care, particularly when the class is large. To tackle this problem, in this paper we present a functional system for analyzing and visualizing student emotions expressed in learning diaries. The system allows instructors to automatically extract emotions and the changes in these emotions throughout students' learning experience as expressed in their diaries. The emotions extracted by the system are based on Plutchik's eight emotion categories, and they are shown over the time period that the diaries were written. The potential impact and usefulness of our system are highlighted during our experiments with promising results for improving the communication between instructors and students and enhancing the learning experience.