research-article

Exploiting sentiment analysis to track emotions in students' learning diaries

Authors:

Myriam Munezero,

Calkin Suero Montero,

Maxim Mozgovoy,

Erkki SutinenAuthors Info & Claims

Koli Calling '13: Proceedings of the 13th Koli Calling International Conference on Computing Education Research

Pages 145 - 152

https://doi.org/10.1145/2526968.2526984

Published: 14 November 2013 Publication History

Abstract

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.

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Cited By

Li JHe CHu JJia BHalevy AMa X(2024)DiaryHelper: Exploring the Use of an Automatic Contextual Information Recording Agent for Elicitation Diary StudyProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642853(1-16)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642853
Ahmad KIqbal WEl-Hassan AQadir JBenhaddou DAyyash MAl-Fuqaha A(2024)Data-Driven Artificial Intelligence in Education: A Comprehensive ReviewIEEE Transactions on Learning Technologies10.1109/TLT.2023.331461017(12-31)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TLT.2023.3314610
Anderson TSarkar S(2024)From Words to Action: Sentiment Analysis on Sustainability InitiativesSoutheastCon 202410.1109/SoutheastCon52093.2024.10500089(269-274)Online publication date: 15-Mar-2024
https://doi.org/10.1109/SoutheastCon52093.2024.10500089
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Index Terms

Exploiting sentiment analysis to track emotions in students' learning diaries
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources

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Koli Calling '13: Proceedings of the 13th Koli Calling International Conference on Computing Education Research

November 2013

204 pages

ISBN:9781450324823

DOI:10.1145/2526968

Conference Chairs:
Mikko-Jussi Laakso
University of Turku, Finland
,
Simon
University of Newcastle, Australia

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Univ. Eastern Finland: University of Eastern Finland
The University of Newcastle, Australia
Turku University Foundation

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SIGCSE: ACM Special Interest Group on Computer Science Education

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Association for Computing Machinery

New York, NY, United States

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Published: 14 November 2013

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Univ. Eastern Finland

Koli Calling '13: 13th Koli Calling International Conference on Computing Education Research

November 14 - 17, 2013

Koli, Finland

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Koli Calling '13 Paper Acceptance Rate 20 of 40 submissions, 50%;

Overall Acceptance Rate 80 of 182 submissions, 44%

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Cited By

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https://dl.acm.org/doi/10.1145/3613904.3642853
Ahmad KIqbal WEl-Hassan AQadir JBenhaddou DAyyash MAl-Fuqaha A(2024)Data-Driven Artificial Intelligence in Education: A Comprehensive ReviewIEEE Transactions on Learning Technologies10.1109/TLT.2023.331461017(12-31)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TLT.2023.3314610
Anderson TSarkar S(2024)From Words to Action: Sentiment Analysis on Sustainability InitiativesSoutheastCon 202410.1109/SoutheastCon52093.2024.10500089(269-274)Online publication date: 15-Mar-2024
https://doi.org/10.1109/SoutheastCon52093.2024.10500089
Tu CPeterson PYen CHarati HShelton CSujo-Montes L(2024)Using data-informed learning design to support teacher to understand students’ learning sentiment via journal entriesEducational Media International10.1080/09523987.2023.232458360:3-4(169-182)Online publication date: 13-Mar-2024
https://doi.org/10.1080/09523987.2023.2324583
Anderson TSarkar SKelley R(2024)Analyzing public sentiment on sustainability: A comprehensive review and application of sentiment analysis techniquesNatural Language Processing Journal10.1016/j.nlp.2024.1000978(100097)Online publication date: Sep-2024
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Lin YNixon N(2024)Transitioning to Online Instructions and COVID-19 Response: A View from Mining Emergent College Students Discourse in Online Discussion ForumInternational Journal of Artificial Intelligence in Education10.1007/s40593-024-00411-3Online publication date: 25-Jun-2024
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Steinhaeusser SZehe ASchnetter PHotho ALugrin B(2024)Towards the development of an automated robotic storyteller: comparing approaches for emotional story annotation for non-verbal expression via body languageJournal on Multimodal User Interfaces10.1007/s12193-024-00429-wOnline publication date: 4-Apr-2024
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Tan LTan OSze CGoh W(2023)Emotional Variance Analysis: A new sentiment analysis feature set for Artificial Intelligence and Machine Learning applicationsPLOS ONE10.1371/journal.pone.027429918:1(e0274299)Online publication date: 12-Jan-2023
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Omran TSharef BGrosan CLi Y(2023)The Impact of Data Augmentation on Sentiment Analysis of Translated Textual Data2023 International Conference on IT Innovation and Knowledge Discovery (ITIKD)10.1109/ITIKD56332.2023.10099851(1-4)Online publication date: 8-Mar-2023
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Omran TSharef BGrosan CLi Y(2023)Transfer learning and sentiment analysis of Bahraini dialects sequential text data using multilingual deep learning approachData & Knowledge Engineering10.1016/j.datak.2022.102106143(102106)Online publication date: Jan-2023
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