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Semantic Analysis of Learners’ Emotional Tendencies on Online MOOC Education

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  • Ling Wang

    (Department of Computer Technology, School of Information Engineering, Northeast Electric Power University, Jilin 132000, China)

  • Gongliang Hu

    (Department of Computer Technology, School of Information Engineering, Northeast Electric Power University, Jilin 132000, China)

  • Tiehua Zhou

    (Department of Computer Technology, School of Information Engineering, Northeast Electric Power University, Jilin 132000, China)

Abstract

As a new education product in the information age, Massive Open Online Courses (MOOCs) command momentous public attention for their unexpected rise and flexible application. However, the striking contrast between the high rate of registration and the low rate of completion has put their development into a bottleneck. In this paper, we present a semantic analysis model (SMA) to track the emotional tendencies of learners in order to analyze the acceptance of the courses based on big data from homework completion, comments, forums and other real-time update information on the MOOC platforms. Through emotional quantification and machine learning calculations, graduation probability can be predicted for different stages of learning in real time. Especially for learners with emotional tendencies, customized instruction could be made in order to improve completion and graduation rates. Furthermore, we classified the learners into four categories according to course participation time series and emotional states. In the experiments, we made a comprehensive evaluation of the students’ overall learning status by kinds of learners and emotional tendencies. Our proposed method can effectively recognize learners’ emotional tendencies by semantic analysis, providing an effective solution for MOOC personalized teaching, which can help achieve education for sustainable development.

Suggested Citation

  • Ling Wang & Gongliang Hu & Tiehua Zhou, 2018. "Semantic Analysis of Learners’ Emotional Tendencies on Online MOOC Education," Sustainability, MDPI, vol. 10(6), pages 1-19, June.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:6:p:1921-:d:151394
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    References listed on IDEAS

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    1. Sunyoung Kim & Taejung Park, 2022. "Analyzing Log Data of Students Who Have Achieved Scores Adjacent to the Minimum Passing Grade for a K-MOOC Completion in the Context of Learning Analytics," Sustainability, MDPI, vol. 14(18), pages 1-14, September.
    2. Youzhu Li & Xianghui Gao & Mingying Du & Rui He & Shanshan Yang & Jason Xiong, 2020. "What Causes Different Sentiment Classification on Social Network Services? Evidence from Weibo with Genetically Modified Food in China," Sustainability, MDPI, vol. 12(4), pages 1-15, February.
    3. Aleksandra Kobicheva & Elena Tokareva & Tatiana Baranova, 2022. "Students’ Affective Learning Outcomes and Academic Performance in the Blended Environment at University: Comparative Study," Sustainability, MDPI, vol. 14(18), pages 1-14, September.
    4. Wei Wei & Yi Yu & Ge Gao, 2022. "Investigating Learners’ Changing Expectations on Learning Experience in a MOOC of Professional Translation and Interpreter Training," SAGE Open, , vol. 12(4), pages 21582440221, November.
    5. Tie Hua Zhou & Gong Liang Hu & Ling Wang, 2019. "Psychological Disorder Identifying Method Based on Emotion Perception over Social Networks," IJERPH, MDPI, vol. 16(6), pages 1-17, March.
    6. Lifen Bai & Binbin Yang & Shichong Yuan, 2023. "Evaluating of Education Effects of Online Learning for Local University Students in China: A Case Study," Sustainability, MDPI, vol. 15(13), pages 1-13, June.
    7. Serdar Türkeli & Martine Schophuizen, 2019. "Decomposing the Complexity of Value: Integration of Digital Transformation of Education with Circular Economy Transition," Social Sciences, MDPI, vol. 8(8), pages 1-22, August.
    8. Ishteyaaq Ahmad & Sonal Sharma & Rajesh Singh & Anita Gehlot & Neeraj Priyadarshi & Bhekisipho Twala, 2022. "MOOC 5.0: A Roadmap to the Future of Learning," Sustainability, MDPI, vol. 14(18), pages 1-17, September.
    9. Thomas Dolmark & Osama Sohaib & Ghassan Beydoun & Kai Wu, 2021. "The Effect of Individual’s Technological Belief and Usage on Their Absorptive Capacity towards Their Learning Behaviour in Learning Environment," Sustainability, MDPI, vol. 13(2), pages 1-17, January.

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