Journal of The Korean Association of Information Education (정보교육학회논문지)

Korea Association of Information Education (한국정보교육학회)
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Research Trend of Multimodal Learning Analysis: Focuss on online Learning

다중양식 학습분석 연구동향 분석: 온라인 학습을 중심으로

  • Received : 2022.11.30
  • Accepted : 2023.01.27
  • Published : 2023.02.28
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Abstract

This study analyzed research trends on multimodal learning analysis of online learning activities of learners over the past ten years. Specifically, based on 5W1H, the research period, research environment, research subjects, researchers, multi-modality, research content, and analysis method are identified to obtain implications for multi-modality learning analysis. For this purpose, 29 empirical studies were analyzed from 2013 to 2022 at domestic and overseas, excluding literature studies. As a result of the analysis, research on multimodal learning analysis is increasing, and it is primarily conducted by engineering researchers with a focus on asynchronous online learning. The research subjects were mainly university students, and the study used multi-modal data (eye tracking, emotion, EEG, body movement, and questionnaires) to measure learners' attention or cognitive load. It also notes that the study confirms a trend in research on multimodal learning analysis in the context of online learning and suggests implications for future research in this area.

본 연구는 최근 10년간 학습자의 온라인 학습을 다중양식을 활용하여 학습분석한 연구동향을 분석하였다. 구체적으로는 5W1H을 기준으로 연구시기, 연구환경, 연구대상자, 연구자, 다중양식, 연구내용, 분석방법을 파악하고, 다중양식 학습분석의 시사점을 얻고자 한다. 이를 위해 2013-2022년에 문헌연구를 제외한 국내외 실증연구 29편을분석하였다. 분석결과, 다중양식 학습분석 연구는 지속해서 증가하고 있으며, 비실시간 온라인학습 배경에서, 대부분 공학계열 연구자들을 중심으로 이루어지고 있었다. 실험대상은 주로 대학생들이었고, 학습자의 주의집중이나 인지부하를 측정하기 위해 다중양식데이터(시선추적, 감정, 뇌파, 신체움직임, 설문지 등)를 사용했다. 본 연구는 온라인 학습에서 다중양식 학습분석의 연구동향을 확인하였으며, 이 결과를 바탕으로 향후 이루어질 다중양식 학습분석에서의 시사점을 제안하였다.

Keywords

Acknowledgement

이 성과는 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(No. NRF-2022R1F1A1065295).

References

  1. Ham, Y., Cho, Y., Lee, H., & Kim, H. (2021), Systematic literature review to explore research trends and future directions of multimodal learning analytics, The Journal of Educational Information and Media, 27(2), 501-529.
  2. Andrade, A., Delandshere, G., & Danish, J. A. (2016), Using multimodal learning analytics to model student behavior: A systematic analysis of epistemological framing, Journal of Learning Analytics, 3(2), 282-306. https://doi.org/10.18608/jla.2016.32.14
  3. Johnson, L., Adams, S., & Cummins, M. (2012), Technology outlook for Australian tertiary education 2012-2017: An NMC Horizon Report regional analysis. TA: The New Media Consortium.
  4. Lockyer, L., Heathcote, E., & Dawson, S. (2013), Informing pedagogical action: Aligning learning analytics with learning design, American Behavioral Scientist, 57(10), 1439-1459. https://doi.org/10.1177/0002764213479367
  5. Jo. I. (2012), Propose LAPA (Learning Analytics for Prediction & Action) model. Seoul: The Knowledge Management Society of Korea.
  6. Blikstein, P., & Worsley, M. (2016), Multimodal learning analytics and education data mining: Using computational technologies to measure complex learning tasks, Journal of Learning Analytics, 3(2), 220-238. https://doi.org/10.18608/jla.2016.32.11
  7. Scherer, S., Worsley, M., & Morency, L. P. (2012), 1st international workshop on multimodal learning analytics, In Proceedings of the 14th ACM international conference on Multimodal interaction (pp. 609-610).
  8. Worsley, M., Martinez-Maldonado, R., & D'Angelo, C. (2021), A new era in multimodal learning analytics: twelve core commitments to ground and grow MMLA, Journal of Learning Analytics, 8(3), 10-27. https://doi.org/10.18608/jla.2021.7361
  9. Lee, H., Cho, Y., Lee, H., & Ham, Y. (2020), Elementary school teachers' perception on ethical issues and strategies of learning analytics, The Journal of Educational Information and Media, 26(1), 157-181.
  10. Alwahaby, H., Cukurova, M., Papamitsiou, Z., &Giannakos, M. (2022), The evidence of impact and ethical considerations of multimodal learning analytics: A systematic literature review, The Multimodal Learning Analytics Handbook, 289-325.
  11. Sung, H., & Jo, I.. (2018), Utilizing multimodal data to predict learning achievement: Behavioral log, psychysiological response, and test anxiety. Journal of Educational Technology, 34(2), 287-308. https://doi.org/10.17232/KSET.34.2.287
  12. Jo, I, Ha, K., & Park, Y. (2015), Measuring information perception in learning analytics dashboard: Use of eye-tracking system, The Journal of Educational Information and Media, 21(3), 441-469.
  13. Luo, Z., Jingying, C., Guangshuai, W., & Mengyi, L. (2022), A three-dimensional model of student interest during learning using multimodal fusion with natural sensing technology, Interactive Learning Environments, 30(6), 1117-1130.
  14. Herbig, N., Duwel, T., Helali, M., Eckhart, L., Schuck, P., Choudhury, S., & Kruger, A. (2020), Investigating multi-modal measures for cognitive load detection in e-learning, ACM Conference on User Modeling, Adaptation and Personalization, 28, 88-97.
  15. Lee., S & Byun, H. (2021), Analysis of learners' cognitive learning activities using brain waves. Journal of Educational Technology, 37(3), 649-679. https://doi.org/10.17232/KSET.37.3.649
  16. Noroozi, O., Pijeira-Diaz, H. J., Sobocinski, M., Dindar, M., Jarvela, S., & Kirschner, P. A. (2020), Multimodal data indicators for capturing cognitive, motivational, and emotional learning processes: A systematic literature review, Education and Information Technologies, 25(6), 5499-5547. https://doi.org/10.1007/s10639-020-10229-w
  17. Pei, B., Xing, W., & Wang, M. (2021), Academic development of multimodal learning analytics: a bibliometric analysis, Interactive Learning Environments, DOI: 10.1080/10494820.2021.1936075
  18. Choi, S., Kim, M., & Kim, D. (2022), Systematic literature review on domestic educational research using EEG, Journal of Digital Contents Society, 23(2), 217-225. https://doi.org/10.9728/dcs.2022.23.2.217
  19. Song, J. & Shin, S. (2022), A systematic review of educational research using eye-tracking data: based on the cognitive process framework, Journal of Educational Technology, 38(1), 109-148. https://doi.org/10.17232/KSET.38.1.109
  20. Lin, J., & Sekiguchi, T. (2020), E-learning in entrepreneurship education: A systematic literature review, 2020 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, 83-90. DOI: 10.1109/TALE48869.2020.9368412
  21. Paliwal, V., Chandra, S., & Sharma, S. (2020), Blockchain technology for sustainable supply chain management: A systematic literature review and a classification framework, Sustainability, 12(18), 7638.
  22. Kim, J., Lee, C., Song, H., & Kwon, S. (2022), Real-time online study and exam attitude dataset design and implementation, Jurnal of Broadcast Engineering, 27(1), 124-132.
  23. Kwon, H. & Lee, S. (2021), Exploring factors affecting learning motivation of elementary school students in the online class, Journal of Korean Association for Educational Information and Media, 27(3), 979-1006.
  24. Lim, S, Yang, I, & Kim, S. (2021), A survey on the perception of elementary school field education in the context of COVID-19 based on the teaching, The Journal of Learner-Centered Curriculum and Instruction, 21(2), 371-400.
  25. Noh, Y. & Kim, J. (2022), A study on the status of industrial support and research performance utilization of humanities and social sciences-based convergence researchers, The Journal of Humanities and Social science, 13(2), 779-792.
  26. Park, H. & Jo, I. (2021), Comparison of learner's perceived difficulty and pupil response in computer-based problem solving, The Journal of Korean Association of Computer Education, 24(1), 97-105.