과제정보
이 성과는 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(No. NRF-2022R1F1A1065295).
참고문헌
- 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.
- 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
- 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.
- 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
- Jo. I. (2012), Propose LAPA (Learning Analytics for Prediction & Action) model. Seoul: The Knowledge Management Society of Korea.
- 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
- 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).
- 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
- 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.
- 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.
- 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
- 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.
- 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.
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.