 |
[email protected] |
 |
3275638434 |
 |
 |
| Paper Publishing WeChat |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
ZHANG Chao, SHI Qing, TONG Mingwen
Full-Text PDF
XML 276 Views
DOI:10.17265/2159-5542/2023.05.002
Central China Normal University, Wuhan, China
As the field of artificial intelligence continues to evolve, so too does the application of multimodal learning analysis and intelligent adaptive learning systems. This trend has the potential to promote the equalization of educational resources, the intellectualization of educational methods, and the modernization of educational reform, among other benefits. This study proposes a construction framework for an intelligent adaptive learning system that is supported by multimodal data. It provides a detailed explanation of the system’s working principles and patterns, which aim to enhance learners’ online engagement in behavior, emotion, and cognition. The study seeks to address the issue of intelligent adaptive learning systems diagnosing learners’ learning behavior based solely on learning achievement, to improve learners’ online engagement, enable them to master more required knowledge, and ultimately achieve better learning outcomes.
multimodal, intelligent adaptive learning system, online learning engagement
Psychology Research, May 2023, Vol. 13, No. 5, 202-206
Atapattu, T., Thilakaratne, M., Vivian, R., & Falkner, K. (2019). Detecting cognitive engagement using word embeddings within an online teacher professional development community. Computers & Education, 140, 103594. Retrieved from https://doi.org/10.1016/j.compedu.2019.05.020
Bahreini, K., Nadolski, R., & Westera, W. (2016). Towards multimodal emotion recognition in e-learning environments. Interactive Learning Environments, 24(3), 590-605. Retrieved from https://doi.org/10.1080/10494820.2014.908927
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. Retrieved from https://doi.org/10.18608/jla.2016.32.11
Carroll, A., Gillies, R. M., Cunnington, R., McCarthy, M., Sherwell, C., Palghat, K., … Seary, T. (2019). Changes in science attitudes, beliefs, knowledge and physiological arousal after implementation of a multimodal, cooperative intervention in primary school science classes. Information and Learning Sciences, 120(7/8), 409-425. Retrieved from https://doi.org/10.1108/ILS-08-2018-0089
Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59-109. Retrieved from https://doi.org/10.3102/00346543074001059
Henrie, C. R., Halverson, L. R., & Graham, C. R. (2015). Measuring student engagement in technology-mediated learning: A review. Computers & Education, 90, 36-53. Retrieved from https://doi.org/10.1016/j.compedu.2015.09.005
Liu, B., Xing, W., Zeng, Y., & Wu, Y. (2021). Quantifying the influence of achievement emotions for student learning in MOOCs. Journal of Educational Computing Research, 59(3), 429-452. Retrieved from https://doi.org/10.1177/0735633120967318
Mou, Z. J. (2020). Multimodal learning analytics: A new growth point for learning analytics research. Research in Electro-Chemical Education, 41(5), 27-32+51. Retrieved from https://doi.org/10.13811/j.cnki.eer.2020.05.004
Vanbecelaere, S., Van den Berghe, K., Cornillie, F., Sasanguie, D., Reynvoet, B., & Depaepe, F. (2020). The effectiveness of adaptive versus non‐adaptive learning with digital educational games. Journal of Computer Assisted Learning, 36(4), 502-513. Retrieved from https://doi.org/10.1111/jcal.12416
Yang, Y. T. C., Gamble, J. H., Hung, Y. W., & Lin, T. Y. (2014). An online adaptive learning environment for critical‐thinking‐infused English literacy instruction. British Journal of Educational Technology, 45(4), 723-747. Retrieved from https://doi.org/10.1111/bjet.12080
Zhou, Q., & Wen, X. Y. (2020). From adaptive to intelligent adaptation: A new path of personalized learning in the era of artificial intelligence. Modern Educational Management, 40(9), 89-96. Retrieved from https://doi.org/10.16697/j.1674-5485.2020.09.012
Zou, W., Hu, X., Pan, Z., Li, C., Cai, Y., & Liu, M. (2021). Exploring the relationship between social presence and learners’ prestige in MOOC discussion forums using automated content analysis and social network analysis. Computers in Human Behavior, 115, 106582. Retrieved from https://doi.org/10.1016/j.chb.2020.106582