Abstract
Traditionally, systems supporting blended learning focus only on one portion of the course by tracing students’ interaction with learning content at home. In this paper, we argue that in-class activity can be also instrumental in eliciting the true state of students’ knowledge and can lead to more accurate models of their performance. Quizitor is an online platform that delivers both the at-home and the in-class assessment. We show that a combination of the two streams of data that Quizitor collects from students can help build more accurate models of students’ mastery that help predict their course performance better than models separately trained on either of these two types of activity.
The research presented in this paper is partially supported by Universitas Islam Indonesia under Doctoral Grant for Lecturer 2019 (grant no 1296).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Herder, E., Sosnovsky, S., Dimitrova, V.: Adaptive intelligent learning environments. In: Duval, E., Sharples, M., Sutherland, R. (eds.) Technology Enhanced Learning, pp. 109–114. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-02600-8_10
Koedinger, K.R., Anderson, J.R., Hadley, W.H., Mark, M.A., et al.: Intelligent tutoring goes to school in the big city. Int. J. Artif. Intell. Educ. 8(1), 30–43 (1997)
Brusilovsky, P., Sosnovsky, S., Yudelson, M.: Addictive links: the motivational value of adaptive link annotation. New Rev. Hypermedia Multimed. 15(1), 97–118 (2009)
Rasheed, R.A., Kamsin, A., Abdullah, N.A.: Challenges in the online component of blended learning: a systematic review. Comput. Educ. 144, 103701 (2020)
Howard, L., Remenyi, Z., Pap, G.: Adaptive blended learning environments. In: International Conference on Engineering Education, pp. 23–28 (2006)
Gynther, K.: Design framework for an adaptive MOOC enhanced by blended learning: supplementary training and personalized learning for teacher professional development. Electron. J. e-Learning 14(1), 15–30 (2016)
Yudelson, M.: Elo, i love you won’t you tell me your K. In: Scheffel, M., Broisin, J., Pammer-Schindler, V., Ioannou, A., Schneider, J. (eds.) EC-TEL 2019. LNCS, vol. 11722, pp. 213–223. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29736-7_16
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Sosnovsky, S., Hamzah, A. (2022). Improving Prediction of Student Performance in a Blended Course. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2022. Lecture Notes in Computer Science, vol 13355. Springer, Cham. https://doi.org/10.1007/978-3-031-11644-5_54
Download citation
DOI: https://doi.org/10.1007/978-3-031-11644-5_54
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-11643-8
Online ISBN: 978-3-031-11644-5
eBook Packages: Computer ScienceComputer Science (R0)