Publication: Towards Self-Regulated Individual Learning Path Generation Using Outcome Taxonomies and Constructive Alignment
Issued Date
2021-01-01
Resource Type
Other identifier(s)
2-s2.0-85125944045
Rights
Mahidol University
Rights Holder(s)
SCOPUS
Bibliographic Citation
TALE 2021 - IEEE International Conference on Engineering, Technology and Education, Proceedings. (2021), 465-472
Suggested Citation
Phat Nguyen Huu, Preecha Tangworakitthaworn, Lester Gilbert Towards Self-Regulated Individual Learning Path Generation Using Outcome Taxonomies and Constructive Alignment. TALE 2021 - IEEE International Conference on Engineering, Technology and Education, Proceedings. (2021), 465-472. doi:10.1109/TALE52509.2021.9678777 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/76699
Research Projects
Organizational Units
Authors
Journal Issue
Thesis
Title
Towards Self-Regulated Individual Learning Path Generation Using Outcome Taxonomies and Constructive Alignment
Other Contributor(s)
Abstract
Self-regulated individual learning is widely used in academia. Besides the model's advantages, such as flexible learning in time and space, some implementations have limitations, for example fixed learning paths, and unclear relationships between learning activities and intended learning outcomes. This paper introduces an individualized learning model based on Bloom's cognitive taxonomy and Biggs' Principle of Constructive Alignment (PCA). The model provides individual tailored learning paths, adjusted for different background knowledge and ability to learn, based on regularly measured achievement of the intended learning outcomes.