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New journal publication: Knowledge-based Design Analytics for Authoring Courses with Smart Learning Content

New journal publication: Knowledge-based Design Analytics for Authoring Courses with Smart Learning Content

Design analytics and smart learning content en edCrumble

08.06.2021

 

Laia Albó visited the University of Pittsburgh for 3 months in 2019 (see related news). As a result of the collaboration during the research stay, the following journal article has been achieved and recently published in the International Journal of Artificial Intelligence in Education. Moreover, the article was invited to be presented online at the 22nd International Conference on Artificial Intelligence in Education (AIED conference) last June 17 (slides accessible here). 

Albó, L., Barria-Pineda, J., Brusilovsky, P., & Hernández-Leo, D. (2021). Knowledge-Based Design Analytics for Authoring Courses with Smart Learning Content. International Journal of Artificial Intelligence in Education, 1-24. https://doi.org/10.1007/s40593-021-00253-3 

 

Abstract: Over the last 10 years, learning analytics have provided educators with both dashboards and tools to understand student behaviors within specific technological environments. However, there is a lack of work to support educators in making data-informed design decisions when designing a blended course and planning appropriate learning activities. In this paper, we introduce knowledge-based design analytics that uncover facets of the learning activities that are being created. A knowledge-based visualization is integrated into edCrumble, a (blended) learning design authoring tool. This new approach is explored in the context of a higher education programming course, where instructors design labs and home practice sessions with online smart learning content on a weekly basis. We performed a within-subjects user study to compare the use of the design tool both with and without visualization. We studied the differences in terms of cognitive load, controllability, confidence and ease of choice, design outcomes, and user actions within the system to compare both conditions with the objective of evaluating the impact of using design analytics during the decision-making phase of course design. Our results indicate that the use of a knowledge-based visualization allows the teachers to reduce the cognitive load (especially in terms of mental demand) and that it facilitates the choice of the most appropriate activities without affecting the overall design time. In conclusion, the use of knowledge-based design analytics improves the overall learning design quality and helps teachers avoid committing design errors.

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