With hybrid teaching and learning becoming the new educational reality, teachers often face the challenge of searching in open repositories that vary substantially in quality and standards, in order to find suitable learning materials that fit their students’ needs and their own pedagogical preferences. Social recommendations, i.e. recommendations from fellow teachers about learning resources, are becoming a popular feature in Open Educational Resources (OER) repositories. However, very little is known about their value for teachers, namely, whether teachers actually rely on them for choosing learning resources. To address this gap, we studied the behaviour of science teachers who are using a nation-wide OER system with social-based recommendation features, in which teachers can share experiences and feedback on learning resources with the community. Our work helps to establish a reliable causal link between recommendations and use. This is done by adding a time-series quantitative analysis on the impact of the social recommendations on teachers’ instructional choices.
|Title of host publication
|Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium
|Number of pages
|Published - 26 Jul 2022
|Lecture Notes in Computer Science