TY - GEN
T1 - Assisting Teachers in Finding Online Learning Resources
T2 - 23rd International Conference on Artificial Intelligence in Education, AIED 2022
AU - Toda, Armando M.
AU - Cristea, Alexandra I.
AU - Alexandron, Giora
N1 - Publisher Copyright: © 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85135929304&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-11647-6_77
DO - 10.1007/978-3-031-11647-6_77
M3 - منشور من مؤتمر
SN - 9783031116469
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 391
EP - 395
BT - Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium - 23rd International Conference, AIED 2022, Proceedings
A2 - Rodrigo, Maria Mercedes
A2 - Matsuda, Noburu
A2 - Cristea, Alexandra I.
A2 - Dimitrova, Vania
PB - Springer Science and Business Media B.V.
Y2 - 27 July 2022 through 31 July 2022
ER -