%0 Conference Paper %1 Leite_2022 %A Leite, Walter L. %A Kuang, Huan %A Shen, Zuchao %A Chakraborty, Nilanjana %A Michailidis, George %A D\textquotesingleMello, Sidney %A Xing, Wanli %B Proceedings of the Ninth ACM Conference on Learning @ Scale %D 2022 %I ACM %K e-learning las2022 recommender video %P 12-23 %R 10.1145/3491140.3528275 %T Heterogeneity of Treatment Effects of a Video Recommendation System for Algebra %U https://doi.org/10.1145%2F3491140.3528275 %X Previous research has shown that providing video recommendations to students in virtual learning environments implemented at scale positively affects student achievement. However, it is also critical to evaluate whether the treatment effects are heterogeneous, and whether they depend on contextual variables such as disadvantaged student status and characteristics of the school settings. The current study extends the evaluation of a novel video recommendation system by performing an exploratory search for sources of heterogeneity of treatment effects. This study's design is a multi-site randomized controlled trial with an assignment at the student level across three large and diverse school districts in the southeast United States. The study occurred in Spring 2021, when some students were in regular classrooms and others in online classrooms. The results of the current study replicate positive effects found in a previous field experiment that occurred in Spring 2020, at the onset of the COVID-19 pandemic. Then, causal forests were used to investigate the heterogeneity of treatment effects. This study contributes to the literature on content sequencing systems and recommendation systems by showing how these systems can disproportionally benefit the groups of students who had higher levels of previous algebra ability, followed more recommendations, learned remotely, were Hispanic, and received free or reduced-price lunch, which has implications for the fairness of implementation of educational technology solutions.

@inproceedings{Leite_2022, abstract = {Previous research has shown that providing video recommendations to students in virtual learning environments implemented at scale positively affects student achievement. However, it is also critical to evaluate whether the treatment effects are heterogeneous, and whether they depend on contextual variables such as disadvantaged student status and characteristics of the school settings. The current study extends the evaluation of a novel video recommendation system by performing an exploratory search for sources of heterogeneity of treatment effects. This study's design is a multi-site randomized controlled trial with an assignment at the student level across three large and diverse school districts in the southeast United States. The study occurred in Spring 2021, when some students were in regular classrooms and others in online classrooms. The results of the current study replicate positive effects found in a previous field experiment that occurred in Spring 2020, at the onset of the COVID-19 pandemic. Then, causal forests were used to investigate the heterogeneity of treatment effects. This study contributes to the literature on content sequencing systems and recommendation systems by showing how these systems can disproportionally benefit the groups of students who had higher levels of previous algebra ability, followed more recommendations, learned remotely, were Hispanic, and received free or reduced-price lunch, which has implications for the fairness of implementation of educational technology solutions.}, added-at = {2022-06-07T00:45:45.000+0200}, author = {Leite, Walter L. and Kuang, Huan and Shen, Zuchao and Chakraborty, Nilanjana and Michailidis, George and D{\textquotesingle}Mello, Sidney and Xing, Wanli}, biburl = {https://www.bibsonomy.org/bibtex/256ec2f2d8ecb165fff500f6c902b7cc2/brusilovsky}, booktitle = {Proceedings of the Ninth {ACM} Conference on Learning @ Scale}, description = {Heterogeneity of Treatment Effects of a Video Recommendation System for Algebra | Proceedings of the Ninth ACM Conference on Learning @ Scale}, doi = {10.1145/3491140.3528275}, interhash = {c807cd0a5a20ee9b0eab0abb6ef1a801}, intrahash = {56ec2f2d8ecb165fff500f6c902b7cc2}, keywords = {e-learning las2022 recommender video}, month = jun, pages = {12-23}, publisher = {{ACM}}, timestamp = {2022-06-07T00:45:45.000+0200}, title = {Heterogeneity of Treatment Effects of a Video Recommendation System for Algebra}, url = {https://doi.org/10.1145%2F3491140.3528275}, year = 2022 }