Implementing and Evaluating ASSISTments Online Math Homework Support At large Scale over Two Years: Findings and Lessons Learned

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Abstract

Math performance continues to be an important focus for improvement. The most recent National Report Card in the U.S. suggested student math scores declined in the past two years possibly due to COVID-19 pandemic and related school closures. We report on the implementation of a math homework program that leverages AI-based one-to-one technology, in 32 schools for two years as a part of a randomized controlled trial in diverse settings of the state of North Carolina in the US. The program, called “ASSISTments,” provides feedback to students as they solve homework problems and automatically prepares reports for teachers about student performance on daily assignments. The paper describes the sample, the study design, the implementation of the intervention, including the recruitment effort, the training and support provided to teachers, and the approaches taken to assess teacher’s progress and improve implementation fidelity. Analysis of data collected during the study suggest that (a) treatment teachers changed their homework review practices as they used ASSISTments, and (b) the usage of ASSISTments was positively correlated with student learning outcome.

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Feng, M., Heffernan, N., Collins, K., Heffernan, C., & Murphy, R. F. (2023). Implementing and Evaluating ASSISTments Online Math Homework Support At large Scale over Two Years: Findings and Lessons Learned. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13916 LNAI, pp. 28–40). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-36272-9_3

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