This dissertation focuses on three manuscripts all related to bolstering science achievement through recommendations from the National Research Council (NRC) regarding the teaching and learning of science. The manuscripts address meeting the NRC’s call to incorporate curriculum and assessment that lead to more in-depth knowledge that can be transferred across domains, and the use of technology to facilitate teaching and learning. The first manuscript, “Improving Science Achievement – Is It Possible? Evaluating the Efficacy of a High School Chemistry and Physics Project-Based Learning Intervention,” describes the process of developing intensive project-based curriculum and assessment materials for high school chemistry and physics classrooms. This study answers the question of what impact that curriculum has on students’ future science achievement and academic ambition. The second manuscript, “Using Machine Learning to Score Multi-Dimensional Assessments of Chemistry and Physics,” focuses on the use of a supervised machine learning approach to facilitate the scoring of science assessments. The goal of this study was to determine whether automating the process of classifying these responses could reduce the burden placed on teachers in scoring assessments that effectively measure the dimensions of learning spelled out by the NRC. The third study, “U.S. and Finnish high school engagement during the Covid-19 Pandemic,” then explores student engagement with the use of technologies that facilitate remote instruction.
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