Deeper Learning Effectiveness and Variation on Mathematic Achievement

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Abstract

Deeper Learning Effectiveness and Variation on Mathematic Achievement

This study has obvious value on program evaluation and also implementation and data collection to policymakers and practitioners who want to know not only the inference of overall deeper learning (DL) effects across individual students but also its heterogeneity across different school sites. Providing evidence of average DL program effects may not tell the whole story about DL program effectiveness. Investigating the heterogeneity of the effectiveness is crucial. The study adopts quasi-experimental analysis of propensity score matching method to facilitate causal inference of the DL “treatment” effects via reducing systematic differences between students who were in network schools versus their counterparts in comparison schools. The poster presented here focuses on mathematics achievement. Findings of this study suggest that although students in network schools had higher average mathematics scores than students in non-network schools, overall, the differences were not significant, except for the student cohort in 2009-2010 and students in California.

Author

• Ting Shen, Missouri University of Science & Technology