Decision-Based Learning: ″If I Just Knew Which Equation To Use, I Know I Could Solve This Problem!″

Heat and enthalpy are challenging topics for general chemistry students because they are conceptually complex and require avariety of quantitative problem-solving approaches. Expert chemists draw on conditional knowledge, deciding when and under what conditions a certain problem-solving approach should be used. Decision-based learning (DBL) explicitly teaches conditional knowledge, helping students decide the most appropriate strategy for each type of problem as they learn key concepts. In this study, we created a DBL model for heat and enthalpy problems and introduced it to students enrolled in first-semester general chemistry. We compared performance on test items related to heat and enthalpy with students not using the DBL model, from the previous cohort. Students engaging with the model outside of class, using it to work through five to ten problems related to heat and enthalpy, performed statistically significantly better than students in the comparison group. Students only using the model in class or using the model for twenty or more problems outside of class did not improve relative to the comparison group. These results suggest that, when used appropriately, DBL models improve student achievement in general chemistry. We propose that further research about the efficacy of DBL as an instructional strategy be conducted and DBL models for other difficult topics in general chemistry be developed.