Is Peer Interaction Necessary for Optimal Active Learning?

Meta-analyses of active-learning research consistently show that active-learning techniques result in greater student performance than traditional lecture-based courses. However, some individual studies show no effect of active-learning interventions. This may be due to inexperienced implementation of active learning. To minimize the effect of inexperience, we should try to provide more explicit implementation recommendations based on research into the key components of effective active learning. We investigated the optimal implementation of active-learning exercises within a “lecture” course. Two sections of nonmajors biology were taught by the same instructor, in the same semester, using the same instructional materials and assessments. Students in one section completed in-class active-learning exercises in cooperative groups, while students in the other section completed the same activities individually. Performance on low-level, multiple-choice assessments was not significantly different between sections. However, students who worked in cooperative groups on the in-class activities significantly outperformed students who completed the activities individually on the higher-level, extended-response questions. Our results provide additional evidence that group processing of activities should be the recommended mode of implementation for in-class active-learning exercises.     

Comparing the Accuracy of Student Growth Measures

Testing programs are often interested in using a student growth measure. This article presents analytic derivations of the accuracy of common student growth measures on both the raw scale of the test and the percentile rank scale in terms of the proportional reduction in mean squared error and the squared correlation between the estimator and target. The study contrasts the accuracy of the growth measures against that of current status measures—current test scores and their percentile ranks. Key findings include the extent that status measures are more accurate than any of the growth measures and that alternative methods to estimate growth could be more accurate than the currently used methods. Our findings highlight the importance for evaluating the statistical properties of growth measures along with other concerns for states that are debating the reporting of growth. Our results also point out that assessing the accuracy of growth measures requires the specification of quantities of interest in terms of latent achievement rather than observed test scores, which is common practice for developing status measures but essentially never done by testing programs for growth measures.