What works? What's missing? An evaluation model for science curricula that analyses learning outcomes through five lenses

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Abstract

Science is rapidly changing with vast amounts of new information and technologies available. However, traditional instructional formats do not adequately prepare a diverse population of learners who need to evaluate and use knowledge, not simply memorize facts. Moreover, curricular change has been glacially slow. One starting goal for curricular change can be identifying the features of a current curriculum, including potential areas for improvement, but a model is needed to accomplish that goal. The vast majority of studies related to curricular change have been conducted in K-12 environments, with an increasing number in post-secondary environments. Herein, we describe a model for science curriculum evaluation that we designed by integrating a number of different approaches. That model evaluates the intended, enacted, and achieved components of the curriculum, anchored by analyzing learning outcomes through five lenses: (i) a scientific Framework reported by the US National Research Council, (ii) systems thinking, (iii) equity, diversity, and inclusion, (iv) professional skills, and (v) learning skills. No curriculum evaluation models to date have used the five learning outcomes lenses that we describe herein. As a proof of principle, we applied the evaluation model to one organic chemistry course, which revealed areas of strength and possible deficiencies. This model could be used to evaluate other science courses or programs. Possible deficiencies may be addressed in other courses, in the course at hand, or may not be deemed necessary or important to address, demonstrating the potential for this evaluation to generate areas for discussion and ultimately, improvements to post-secondary science education.

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Active learning increases student performance in science, engineering, and mathematics.

Scott Freeman, Sarah L. Eddy, Miles McDonough … (2014)

To test the hypothesis that lecturing maximizes learning and course performance, we metaanalyzed 225 studies that reported data on examination scores or failure rates when comparing student performance in undergraduate science, technology, engineering, and mathematics (STEM) courses under traditional lecturing versus active learning. The effect sizes indicate that on average, student performance on examinations and concept inventories increased by 0.47 SDs under active learning (n = 158 studies), and that the odds ratio for failing was 1.95 under traditional lecturing (n = 67 studies). These results indicate that average examination scores improved by about 6% in active learning sections, and that students in classes with traditional lecturing were 1.5 times more likely to fail than were students in classes with active learning. Heterogeneity analyses indicated that both results hold across the STEM disciplines, that active learning increases scores on concept inventories more than on course examinations, and that active learning appears effective across all class sizes--although the greatest effects are in small (n ≤ 50) classes. Trim and fill analyses and fail-safe n calculations suggest that the results are not due to publication bias. The results also appear robust to variation in the methodological rigor of the included studies, based on the quality of controls over student quality and instructor identity. This is the largest and most comprehensive metaanalysis of undergraduate STEM education published to date. The results raise questions about the continued use of traditional lecturing as a control in research studies, and support active learning as the preferred, empirically validated teaching practice in regular classrooms.