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The Right Tool for the Job: Matching Active Learning Techniques to Learning Objectives

Sarah Jacobson (), Luyao Zhang and Jiasheng Zhu

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Abstract: Active learning comprises many varied techniques that engage students actively in the construction of their understanding. Because of this variation, different active learning techniques may be best suited to achieving different learning objectives. We study students' perceptions of a set of active learning techniques (including a Python simulation and an interactive game) and some traditional techniques (like lecture). We find that students felt they engaged fairly actively with all of the techniques, though more with those with a heavy grade weight and some of the active learning techniques, and they reported enjoying the active learning techniques the most except for an assignment that required soliciting peer advice on a research idea. All of the techniques were rated as relatively effective for achieving each of six learning objectives, but to varying extents. The most traditional techniques like exams were rated highest for achieving an objective associated with lower order cognitive skills, remembering concepts. In contrast, some active learning techniques like class presentations and the Python simulation were rated highest for achieving objectives related to higher order cognitive skills, including learning to conduct research, though lectures also performed surprisingly well for these objectives. Other technique-objective matches are intuitive; for example, the debate is rated highly for understanding pros and cons of an issue, and small group discussion is rated highly for collaborative learning. Our results support the idea that different teaching techniques are best suited for different outcomes, which implies that a mix of techniques may be optimal in course design.

Date: 2022-05, Revised 2022-07
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