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Designing Active Learning Environments
Figure 2. Painting by Laurentius de Voltolina dating from the second half of the 14th century. Judging from this illustration, student dis- traction and dozing during lecture are not modern problems. Photo from commons.wikimedia.org.
the fraction of the available improvement that was attained during the course. Hake’s survey of over 6,000 students in mechanics courses showed that the average gain for tradi- tional lecture courses was 0.23. That is, students learned less than 25% of what they didn’t know (Figure 3). In contrast, active-learning courses had an average gain of 0.48.
Are Hake’s results applicable to courses other than Newto- nian mechanics? Two of us (JRB and KEW) developed the signals and systems concept inventory (SSCI), which is de- signed to measure conceptual understanding in undergrad- uate linear systems courses (Wage et al., 2005). The SSCI assesses students’ understanding of Fourier analysis, convo- lution, filtering, and sampling. Figure 3 shows the results of our analysis of gain for the SSCI. Similar to Hake’s (1998) results, the SSCI analysis shows a significant increase in gain for active learning courses.
Recently, Freeman et al. (2014) prepared a meta-analysis comparing traditional lecture and active learning for STEM disciplines. Based on data from 225 studies, they showed that student performance on examinations in active-learn- ing courses increased by 0.47 standard deviations over ex- aminations in traditional lecture courses. Using CI data from 22 studies, including those for the FCI and SSCI, the authors concluded that active learning improves the final CI score by 0.88 standard deviations, indicating that students in active-learning courses demonstrated greater improvement in conceptual understanding. Finally, as Figure 3 illustrates, Freeman et al. showed that the failure rate for students in traditional lecture courses was 33.8%, whereas the failure rate in active-learning courses was 21.8%. Based on this meta-analysis, Freeman et al. concluded that active learn- ing is the “preferred empirically validated teaching practice” and suggested that the traditional lecture should no longer be used as the control in research studies.
Why Is the Traditional Lecture So Ineffective?
A skilled lecturer can present material in such a gloriously smooth fashion that everything seems clear to even the most naive listener. But is this clarity real? Or is it an illusion? Watching an expert perform in any domain, be it techni- cal, musical, or athletic, can mislead us into thinking we can easily duplicate their performance. This “illusion of know- ing” (Brown et al., 2014, pp. 102-130) is typically dispelled the moment we attempt the same feat. The typical college classroom has more illusions floating around than Hog- warts School of Witchcraft and Wizardry (Rowling, 1999). The students are under the illusion that they understand what the instructor is saying. The students often don't real- ize that their understanding is a mere mirage until they get home and start the homework. At that point, there is no one around to answer his or her questions. The instructor is un- der the illusion that the lecture is clear and that all students
Figure 3. Active learning increases how much students gain and re- duces failure rates. Left: Gains (means ± SD) for lecture and active courses as measured by two concept inventories, the force concept in- ventory (FCI) and the signals and systems concept inventory (SSCI). The FCI results are from Hake’s analysis (1998) of 14 lecture courses and 48 active courses. The SSCI results are from our own analysis of 28 lecture courses and 34 active courses. Right: Freeman et al.’s (2014) results from a meta-analysis of the failure rates in lecture and active learning courses in science, technology, engineering, and math (STEM).
14 | Acoustics Today | Summer 2016