Title: Costs of success: Financial implications of implementation of active learning in introductory physics courses for students and administrators
Authors: Eric Brewe, Remy Dou, Robert Shand
First author’s institution: Drexel University
Journal: Physical Review Physics Education Research, 14, 010109 (2018)
Active learning courses have been shown to increase conceptual learning and to increase the odds of success compared to traditional lecture courses. Despite these benefits, lecture courses remain common in universities, due to the belief that transforming a lecture course into an active learning course would be too costly. However, the costs of lecture courses and active learning courses have not been well studied. The goal of today’s paper is to try to estimate what that cost is, specifically, what is the cost per student passing each course.
Due to a variety of research describing the positive outcomes of Modeling Instruction and the fact that Modeling Instruction has not been widely adopted, the researchers decided to compare this active learning course to traditional lecture courses, since one reason it may not be widely implemented is the cost. At Florida International University, where this study was conducted, Modeling Instruction is a 5 credit hour introductory physics course taught by physics faculty members. The course is taught in a studio format, meaning the “lecture” and lab components are integrated into a single class. The course has a maximum enrollment of 30 students. In contrast, the traditional lecture courses are 4 credit hours with a separate 1 credit hour laboratory course that is required but is not always taken the same semester as the lecture course. The traditional course is taught in sections of 120 students by physics faculty along with graduate students serving as teaching assistants. Historically (from 2004 to 2008), students in the Modeling Instruction class have a passing rate of 88% while students in the traditional course have a passing rate of around 52%. Since students in both courses are equally likely to complete their degrees, the difference in passing rates is due to the way the course is taught as opposed to “better” students taking the Modeling Instruction course or lower expectations for receiving a passing grade in the Modeling Instruction course.
To actually compare the costs of the courses, the authors looked at enrollment and financial data from the university in 2013 so that most of the students who would have been enrolled in the courses would have had time to graduate. The authors looked at cost of the instructors, the cost of using the classroom, and the cost of any equipment needed for laboratory experiments. The authors then compared the cost per student, the cost per passing student, the cost of the Modeling Instruction course if the same number of students were enrolled in that course as in the traditional lecture course, and the number of passing students per $1000 spent on the course.
So what did they find? The authors found that the Modeling Instruction was indeed more expensive than the traditional lecture course. The cost per student was nearly double ($920/student in Modeling Instruction compared to $410/student in the lecture course) while the cost was $1030/passing student in Modeling Instruction compared to $790/passing student in the traditional course. If Modeling Instruction were scaled to instruct the same number of students as the traditional courses do, it would cost an additional $60,580 over the current cost of the traditional course. Finally, the traditional course has a greater number of passing students per $1000 spent than the Modeling Instruction course does. The full results are shown in table 1.
From a purely economic perspective, traditional instruction seems like the better way to go. However, educational decisions are not based strictly off economic factors and many assumptions have went into the comparisons. The authors assumed that the Modeling Instruction would still be a 30 student class if it were to replace the traditional course. However, they calculate that there would need to be 45 sections of Modeling Instruction to cover the enrollment of the traditional courses, which would be too many sections for the departmental faculty to teach and would pose a challenge for scheduling the course and finding an adequate number of classrooms. These problems could be alleviated if more students were enrolled in a single section, but the course outcomes would need to stay the same, which is not guaranteed. An initial study with 76 students in a Modeling Instruction course found similar academic outcomes for the students but even a course of this size would still require two sections to enroll the same number of students as a single traditional course does and hence would still have a higher cost than a traditional lecture course.
From a student perspective however, the active learning courses appear to be a better value. Since the Modeling Instruction has a higher passing rate, students are less likely to have to retake the course and need to pay for the course a second time. In addition, students gain more conceptual knowledge in Modeling Instruction than in traditional courses so students are getting more out of the course for the same amount of their tuition dollars. From a student’s perspective, active learning courses provide the better value.
So what can we take away from this paper? First, this paper shows that one specific type of active learning course, Modeling Instruction, is more expensive to implement than a traditional course by multiple measures. However, this study only looks at a single active learning method at a single institution so the results must be taken lightly. Nevertheless, the study brings up an important issue of what the proper balance between increased cost of implementing more effective teaching methods and student success should be. As governmental funding to universities continues to decline, universities are often expected to cut their costs while also improving student outcomes so some type of balance between the two must be struck.
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I am a physics and computational mathematics, science, and engineering PhD student at Michigan State University and the founder of PERbites. I’m interested in applying machine learning to analyze educational datasets and am currently studying the physics graduate school admissions process.