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

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

Scott Freeman^a, Sarah L. Eddy^a, Miles McDonough^a, Michelle K. Smith^b, Nnadozie Okoroafor^a, Hannah Jordt^a, and Mary Pat Wenderoth^a

^aDepartment of Biology, University of Washington, Seattle, WA 98195; and ^bSchool of Biology and Ecology, University of Maine, Orono, ME 04469

Edited* by Bruce Alberts, University of California, San Francisco, CA, and approved April 15, 2014 (received for review October 8, 2013)

PNAS 2014 111 (23) 8410-8415; published ahead of print May 12, 2014,doi:10.1073/pnas.1319030111

Abstract: http://www.pnas.org/content/111/23/8410.abstract

In this article, the authors meta-analyzed 225 studies comparing performance of students in lectures that use active learning methods and in traditional lectures. When this was published, this was the largest and most comprehensive meta-analysis of STEM education literature. 

The authors provide the following definitions of the two types of lectures:

Active learning engages students in the process of learning through activities and/or discussion in class, as opposed to passively listening to an expert. It emphasizes higher-order thinking and often involves group work.

The authors used Bligh’s (see article for reference) defined traditional lecturing as “. . .continuous exposition by the teacher.” Under this definition, student activity was assumed to be limited to taking notes and/or asking occasional and unprompted questions of the instructor.”

The definition of active learning was derived from coding elements from 338 audience members before departmental biology seminars on active learning in college and universities across the United States.

The authors wanted to know whether active learning lectures have any effect on student performance.  The two metrics used for student performance are (from the supporting information document):

We evaluated performance using two metrics: (i) scores on identical or formally equivalent examinations, concept inventories, or other assessments, and (ii) failure rates—in most cases measured as the percentage of Ds, Fs, and/or withdrawals. These were relevant criteria for failure because students with a D, F, or W in a STEM course are usually barred from receiving credit in the major.

The paper and the supporting information provide the details on:

·         Literature search:
In addition to peer-reviewed resources, the authors also searched gray literature, primarily unpublished dissertations and conference proceedings “for studies that compared student performance in undergraduate STEM courses under traditional lecturing versus active learning”.

·         Literature selection for inclusion in the meta-analysis:

The papers were independently coded using the following criteria:

(i) contrasted traditional lecturing with any active learning intervention, with total class time devoted to each approach not differing by more than 30 min/wk;

(ii) occurred in the context of a regularly scheduled course for undergraduates;

(iii) were largely or solely limited to changes in the conduct of the regularly scheduled class or recitation sessions;

(iv) involved a course in astronomy, biology, chemistry, computer science, engineering, geology, mathematics, natural resources or environmental science, nutrition or food science, physics, psychology, or statistics; and (v) included data on some aspect of student academic performance.

The two coders reviewed and discussed the literature until they reached consensus on the basis of the five criteria above and the additional information below:

i) The five criteria listed above for admission to the study;

ii) Examination equivalence—meaning that the assessment given to students in the lecturing and active learning treatment groups had to be identical, equivalent as judged by at least one third-party observer recruited by the authors of the study in question but blind to the hypothesis being tested, or comprising questions drawn at random from a common test bank;

iii) Student equivalence—specifically whether the experiment was based on randomization or quasirandomization among treatments and, if quasirandom, whether students in the lecture and active learning treatments were statistically indistinguishable in terms of (a) prior general academic performance (usually measured by college GPA at the time of entering the course, Scholastic Aptitude Test, or American College Testing scores), or (b) pretests directly relevant to the topic in question;

iv) Instructor equivalence—meaning whether the instructors in the lecture and active learning treatments were identical, randomly assigned, or consisted of a group of three or more in each treatment; and

v) Data that could be used for computing an effect size.

·         Data Analysis

See article for a detailed summary.

RESULTS

Overall conclusions by the authors, in their own words as stated in the Discussion section:

The data reported here indicate that active learning increases examination performance by just under half a SD and that lecturing increases failure rates by 55% (21.8% for active learning students and 33.8% for traditional lecture students). The heterogeneity analyses indicate that (i) these increases in achievement hold across all of the STEM disciplines and occur in all class sizes, course types, and course levels; and (ii) active learning is particularly beneficial in small classes and at increasing performance on concept inventories.

See relevant figures below from the article.

Fig. 2.

Effect sizes by discipline. (A) Data on examination scores, concept inventories, or other assessments. (B) Data on failure rates. Numbers below data points indicate the number of independent studies; horizontal lines are 95% confidence intervals.