Students engaged in real biological research, either via experiences outside of the class, or open-inquiry activities in the class, learn more and are better motivated. This project capitalizes on these facts to address the disinterest and discomfort many university biology students have with mathematics and statistics. Learning materials are being created, assessed, and disseminated that allow meaningful hypothesis formation, data collection, and most importantly analyses, and that capture student interest via image analysis of fascinating biological phenomena.

For example, students might study first-hand the bioenergetics of leaf-cutter ant foraging, or the role of macrophage recruitment in atherosclerosis, using image analysis, without leaving their dorm room or computer lab. How can this be accomplished? First, students are presented with background information setting the framework for a current research frontier, a video interview with the researcher, and then a set of images or videos that the researcher would use in their own work. Open-inquiry modules lead the students to develop testable hypotheses, define the variables they should measure, and collect data themselves, using image analysis software. Careful planning and choice of image sets mean students may choose to collect data besides that envisioned by the original investigator – thus, this is not simply “click through the steps”, science by recipe. Moreover, involvement in data collection gives the students a much deeper understanding for the data, and expectations for the results of analyses or modeling.

The learning materials are created after visiting researchers in the field or lab to collect images, videos, and background information. The design process is iterative, alternating between formative feedback from peers and classroom testing.  Partner institutions will broaden the scope of assessment to cover a great deal of diversity in student population and institution type.

Image analysis is performed using the free, easy-to-use software ImageJ. Instructors don't need special experience with image analysis, or with the particular research contexts, but only a willingness to explore and learn for an hour or two before assigning their students the modules.

Student responses to the modules has been encouraging and positive, and analyses of assessment data from multiple institutions and classroom settings are forthcoming. But just as an example, in one class that did the leaf-cutter ant module, 74% of students said learning statistics was more interesting because of the ant image analysis context than it would have been otherwise, and no students said it was less interesting (i.e. 26% said it was no more or less interesting). Imagine the difference in your classroom if 3/4 of the students became more interested to learn!


Questions, comments, feedback? Contact Dr. Jeremy M. Wojdak

This work is funded by the National Science Foundation's Improving Undergraduate STEM Education program, under grant DUE-1431671.