Document Type

Conference Paper

Publication Date

2026

Abstract

STEM education in the modern age has been subject to much reform – from the integration of technology to an emphasis on student-centered teaching strategies, such as active learning. However, in the wake of virtual and blended-learning environments, student engagement and teacher assessment of student success have been challenged. Tools such as KAHOOT! and iClicker have been promoted to foster an active learning environment while sometimes falling short in regards to student retention of course material. In light of this technological educational revolution, instructors need to be able to determine the most effective tools for their discipline to aid in student understanding and success. Using the TPACK framework, however, instructors are equipped to optimize technology selection for the promotion of active learning and maximal student success.

We present here the effective teaching practices, viz., active learning through algorithm development and computer programming, that enabled us to teach undergraduate and graduate students complex genetic and mathematical principles without requiring extensive background knowledge or traditional laboratory resources. Within the past academic year, a special topics course in Investigative Genetic Genealogy (IGG) was developed. We integrated the genetic principles of inheritance and corresponding probabilistic data interpretation used by IGG practitioners in casework. As such, the TPACK framework was implemented to promote active learning of genetic principles through algorithm development, computer programming, and simulation. Such an approach proved to be accessible for us as instructors, as well as effective for improving the technological and scientific literacy of students.

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