Hidden Markov Models (HMMs) are an essential tool for Bioinformatic analysis, with extensive success at finding patterns (e.g. CRISPR arrays or genes of interest) in DNA or protein sequences. HMMs are conceptually intricate, and the algorithms that make use of them are complicated. Thus they present a challenge to Bioinformatics instructors at the undergraduate level, particularly when the students’ educational backgrounds are broadly diverse. At San Jose State University, many undergraduate Bioinformatics students are Biology majors with little or no prior coursework in mathematics, statistics, or programming. For this population a theory-based approach to teaching HMMs would be ineffective. To address this problem we have developed an active learning module that takes advantage of the similarity between HMMs and board games. Our materials include a physical game board for introducing concepts, a software implementation of the game, similar software for visualizing and manipulating HMMs that model proteins, in-class lab exercises, and homework assignments. We have observed high student engagement with these materials over 4 semesters in a diverse undergraduate Advanced Bioinformatics course. Here we present our materials, which are freely available to educators.
Education, Bioinformatics, Hidden Markov Models, Engagement
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Philip Heller and Pratyusha Pogaru. "A novel approach to teaching Hidden Markov Models to a diverse undergraduate population" Heliyon (2021). https://doi.org/10.1016/j.heliyon.2021.e06437