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A Novel Approach To Teaching Hidden Markov Models To A Diverse Undergraduate Population, Philip Heller, Pratyusha Pogaru

Faculty Research, Scholarly, and Creative Activity

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 …