Publication Date
3-1-2021
Document Type
Article
Publication Title
Heliyon
Volume
7
Issue
3
DOI
10.1016/j.heliyon.2021.e06437
Abstract
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.
Keywords
Education, Bioinformatics, Hidden Markov Models, Engagement
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Department
Computer Science
Recommended Citation
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
Comments
This is the Version of Record and can also be read online here.