The Genome Solver Project: Faculty Training and Student Performance Gains in Bioinformatics †
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Authors:
Vinayak Mathur1,
Gaurav S. Arora2,
Mindy McWilliams3,
Janet Russell4,
Anne G. Rosenwald5,*
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Received 22 March 2018 Accepted 07 February 2019 Published 26 April 2019
- ©2019 Author(s). Published by the American Society for Microbiology
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[open-access] This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial-NoDerivatives 4.0 International license (https://creativecommons.org/licenses/by-nc-nd/4.0/ and https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode), which grants the public the nonexclusive right to copy, distribute, or display the published work.
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†Supplemental materials available at http://asmscience.org/jmbe
- *Corresponding author. Mailing address: Georgetown University, 306 Regents, 37th and O Sts., NW, Washington, DC 20057. Phone: 202-687-5997. E-mail: [email protected].
Abstract:
Bioinformatics brings together biology, mathematics, statistics, and computer science to analyze biological sequence information. Anyone with a computer, access to the Internet, and basic training in this field can contribute to genomics research. Yet many biology faculty feel they lack training in the use of bioinformatics tools and therefore include little bioinformatics content in their courses. To overcome this challenge, the Genome Solver Project was created to empower undergraduate faculty by offering training and resources for creating hands-on bioinformatics course materials. In this study, we show the results of one survey completed directly after the workshop and a further follow-up survey to gain insight into the impact the workshop had on faculty willingness to include bioinformatics content in their courses and what challenges they still faced. We also measured student performance at five different institutions using a 20-question multiple-choice quiz delivered before and after bioinformatics instruction. Data collected from 640 students at these five schools demonstrated student performance increased, suggesting that bioinformatics training workshops can be an effective means of encouraging faculty to engage in bioinformatics instruction and positively influence student learning.
References & Citations
Supplemental Material
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Appendix 1: Follow-up participant survey, Appendix 2: Pre-and post-course quiz questions for students, Appendix 3: Faculty interview protocol, Appendix 4: Supplemental Figure 1. Comparison of pre- and post-course quiz responses after removing school 5 (Georgetown University)
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Abstract:
Bioinformatics brings together biology, mathematics, statistics, and computer science to analyze biological sequence information. Anyone with a computer, access to the Internet, and basic training in this field can contribute to genomics research. Yet many biology faculty feel they lack training in the use of bioinformatics tools and therefore include little bioinformatics content in their courses. To overcome this challenge, the Genome Solver Project was created to empower undergraduate faculty by offering training and resources for creating hands-on bioinformatics course materials. In this study, we show the results of one survey completed directly after the workshop and a further follow-up survey to gain insight into the impact the workshop had on faculty willingness to include bioinformatics content in their courses and what challenges they still faced. We also measured student performance at five different institutions using a 20-question multiple-choice quiz delivered before and after bioinformatics instruction. Data collected from 640 students at these five schools demonstrated student performance increased, suggesting that bioinformatics training workshops can be an effective means of encouraging faculty to engage in bioinformatics instruction and positively influence student learning.

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Author and Article Information
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Received 22 March 2018 Accepted 07 February 2019 Published 26 April 2019
- ©2019 Author(s). Published by the American Society for Microbiology
-
[open-access] This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial-NoDerivatives 4.0 International license (https://creativecommons.org/licenses/by-nc-nd/4.0/ and https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode), which grants the public the nonexclusive right to copy, distribute, or display the published work.
-
†Supplemental materials available at http://asmscience.org/jmbe
- *Corresponding author. Mailing address: Georgetown University, 306 Regents, 37th and O Sts., NW, Washington, DC 20057. Phone: 202-687-5997. E-mail: [email protected].
Figures

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FIGURE 1
Workshop survey results. Participants completed a survey directly after workshop instruction. The statements were presented in the context of a five-point Likert scale (1 = strongly disagree, 2 = disagree, 3 = neither agree nor disagree, 4 = agree, and 5 = strongly agree). The data set represents the responses from 277 workshop participants over the course of workshops presented from 2012 to 2017. Responses from individual workshops were similar to those shown here in aggregate. GS = Genome Solver.

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FIGURE 2
Proportion of correct responses on the pre- and post-course quiz. The data shown here are the aggregate data from all students (n = 640) who took the pre- and post-course quiz ( Appendix 2 ). The proportion of correct responses increased for 15 of the 20 questions. Note that questions 1 and 2 are basic biology questions included as controls and show no increase. The others are specific to aspects of genomics and bioinformatics. # p < 0.05; * p < 0.01.

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FIGURE 3
Post-course mean scores of students at the five schools are significantly higher than pre-course means. The mean scores of the students (n = 640) who took the pre- and post-course quiz, by school. The proportion of correct responses increased for 16 of the 20 questions. # p < 0.05; * p < 0.01.

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FIGURE 4
Differences between the pre- and post-course mean over time. The difference between the pre- and post-course mean scores of the students from the three schools with bioinformatics instruction for at least three semesters is shown. School 5 had two different instructors; the three data points from 2012–2014 are one instructor and 2015 is the second instructor.