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Updated December 15, 2014

Rationale or motivation for the study

1. Schatz, M.C. (2012). Computational thinking in the era of big data biology. Genome Biology, 13, 177.

This essay briefly defines quantitative biology and argues why the advent of “big data biology” demands an expansion of skills beyond those traditionally required of biologists. It describes how a graduate level course in quantitative biology is organized at Cold Spring Harbor Laboratory, and provides examples of topics discussed and specific lessons discussed in class. This essay is useful for me because it clearly outlines the rationale for expanding quantitative skills for undergraduate biologists and illustrates what types of analytical skills they should have to succeed in their continued education and careers. This essay also illustrates the importance of interdisciplinary collaboration- the author is a computer scientist, teaching in a PhD biology program. This helps justify my own collaboration with mathematicians.

2. Murdoch, T. B.. & Detsky, A. S. (2013) The Inevitable Application of Big Data to Health Care. Journal of the American Medical Association, 309,1351-2.

This Viewpoint article explains the origins of the term big data and outlines the economic opportunities and roadblocks that adoption of big data analysis will introduce into health care. Specifically, the authors identify (a) a capacity to generate new knowledge via data analysis, (b) increased knowledge dissemination to a broader professional network, (c) integration of systems biology (e.g., genomic) data with digital health records to advance personalized medicine, and (d) empowering of patients by providing them more immediate access to their health records, as major advantages to embracing big data in health care. Topics (a) and (c) are important elements of my course and are important motivators for my research project. Implicitly, this article argues why increasing big data analysis by biologists will be an important driver for transforming their undergraduate education. Combined with article #1 described above, it lays a solid foundation for pursuing my research project.

3. Rylands, L., Simbag, V., Matthews, K. E., Carmel, C. &  Belward, S. (2013) Scientists and mathematicians collaborating to build quantitative skills in undergraduate science. International Journal of Mathematical Education in Science and Technology, 44, 834·845, DOI: 10.108010020739X.2013.783239

One of the most significant findings in this survey article is the relative scarcity of interdisciplinary math/science collaboration in the education literature. The authors conducted interviews with 48 faculty and administrators across 11 Australian and two US universities to ascertain who was teaching quantitative science courses and why teaching collaboration was so scarce. The results demonstrate that mathematics departments are responsible for teaching approximately 75% of these skills, and that over 90% of the comments by scientists were negative towards collaborative teaching with mathematicians. This clearly establishes that filling the need for interdisciplinary teaching of big data analysis begins with building collaborative teaching projects, such as the one I am participating in this Fall.

Design of the study

4. Knisley, J. & Behravesh, E. (2010). Developing Student Collaborations across Disciplines, Distances, and Institutions. CBE—Life Sciences Education, 9, 364–369. DOI: 10.1187/cbe.10–03–0031.

This article describes a course that promotes collaboration between biomedical engineering and mathematics students across two institutions (East Tennessee State University and Georgia Technological University/Emory University). The course uses an investigative lase-based learning (ICBL) format to propose solutions to a real-world problem: why female athletes are 4-6 times more likely to suffer an ACL injury than male athletes. They conclude that while most of the models developed by the mathematicians did not “lend themselves to implementation” by the engineers, the students did acquire a greater appreciation for working outside their primary discipline, as measured by before-  and after-project questionnaires. This was especially true for students whose projects were testable. It emphasizes that a case study-format would be acceptable for doing this research, and that if we are careful to constrain the analysis to generate mostly meaningful results, we should be able to increase biology student enthusiasm for mathematics modeling.

Methods for data collection and analysis, and
Interpretation of the results

5. Birnbaum, M. J. (2010). Using Osteoclast Differentiation as a Model for Gene Discovery in an Undergraduate Cell Biology Laboratory. Biochemistry and Molecular Biology Education, 38, 385-392.

The goal of this study was to train students how to identify genes that are expressed more during differentiation of an immortalized preosteoclast cell line or primary bone marrow mononuclear cells. Cells were differentiated and gene expression was determined from Affymetrix gene chips before the course started. Students were trained to access the instructor’s sequence data and find third-party data sets at NCBI GEO, select genes of interest, explore evolutionary history of the genes, search tissue and developmental expression (using NCBI Unigene), search for transcription factor binding sites, and discover potential phosphorylation sites of their gene of interest (myoD1). Finally, students searched for pre-designed siRNA constructs for knocking down myoD1, then performed a set of laboratory experiments to assay the impact of myoD1 knockdown on cultured preosteoblast cells. This paper is useful for me because it shows how students can access and analyze gene expression data without having to generate the data themselves. It provides a foundation for the mathematicians to expand their analysis techniques. It is more ambitious than my project, because my class will not be using siRNA or performing any wetlab experiments.

***6. Susan Hester, S.,  Buxner, S., Elfring, L., & Nagy, L. (2014). Integrating Quantitative Thinking into an Introductory Biology Course Improves Students’ Mathematical Reasoning in Biological Contexts. CBE—Life Sciences Education, 13, 54–64.

The goal of this study was to measure the impact of active learning techniques that integrated mathematical skills with course content on the ability "to apply mathematical skills in biological contexts" in an introductory cell & molecular biology course. The active learning exercises were performed throughout the semester, using a flipped classroom approach. The outcomes were assessed with a multiple choice,  pre-post assessment. One author taught four "traditional" sections of the same course to serve as a negative control. The gains were assessed as percentage of possible gain realized (100% x [post score-pre score]/[total possible score- pre score]). The results show that students in the experimental section made significantly greater gains than students in control sections. This paper is useful to me because its active learning format resembles the format of my own class and it validates the use of pre-post surveys as a means of measuring quantitative reasoning skills.

***7. Kitchen, E., Bell, J.D., Reeve, S., Sudweeks, R.R., & Bradshaw, W.S. (2003). Teaching Cell Biology in the Large-Enrollment Classroom: Methods to Promote Analytical Thinking and Assessment of Their Effectiveness. Cell Biology Education, 2, 180–194.

The goal of this study was to measure the imapct of active learning exercises on students' ability to interpret experimental data in a large enrollment cell biology course. The exercises were conducted in class, using a traditional "flipped" approach. A non-active learning version of the same course was included as a negative control. Effectiveness of the approach was assessed via three data analysis problems and three conceptual problems on each of four midterm exams and a comprehensive final exam. The questions were graded according to a rubric designed to reduce inter-grader variance, and difficulty of exam questions was normalized using a Rasch analysis. Changes in student performance were quantified as changes in statistical Z scores (displacement from class mean normalized to class standard deviation). The authors also developed a novel indexing system for measuring the course-wide changes in student abilities. Student attitudes and confidfence were evaluated at the beginning and end of the course. The results demonstrate that the active learning version of the course increased student analytical skills beyond a theortically random improvement and improved both student confidence and attitudes towards courses that require analytical thinking. This study is useful to me because it provides concrete examples of how to analyze my data from the Fall semester and offers examples for approahces that I can use in the Spring 2015 semester. It also helps me focus the goals of my own study to Bloom's Taxonomy-based evaluation of critical thinking.


***Added on December 15, 2015


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