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SoTL Publications by LMU Faculty

Bennett, C. & Dewar J. (2007). Developing and applying a taxonomy for mathematical knowledge-expertise. Electronic Proceedings for the Tenth Special Interest Group of the Mathematical Association of America on Research in Undergraduate Mathematics Education Conference on Research in Undergraduate Mathematics Education. Available from

V. P. Coletta, J. A. Phillips and J. J. Steinert, “Why You Should Measure Your Students’ Reasoning Ability Before You Give The Force Concept Inventory” Phys. Teach.. 45 (4) 235 (2007)

Many teachers administer a force concept test such as the Force Concept Inventor (FCI) to their students in an effort to evaluate and improve their instructional practices. It has been commonly assumed that looking at class normalized gains allows teachers to compare their courses with other courses. In this paper we present evidence to suggest that the use of class normalized gains alone may not provide a complete picture. We argue that student reasoning ability should also be assessed before between-course comparisons can be made. Assessment of reasoning ability is also useful in identifying students who are at risk. In the following we shall concentrate on the FCI, but we think our conclusions probably apply to physics concept tests generally.

V. P. Coletta, J. A. Phillips and J. J. Steinert, “Interpreting FCI scores: normalized gain and SAT scores” Phys. Rev. Spec. Top.- Phys. Educ. Res.. 3, 010106 (2007)

Preinstruction SAT scores and normalized gains G on the force concept inventory FCI were examined for individual students in interactive engagement IE courses in introductory mechanics at one high school N=335 and one university N=292 , and strong, positive correlations were found for both populations r=0.57 and r=0.46, respectively . These correlations are likely due to the importance of cognitive skills and abstract reasoning in learning physics. The larger correlation coefficient for the high school population may be a result of the much shorter time interval between taking the SAT and studying mechanics, because the SAT may provide a more current measure of abilities when high school students begin the study of mechanics than it does for college students, who begin mechanics years after the test is taken. In prior research a strong correlation between FCI G and scores on Lawson’s Classroom Test of Scientific Reasoning for students from the same two schools was observed. Our results suggest that, when interpreting class average normalized FCI gains and comparing different classes, it is important to take into account the variation of students’ cognitive skills, as measured either by the SAT or by Lawson’s test. While Lawson’s test is not commonly given to students in most introductory mechanics courses, SAT scores provide a readily available alternative means of taking account of students’ reasoning abilities. Knowing the students’ cognitive level before instruction also allows one to alter instruction or to use an intervention designed to improve students’ cognitive level.

V. P. Coletta and J. A. Phillips, “Interpreting FCI scores: Normalized gain, pre-instruction scores, and scientific reasoning ability” Am. J. Phys., 73, 1172 (2005).

We examined normalized gains and preinstruction scores on the force concept inventory FCI for students in interactive engagement courses in introductory mechanics at four universities and found a significant, positive correlation for three of them. We also examined class average FCI scores of 2948 students in 38 interactive engagement classes, 31 of which were from the same four universities and 7 of which came from 3 other schools. We found a significant, positive correlation between class average normalized FCI gains and class average preinstruction scores. To probe this correlation, we administered Lawson’s classroom test of scientific reasoning to 65 students and found a significant, positive correlation between these students’ normalized FCI gains and their Lawson test scores. This correlation is even stronger than the correlation between FCI gains and preinstruction FCI scores. Our study demonstrates that differences in student populations are important when comparing normalized gains in different interactive engagement classes. We suggest using the Lawson test along with the FCI to measure the effectiveness of alternative interactive engagement strategies.

Dewar, J., Larson, S. & Zachariah, T. (to appear). Group Projects and Civic Engagement in a Quantitative Literacy Course. PRIMUS: Problems, Resources, and Issues in Mathematics Undergraduate Studies.

Dewar, J. & Bennett, C. (2010). Situating SoTL within the disciplines: Mathematics in the United States as a case study. International Journal of the Scholarship of Teaching and Learning. (4)1.

Dewar, J. "An Apology for the Scholarship of Teaching and Learning," InSight: A Journal for Scholarly Teaching, 3, 2008, pp. 17-22.

Dewar, J. (2008). What is mathematics: Student and faculty views. Electronic Proceedings for the Eleventh Special Interest Group of the Mathematical Association of America on Research in Undergraduate Mathematics Education Conference on Research in Undergraduate Mathematics Education. Available here.

Dewar, J. (2007). Scholarship of Teaching and Learning: What? and Why Now?Association for Women in Mathematics Newsletter. 37(6), 26-28.

Dionisio, J. D. and Dahlquist, K. D. 2008. Improving the computer science in bioinformatics through open source pedagogy. SIGCSE Bull. 40, 2 (Jun. 2008), 115-119.

This is the URL to the journal web page. However, to download the article, you need a subscription.


Bioinformatics relies more than ever on information technologies. This pressures scientists to keep up with software development best practices. However, traditional computer science curricula do not necessarily expose students to collaborative and long-lived software development. Using open source principles, practices, and tools forms an effective pedagogy for software development best practices. This paper reports on a bioinformatics teaching framework implemented through courses introducing computer science students to the field. The courses led to an initial product release consisting of software and an Escherichia coli K12 GenMAPP Gene Database, within a total "incubation time" of six months.

Thadani, V., Breland, W. & Dewar, J. (to appear). College Instructors' Implicit Theories About Teaching Skills and Their Relationship to Professional Development Choices. Journal on Excellence in College Teaching.

Zachariah, T., Larson, S. & Dewar, J. (2006). An emerging model:

Quantitative literacy through community-based group projects. Retrieved April 4, 2008 from