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Register a new userThe Initial State of Students Taking an Introductory Physics MOOC
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As part of a larger research project into massively open online courses (MOOCs), we have investigated student background, as well as student participation in a physics MOOC with a laboratory component. Students completed a demographic survey and the Force and Motion Conceptual Evaluation at the beginning of the course. While the course is still actively running, we have tracked student participation over the first five weeks of the eleven-week course.
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In an Introductory Physics for Life Science (IPLS) course that leverages authentic biological examples, student ideas about entropy as disorder or chaos come into contact with their ideas about the spontaneous formation of organized biological structure. It is possible to reconcile the natural tendency to disorder with the organized clustering of macromolecules, but doing so in a way that will be meaningful to students requires that we take seriously the ideas about entropy and spontaneity that students bring to IPLS courses from their prior experiences in biology and chemistry. We draw on case study interviews to argue that an approach that emphasizes the interplay of energy and entropy in determining spontaneity (one that involves a central role for free energy) is one that draws on students resources from biology and chemistry in particularly effective ways. We see the positioning of entropic arguments alongside energetic arguments in the determination of spontaneity as an important step toward making our life science students biology, chemistry, and physics experiences more coherent.
Physics lab courses are an essential part of the physics undergraduate curriculum. Learning goals for these classes often include the ability to interpret measurements and uncertainties. The Physics Measurement Questionnaire (PMQ) is an established open-response survey that probes students understanding of measurement uncertainty along three dimensions: data collection, data analysis, and data comparison. It classifies students reasoning into point-like and set-like paradigms, with the set-like paradigm more aligned with expert reasoning. In the context of a course transformation effort at the University of Colorado Boulder, we examine over 500 student responses to the PMQ both before and after instruction in the pre-transformed course. We describe changes in students overall reasoning, measured by aggregating four probes of the PMQ. In particular, we observe large shifts towards set-like reasoning by the end of the course.
The lack of diversity and the under-performance of underrepresented students in STEM courses have been the focus of researchers in the last decade. In particular, many hypotheses have been put forth for the reasons for the under-representation and under-performance of women in physics. Here, we present a framework for helping all students learn in science courses that takes into account four factors: 1) characteristics of instruction and learning tools, 2) implementation of instruction and learning tools, 3) student characteristics, and 4) students environments. While there has been much research on factor 1 (characteristics of instruction and learning tools), there has been less focus on factor 2 (students characteristics, and in particular, motivational factors). Here, we focus on the baseline motivational characteristics of introductory physics students obtained from survey data to inform factor 2 of the framework. A longitudinal analysis of students motivational characteristics in two-semester introductory physics courses was performed by administering pre- and post-surveys that evaluated students self-efficacy, grit, fascination with physics, value associated with physics, intelligence mindset, and physics epistemology. Female students reported lower self-efficacy, fascination and value, and had a more fixed view of intelligence in the context of physics compared to male students. Grit was the only factor on which female students reported averages that were equal to or higher than male students throughout introductory physics courses. These gender differences can at least partly be attributed to the societal stereotypes and biases about who belongs in physics and can excel in it. The findings inform the framework and have implications for the development and implementation of effective pedagogies and learning tools to help all students learn.
The Physics Inventory of Quantitative Literacy (PIQL), a reasoning inventory under development, aims to assess students physics quantitative literacy at the introductory level. The PIQLs design presents the challenge of isolating types of mathematical reasoning that are independent of each other in physics questions. In its current form, the PIQL spans three principle reasoning subdomains previously identified in mathematics and physics education research: ratios and proportions, covariation, and signed (negative) quantities. An important psychometric objective is to test the orthogonality of these three reasoning subdomains. We present results from exploratory factor analysis, confirmatory factor analysis, and module analysis that inform interpretations of the underlying structure of the PIQL from a student viewpoint, emphasizing ways in which these results agree and disagree with expert categorization. In addition to informing the development of existing and new PIQL assessment items, these results are also providing exciting insights into students quantitative reasoning at the introductory level.
An important goal of introductory physics for the life sciences (IPLS) is for those students to be prepared to use physics to model and analyze biological situations in their future studies and careers. Here we report our findings on life science students ability to carry out a sophisticated biological modeling task at the end of first-semester introductory physics, some in a standard course (N = 34), and some in an IPLS course (N = 61), both taught with active learning and covering the same core physics concepts. We found that the IPLS students were dramatically more successful at building a model combining multiple ideas they had not previously seen combined, and at making complex decisions about how to apply an equation to a particular physical situation, although both groups displayed similar success at solving simpler problems. Both groups identified and applied simple models that they had previously used in very similar contexts, and executed calculations, at statistically indistinguishable rates. Further study is needed to determine whether IPLS students are more expert problem-solvers in general or solely in biological settings.
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