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Register a new userA Framework for Evaluating Statistical Models in Physics Education Research
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Across the field of education research there has been an increased focus on the development, critique, and evaluation of statistical methods and data usage due to recently created, very large data sets and machine learning techniques. In physics education research (PER), this increased focus has recently been shown through the 2019 Physical Review PER Focused Collection examining quantitative methods in PER. Quantitative PER has provided strong arguments for reforming courses by including interactive engagement, demonstrated that students often move away from scientist-like views due to science education, and has injected robust assessment into the physics classroom via concept inventories. The work presented here examines the impact that machine learning may have on physics education research, presents a framework for the entire process including data management, model evaluation, and results communication, and demonstrates the utility of this framework through the analysis of two types of survey data.
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We describe a course designed to help future educators build an integrated understanding of the different elements of physics education research (PER), including: research into student learning, content knowledge from the perspective of how it is learned, and reform-based curricula together with evidence of their effectiveness. Course elements include equal parts of studying physics through proven curricula and discussion of research results in the context of the PER literature. We provide examples of the course content and structure as well as representative examples of student learning in the class.
Diversity has an important, multifaceted role in the success of education. I have used a corpus analysis of the first ten years of Physical Review Physics Education Research to investigate the diversity, broadly defined, of physics education research. The results show that the physics education research community has extensively investigated the diversity of physics students, especially with respect to gender. However, some less visible but still numerous groups of students are little studied. On the whole, the contents of Physical Review Physics Education Research reveals a robust research community in physics education, but there is room for growth with respect to working with different kinds of students and the interactions between student attributes and teaching methods.
Sound educational policy recommendations require valid estimates of causal effects, but observational studies in physics education research sometimes have loosely specified causal hypotheses. The connections between the observational data and the explicit or implicit causal conclusions are sometimes misstated. The link between the causal conclusions reached and the policy recommendations made is also sometimes loose. Causal graphs are used to illustrate these issues in several papers from Physical Review Physics Education Research. For example, the core causal conclusion of one paper rests entirely on the choice of a causal direction although an unstated plausible alternative gives an exactly equal fit to the data.
Background: Qualitative interviewing is a common tool that has been utilized by Science, Technology, Engineering, and Mathematics (STEM) education researchers to explore and describe the experiences of students, educators, or other educational stakeholders. Some interviewing techniques use co-creation of an artifact, such as a personal timeline, as a unique way to elicit a detailed narrative from a respondent. The purpose of this commentary is to describe an interview artifact called a life grid. First used and validated in medical sociology to conduct life course research, we adapted the life grid for use in research on undergraduate STEM education. We applied the life grid interview technique to two contexts: 1) students in an advance degree program reflecting on their entire undergraduate career as a biology major, and 2) students in an undergraduate physics program reflecting on a multi-week lab project. Results: We found that the life grid supported four important attributes of an interview: facilitation of the respondents agency, establishment of rapport between interviewers and respondents, enhanced depth of the respondents narratives, and the construction of more accurate accounts of events. We situate our experiences with respect to those attributes and compare them with the experiences detailed in literature. Conclusions: We conclude with recommendations for future use of the life grid technique in undergraduate STEM education research. Overall, we find the life grid to be a valuable tool to use when conducting interviews about phenomena with a chronological component.
Mathematical reasoning skills are a desired outcome of many introductory physics courses, particularly calculus-based physics courses. Positive and negative quantities are ubiquitous in physics, and the sign carries important and varied meanings. Novices can struggle to understand the many roles signed numbers play in physics contexts, and recent evidence shows that unresolved struggle can carry over to subsequent physics courses. The mathematics education research literature documents the cognitive challenge of conceptualizing negative numbers as mathematical objects--both for experts, historically, and for novices as they learn. We contribute to the small but growing body of research in physics contexts that examines student reasoning about signed quantities and reasoning about the use and interpretation of signs in mathematical models. In this paper we present a framework for categorizing various meanings and interpretations of the negative sign in physics contexts, inspired by established work in algebra contexts from the mathematics education research community. Such a framework can support innovation that can catalyze deeper mathematical conceptualizations of signed quantities in the introductory courses and beyond.
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