The physics instruction at UC Davis for life science majors takes place in a long-standing reformed large-enrollment physics course in which the discussion or laboratory instructors (primarily graduate student teaching assistants) implement the interactive-engagement (IE) elements of the course. Because so many different instructors participate in disseminating the IE course elements, we find it essential to the instructors’ professional development to observe and document the student-instructor interactions within the classroom. Out of this effort, we have developed a computerized real-time instructor observation tool (RIOT) to take data of student-instructor interactions. We use the RIOT to observe 29 different instructors for 5 hours each over the course of one quarter, for a total of about 150 hours of class time, finding that the range of instructor behaviors is more extreme than previously assumed. In this paper, we introduce RIOT and describe how the variation present across 29 different instructors can provide students in the same course with significantly different course experiences.

Previous research into problem solving in physics resulted in researchers introducing six epistemic games to describe the organizational structures of locally coherent resources. We present a new epistemic game—the “answer-making epistemic game”—which was identified in this paper through the analysis of interviews carried out to validate a survey focusing on students’ understanding of Archimedes’ principle and Pascal’s law. In the game, the ultimate goal is a solution to a problem posed by the survey. Students may remember or intuit an answer, then use conceptual and/or mathematical reasoning to justify it. Alternately, they may use conceptual and/or mathematical reasoning to generate an answer. We demonstrate how students generate their solutions using these two different paths and discuss some implications for instruction.

We have investigated the various approaches taken by first-year university students ($n\approx 550$) when asked to determine the direction of motion, the constancy of speed, and a numerical value of the speed of an object at a point on a numerical linear distance-time graph. We investigated the prevalence of various well-known general graphing difficulties, such as graph-as-picture errors and slope-height confusion. We established that two-thirds of our students could determine the direction of motion with respect to a reference point, just under 80% could determine that the speed is constant, and just under 20% of our students could correctly determine the value of the speed; in the latter case, about half of the students divided the two coordinates. Three stable categories of correctly explaining the constancy of speed emerged from the data. We found that the reason given for determining that the speed of the object was constant did not correlate with successfully determining a value for the speed. We have established that technical difficulties such as determining the slope of any linear graph did not explain the poor performance. By comparing the answers to similar questions on water level versus time graphs, we were able to establish that context dependence and incorrect prior learning are likely to play a role. Post-test data are used to confirm the validity of the categorization and support the conclusion that being able to determine the slope of a $y,x$ graph and having a correct qualitative understanding of a distance-time graph is not sufficient to correctly determine a value for the speed.

In a previous paper that focused on the transmission of periodic waves at the boundary between two media, we documented difficulties with the basic concepts of wavelength, frequency, and propagation speed, and with the relationship $v=f\lambda$. In this paper, we report on student attempts to apply this relationship in problems involving two-source and thin-film interference. In both cases, interference arises from differences in the path lengths traveled by two waves. We found that some students (up to 40% on certain questions) had difficulty with a task that is fundamental to understanding these phenomena: expressing a physical distance, such as the separation between two sources, in terms of the wavelength of a periodic wave. We administered a series of questions to try to identify factors that influence student performance. We concluded that most incorrect responses stemmed from erroneous judgment about the type of reasoning required, not an inability to do said reasoning. A number of students do not seem to treat the spacing of moving wave fronts as analogous to immutable measurement tools (e.g., rulers).

Much research in engineering and physics education has focused on improving students’ problem-solving skills. This research has led to the development of step-by-step problem-solving strategies and grading rubrics to assess a student’s expertise in solving problems using these strategies. These rubrics value “communication” between the student’s qualitative description of the physical situation and the student’s formal mathematical descriptions (usually equations) at two points: when initially setting up the equations, and when evaluating the final mathematical answer for meaning and plausibility. We argue that (i) neither the rubrics nor the associated problem-solving strategies explicitly value this kind of communication during mathematical manipulations of the chosen equations, and (ii) such communication is an aspect of problem-solving expertise. To make this argument, we present a case study of two students, Alex and Pat, solving the same kinematics problem in clinical interviews. We argue that Pat’s solution, which connects manipulation of equations to their physical interpretation, is more expertlike than Alex’s solution, which uses equations more algorithmically. We then show that the types of problem-solving rubrics currently available do not discriminate between these two types of solutions. We conclude that problem-solving rubrics should be revised or repurposed to more accurately assess problem-solving expertise.

Earlier research has shown that after physics instruction, many students have difficulties with the force concept, and with constructing free-body diagrams (FBDs). It has been suggested that treating forces as interactions could help students to identify forces as well as to construct the correct FBDs. While there is evidence that identifying interactions helps students in quantitative problem solving, there is no previous research investigating the effect of a visual-representation tool—an interaction diagram (ID)—on students’ ability to identify forces, and to construct the correct FBDs. We present an empirical study conducted in 11 Finnish high schools on students ($n=335$, aged 16) taking their first, mandatory, introductory physics course. The study design involved groups of students having heavy, light, or no use of IDs. The heavy and light ID groups answered eight pairs of ID and FBD questions in various physical contexts and the no ID group answered two of the eight FBD questions. The results indicate that the heavy ID group outperformed both the light and the no ID groups in identifying forces and constructing the correct FBDs. The analysis of these data indicates that the use of IDs is especially beneficial in identifying forces when constructing FBDs.

This paper describes research on a classification of physics problems in the context of introductory physics courses. This classification, called the Taxonomy of Introductory Physics Problems (TIPP), relates physics problems to the cognitive processes required to solve them. TIPP was created in order to design educational objectives, to develop assessments that can evaluate individual component processes of the physics problem-solving process, and to guide curriculum design in introductory physics courses, specifically within the context of a “thinking-skills” curriculum. Moreover, TIPP enables future physics education researchers to investigate to what extent the cognitive processes presented in various taxonomies of educational objectives are exercised during physics problem solving and what relationship might exist between such processes. We describe the taxonomy, give examples of classifications of physics problems, and discuss the validity and reliability of this tool.

This study explores third-year preservice physics teachers’ ($n=32$) views concerning the objectives of practical work at school and university. Content analysis of their essays about practical work revealed not only the objectives of the practical work undertaken but also how they had experienced teaching as school and university students. The objectives most commonly referred to were related to the connections between theory and practice, motivation, understanding phenomena, learning how to observe, and learning how to report. In contrast, some objectives were recognized only rarely, which is an important issue for discussion as a future challenge. Preservice teachers’ positive experiences of practical work resulted from the successful implementation of practical work. According to our findings, practical work can in many cases be regarded as successful, especially when the participants understand the objectives of the teaching. In contrast, negative experiences reflected failures or difficulties in implementation. We conclude by suggesting that preservice teachers should be offered opportunities to reflect on their previous experiences and to see and experience in practice the advantages of practical work.

We describe the difficulties advanced undergraduate and graduate students have with concepts related to addition of angular momentum in quantum mechanics. We also describe the development and implementation of a research-based learning tool, Quantum Interactive Learning Tutorial (QuILT), to reduce these difficulties. The preliminary evaluation shows that the QuILT related to the basics of the addition of angular momentum is helpful in improving students’ understanding of these concepts.

We use visualization to find patterns in educational data. We represent student scores from high-stakes exams as flow vectors in fluids, define two types of streamlines and trajectories, and show that differences between streamlines and trajectories are due to regression to the mean. This issue is significant because it determines how quickly changes in long-term educational patterns can be deduced from score changes in consecutive years. To illustrate our methods, we examine a policy change in Texas that put increased pressure on public school students to pass several exams, and gave them resources to accomplish it. The response to this policy is evident from the changes in trajectories, although previous evaluation had concluded the program was ineffective. We pose the question of whether increased expenditure on education should be expected to correspond to improved student scores, or whether it should correspond to an increased rate of improvement in student scores.

We have given a group of 56 Massachusetts Institute of Technology (MIT) seniors who took mechanics as freshmen a written test similar to the final exam they took in their freshman course as well as the Mechanics Baseline Test (MBT) and the Colorado Learning Attitudes about Science Survey (CLASS). Students in majors unrelated to physics scored 60% lower on the written analytic part of the final than they would have as freshmen. The mean score of all participants on the MBT was insignificantly changed from their average on the posttest they took as freshmen. However, the students’ performance on 9 of the 26 MBT items (with 6 of the 9 involving graphical kinematics) represents a gain over their freshman posttest score (a normalized gain of about 70%), while their performance on the remaining 17 questions is best characterized as a loss of approximately 50% of the material learned in the freshman course. On multiple-choice questions covering advanced physics concepts, the mean score of the participants was about 50% lower than the average performance of freshmen. Although attitudinal survey results indicate that almost half the seniors feel the specific mechanics course content is unlikely to be useful to them, a significant majority (75%–85%) feel that physics does teach valuable problem solving skills, and an overwhelming majority believe that mechanics should remain a required course at MIT.

In this paper, we examine student success on three variants of a test item given in different representational formats (verbal, pictorial, and graphical), with an isomorphic problem statement. We confirm results from recent papers where it is mentioned that physics students’ problem-solving competence can vary with representational format and that solutions can be triggered by particular details of the representation. Previous studies are complemented with a fine grained analysis of solution strategies. We find that students use different problem-solving strategies, depending on the representational format in which the problem is stated.

A seminal study by Chi et al. firmly established the paradigm that novices categorize physics problems by “surface features” (e.g., “incline,” “pendulum,” “projectile motion,” etc.), while experts use “deep structure” (e.g., “energy conservation,” “Newton 2,” etc.). Yet, efforts to replicate the study frequently fail, since the ability to distinguish experts from novices turns out to be highly sensitive to the problem set being used. Exactly what properties of problems are most important in problem sets that discriminate experts from novices in a measurable way? To answer this question, we studied the categorizations by known physics experts and novices using a large, diverse set of problems. This set needed to be large so that we could determine how well experts and novices could be discriminated by considering both small subsets using an exhaustive Monte Carlo approach and larger subsets using simulated annealing. We found that the number of questions required to accurately classify experts and novices can be surprisingly small so long as the problem set is carefully crafted to be composed of problems with particular pedagogical and contextual features. Finally, we found that not only was what you ask (deep structure) important, but also how you ask it (problem context).

The Energy Project at Seattle Pacific University has developed representations that embody the substance metaphor and support learners in conserving and tracking energy as it flows from object to object and changes form. Such representations enable detailed modeling of energy dynamics in complex physical processes. We assess student learning by means of representations that learners invent to explain energy dynamics in specific real-world scenarios. Refined versions of these learner-generated representations have proven valuable for our own teaching, physics understanding, and research.

The nature of energy is not typically an explicit topic of physics instruction. Nonetheless, verbal and graphical representations of energy articulate models in which energy is conceptualized as a quasimaterial substance, a stimulus, or a vertical location. We argue that a substance ontology for energy is particularly productive in developing understanding of energy transfers and transformations. We analyze classic representations of energy—bar charts, pie charts, and others—to determine the energy ontologies that are implicit in those representations, and thus their affordances for energy learning. We find that while existing representations partially support a substance ontology for energy and thus the learning goal of energy conservation, they have limited utility for tracking the flow of energy among objects.

Technology has been widely involved in science research. Researchers are now applying it to science education in an attempt to bring students’ science activities closer to authentic science activities. The present study synthesizes the research to discuss the development of technology-enhanced laboratories and how technology may contribute to fulfilling the instructional objectives of laboratories in physics. To be more specific, this paper discusses the engagement of technology to innovate physics laboratories and the potential of technology to promote inquiry, instructor and peer interaction, and learning outcomes. We then construct a framework for teachers, scientists, and programmers to guide and evaluate technology-integrated laboratories. The framework includes inquiry learning and openness supported by technology, ways of conducting laboratories, and the diverse learning objectives on which a technology-integrated laboratory may be focused.

This study examined the evolution of student responses to seven contextually different versions of two Force Concept Inventory questions in an introductory physics course at the University of Arkansas. The consistency in answering the closely related questions evolved little over the seven-question exam. A model for the state of student knowledge involving the probability of selecting one of the multiple-choice answers was developed. Criteria for using clustering algorithms to extract model parameters were explored and it was found that the overlap between the probability distributions of the model vectors was an important parameter in characterizing the cluster models. The course data were then clustered and the extracted model showed that students largely fit into two groups both pre- and postinstruction: one that answered all questions correctly with high probability and one that selected the distracter representing the same misconception with high probability. For the course studied, 14% of the students were left with persistent misconceptions post instruction on a static force problem and 30% on a dynamic Newton’s third law problem. These students selected the answer representing the predominant misconception slightly more consistently postinstruction, indicating that the course studied had been ineffective at moving this subgroup of students nearer a Newtonian force concept and had instead moved them slightly farther away from a correct conceptual understanding of these two problems. The consistency in answering pairs of problems with varied physical contexts is shown to be an important supplementary statistic to the score on the problems and suggests that the inclusion of such problem pairs in future conceptual inventories would be efficacious. Multiple, contextually varied questions further probe the structure of students’ knowledge. To allow working instructors to make use of the additional insight gained from cluster analysis, it is our hope that the physics education research community will make these methods available though their Web sites.

We present a method of analyzing the development of self-efficacy in real time using a framework of self-efficacy opportunities (SEOs). Considerable research has shown a connection between self-efficacy, or the confidence in one’s own ability to perform a task, and success in science fields. Traditional methods of investigating the development of self-efficacy have required participants to recollect past events. This reliance on participant memory makes it difficult to understand what impact particular events may have on developing self-efficacy in the moment. We use video recordings of three undergraduate Modeling Instruction students solving a physics problem to characterize SEOs in a moment-by-moment analysis. We then validate these characterizations of the development of self-efficacy by reviewing the problem-solving session with the participants and find evidence that the SEOs identified are taken up and impact self-efficacy. This characterization and validation of SEOs in the moment represents a first step towards establishing a methodology for analyzing the development of self-efficacy in real time.

“Self-diagnosis tasks” aim at fostering students’ learning in an examination context by requiring students to present diagnoses of their solutions to quiz problems. We examined the relationship between students’ learning from self-diagnosis and the typicality of the problem situation. Four recitation groups in an introductory physics class ($\sim 200$ students) were divided into a control group and three intervention groups in which different levels of guidance were provided to aid students in their performance of self-diagnosis activities. The self-diagnosis task was administered twice, first in an atypical problem situation and then in a typical one. In a companion paper we reported our findings in the context of an atypical problem situation. Here we report our findings in the context of a typical problem situation and discuss the effect of problem typicality on students’ self-diagnosis performance and subsequent success in solving transfer problems. We show that the self-diagnosis score was correlated with subsequent problem-solving performance only in the context of a typical problem situation, and only when textbooks and notebooks were the sole means of guidance available to the students for assisting them with diagnosis.