In interviews with physics students and early career physicists, we ask about their experiences with having impairments in the physics setting and physics culture. In this paper, we highlight how experiences shared by participants as disabled people in physics represent clusters of models of disability. Specifically, we apply a theoretical framing of a three-dimensional disability model space, with axes defined as medical versus social (i.e., cause); tragedy versus affirmative (i.e., effect); and minority group versus universal (i.e., ability/disability dichotomy). For example, in this framework, providing accommodations is described by a cluster of the social and minority models of disability. By analyzing participants' experiences in physics through this disability framework, we aim to identify the models that underpin supportive experiences and support the development of policies and professional development for the physics community towards benefiting disabled people. Through analysis and comparison of these models and participants' narratives, we offer a discussion and possible guidelines for instructors interacting with students with disabilities, opportunities for those with disabilities to deconstruct their own prior experiences and analyze potential misinterpretations that may arise from the models.

Designing physics courses that support students' activation and development of expert-like physics epistemologies is a significant goal of Physics Education Research. However, very little research has focused on how physics students' interactions with course structures resonate with different epistemological views. As part of a course redesign effort to increase student success in introductory physics, we interviewed introductory physics students about their experiences with course structures and their learning and belonging beliefs. We present here a case from this broader data corpus in which a student, Robyn, discusses his epistemological views of physics problem solving and his experiences with physics lectures, office hours, and discussion sections. We find that Robyn's physics epistemology manifests consistently across his interactions with each of these different course structures, suggesting a possible resonance between students' beliefs and their experiences with course structures and the value of further investigation into the potential merits of comprehensive course design.

Analogies are known to be powerful tools for making sense of unfamiliar ideas in terms of already understood concepts. Science students regularly encounter unfamiliar ideas, such as microscopic objects that are invisible to our everyday experience and behaviors dictated by quantum mechanics. An understanding of basic concepts of quantum mechanics is useful in many disciplines, especially with the growing field of quantum information sciences and technologies. Physics researchers often use analogies in their own research and science communicators use them to make quantum ideas accessible to K-12 students and across STEM disciplines, but analogy use in upper-division teaching has been less researched. Our research goal is to understand how analogies are used to teach quantum mechanics, and specifically, what prior knowledge is used as a basis for analogies within two widely used quantum mechanics textbooks. This textbook analysis shows the most common bases for analogies include: mathematical structures from linear algebra, which are applied to model quantum systems; everyday life examples, which are used to make quantum systems more familiar and understandable; and macroscopic classical phenomena, which are used to highlight differences between classical and quantum mechanics. We also find authors use different conventions, based on the various cue words that authors use to indicate analogy-based reasoning. In the STEM classroom, this research has implications for enhancing student learning about abstract topics in science.

An understanding of vectors and vector operations is crucial for success in physics, as this serves as the foundation for various essential concepts, including motion and forces. Previous research indicates that only a fraction of introductory physics students have a usable knowledge of vectors and vector operations, and that more attention should be given to how students make sense of vectors. We examined classroom video data from an introductory physics course wherein students worked collaboratively through learning activities to introduce vectors and vector operations. During these activities, students' employment of gesture as a representational mode facilitated group sense-making. We propose a preliminary taxonomy of gestures for representing vector magnitudes, directions, and initial and terminal points. By identifying and characterizing the gestures used by students, we can gain insights into their learning processes and conceptual understanding of vectors, which can inform instructional design and teaching practices.

Code tracing is a foundational programming skill that involves simulating a program’s execution line by line, tracking how variables change at each step. To code trace, students need to understand what a given program line means, which can be accomplished by translating it into plain English. Translation can be characterized as a form of self-explanation, a general learning mechanism that involves making inferences beyond the instructional materials. Our work investigates if this form of self-explanation improves learning from a code-tracing tutor we created using the CTAT framework. We created two versions of the tutor. In the experimental version, students were asked to translate lines of code while solving code-tracing problems. In the control condition students were only asked to code trace without translating. The two tutor versions were compared using a between-subjects study (N = 44). The experimental group performed significantly better on translation and code-generation questions, but the control group performed significantly better on code-tracing questions. We discuss the implications of this finding for the design of tutors providing code-tracing support. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

There is growing recognition that AI technologies can, and should, support collaborative learning. To provide this support, we need models of collaborative talk that reflect the ways in which learners interact. Great progress has been made in modeling dialogue for high school and college-age learners, but the dialogue processes that characterize collaborative talk between elementary learner dyads are not currently well understood. This paper reports on a study with elementary school learners (4th and 5th grade, ages 9–11 years old) coded collaboratively in dyads. We recorded dialogue from 22 elementary school learner dyads, covering 7594 total utterances. We labeled this corpus manually with dialogue acts and then induced a hidden Markov model to identify the underlying dialogue states and the transitions between these states. The model identified six distinct hidden states which we interpret as Social Dialogue, Confusion, Frustrated Coordination, Exploratory Talk, Directive & Disagreement, and Disagreement & Self-Explanation. The HMM revealed that when students entered into a productive exploratory talk state, the primary way they transitioned out of this state is when they became confused or reached an impasse. When this occurred, the learners then moved into states of disputation and conflict before re-entering the Exploratory Talk state. These findings can inform the design of AI agents who support young learners’ collaborative talk and help agents determine when students are conflicting rather than collaborating. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Assigning concept tags to questions enables Intelligent tutoring systems (ITS) to efficiently organize resources, help identify students’ strengths and weaknesses, and recommend suitable learning materials accordingly. Manual tagging is time-consuming, and inefficient for large question banks, and could lead to consistency issues due to differences in the perspectives of individual taggers. Automatic tagging techniques can efficiently generate consistent tags at lower costs. Generating automatic tags for mathematical questions is challenging as the question text is usually short and concise, and the question as well as the answer text contains mathematical symbols and formulas. However, prior works have not studied this problem extensively. In this context, we conducted a study in a graduate-level linear algebra course to understand if student explanations to solving mathematical problems can be employed to generate concept tags associated with those questions. In this paper, we propose a method called Unsupervised Skill Tagging (UST) to extract concept tags associated with a given question from explanation text. Using UST on the explanations generated, we show that the explanations indeed contain the expert-specified concept tags. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A promising way to mitigate inequality is by addressing students' worries about belonging. But where and with whom is this social-belonging intervention effective? Here we report a team-science randomized controlled experiment with 26,911 students at 22 diverse institutions. Results showed that the social-belonging intervention, administered online before college (in under 30 minutes), increased the rate at which students completed the first year as full-time students, especially among students in groups that had historically progressed at lower rates. The college context also mattered: The intervention was effective only when students' groups were afforded opportunities to belong. This study develops methods for understanding how student identities and contexts interact with interventions. It also shows that a low-cost, scalable intervention generalizes its effects to 749 4-year institutions in the United States.

This comparative case study explores how 18 state education agencies (SEAs) support school districts in advancing standards-based elementary science reform. We identify how SEAs understand their work in advancing elementary science reform and describe how SEAs sought to engage districts in bridging from standards to classroom practice. Based on our analysis, we argue that the school subject is a critical explanatory variable in understanding SEA efforts to support standards implementation and SEAs lean on a resource-based approach for instructional policy implementation. This study contributes to the growing research base on the role of state policy in supporting standards implementation.

The coding of text information to recognize the teaching beliefs of outstanding professors is crucial research to enhance teaching performance in university. Most previous studies adopted manual coding, and thus text information was limited to briefly descriptive statements or questionnaires, rather than full narrative stories of outstanding professors, owing to the time-consuming of manual coding. However, outstanding professors’ narrative stories, which contained more detailed information about the outstanding professors’ thinking and behaviors, were valuable text information to recognize the types of teaching beliefs of outstanding professors. Therefore, to overcome the time-consuming obstacle of manual coding, this study proposes a machine-learning-based approach, which exploits BERT with convolutional LSTM, to code narrative stories of outstanding teachers for the identification of the types of teaching beliefs of outstanding professors. In this study, the text information used for coding was a series of fourteen books published across fourteen years, namely The Stories of Outstanding Professors in National Taiwan University (NTU), which contained one million words describing the stories of three hundred NTU outstanding professors. Towards identifying six categories and thirty subcategories of teaching beliefs revealed in the narrative stories, our approach outperforms comparative methods by 1% to 86% in the F1 score. Comprehensive evaluations validate the effectiveness of our approach in assisting in not only recognizing the teaching beliefs from stories of outstanding professors, but also the process of coding text information from narrative stories. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.