Many of the activities and cognitive processes that physicists use while solving problems are invisible to students, which can hinder their acquisition of important expert-like skills. Whereas the detailed calculations performed by researchers are often published in journals and textbooks, other activities such as those undertaken while planning how to approach a problem are rarely discussed in published research. Hence, these activities are especially hidden from students. To better understand how physicists solve problems in their professional research, we leveraged the framework of cognitive task analysis to conduct semi-structured interviews with theoretical physicists (N = 11). Here we elucidate the role of planning and preliminary analysis in theorists' work. Theorists described using a variety of activities in order to decide if their project was doable while also generating possible solution paths. These actions included doing preliminary calculations, reflecting on previous knowledge, gaining intuition and understanding by studying prior work, and reproducing previous results. We found that theorists typically did not pursue projects unless they had a clear idea of what the outcome of their project would be, or at least knew that they would be able to make progress on the problem. Thus, this preliminary design and analysis phase was highly important for theorists despite being largely hidden from students. We conclude by suggesting potential ways to incorporate our findings into the classroom to give students more numerous opportunities to engage in these expert-like practices.

Learning physics in any context, including undergraduate research experiences, requires learning its concepts and the relational structure between those new concepts with what students already know. We use concept maps, a knowledge elicitation method, for assessing mentees' and mentors' knowledge structures during Research Experience for Undergraduates programs. The study looked at maps from seven mentor-mentee pairs to understand how mentors and mentees use specific knowledge and strategies during the development of their concept maps. A qualitative analysis of the maps showed mentors and mentees differed in their ways of organizing and displaying their knowledge in terms of structure, scale, language, and use of conceptual and procedural knowledge. For instance, mentees used more procedural knowledge. It is perhaps due to their perception of finishing their Research Experiences for Undergraduates (REU) projects and the fact that they may have only limited and superficial knowledge of specific topics. However, mentors maps were smaller but more significant in using more comprehensive conceptual knowledge and connecting their maps to the broader scientific context.

Using unique, new, matched UMETRICS data on people employed on research projects and Author-ity data on biomedical publications, this paper shows that National Institutes of Health funding stimulates research by supporting the teams that conduct it. While faculty-both principal investigators (PIs) and other faculty-and their productivity are heavily affected by funding, so are trainees and staff. The largest effects of funding on research output are ripple effects on publications that do not include PIs. While funders focus on research output from projects, they would be well advised to consider how funding ripples through the wide range of people, including trainees and staff, employed on projects.

Current debate surrounds the promise of neuroscience for education, including whether learning-related neural changes can predict learning transfer better than traditional performance-based learning assessments. Long-standing debate in philosophy and psychology concerns the proposition that spatial processes underlie seemingly nonspatial/verbal reasoning (mental model theory). If so, education that fosters spatial cognition might improve verbal reasoning. Here, in a quasi-experimental design in real-world STEM classrooms, a curriculum devised to foster spatial cognition yielded transfer to improved verbal reasoning. Further indicating a spatial basis for verbal transfer, students' spatial cognition gains predicted and mediated their reasoning improvement. Longitudinal fMRI detected learning-related changes in neural activity, connectivity, and representational similarity in spatial cognition-implicated regions. Neural changes predicted and mediated learning transfer. Ensemble modeling demonstrated better prediction of transfer from neural change than from traditional measures (tests and grades). Results support in-school spatial education and suggest that neural change can inform future development of transferable curricula.

ABSTRACT The global spread of the novel coronavirus first reported in December 2019 led to drastic changes in the social and economic dynamics of everyday life. Nationwide, racial, gender, and geographic disparities in symptom severity, mortality, and access to health care evolved, which impacted stress and anxiety surrounding COVID-19. On university campuses, drastic shifts in learning environments occurred as universities shifted to remote instruction, which further impacted student mental health and anxiety. Our study aimed to understand how students from diverse backgrounds differ in their worry and stress surrounding COVID-19 upon return to hybrid or in-person classes during the Fall of 2020. Specifically, we addressed the differences in COVID-19 worry, stress response, and COVID-19-related food insecurity related to race/ethnicity (Indigenous American, Asian/Asian American, black/African American, Latinx/Hispanic, white, or multiple races), gender (male, female, and gender expressive), and geographic origin (ranging from rural to large metropolitan areas) of undergraduate students attending a regional-serving R2 university, in the southeastern U.S. Overall, we found significance in worry, food insecurity, and stress responses with females and gender expressive individuals, along with Hispanic/Latinx, Asian/Asian American, and black/African American students. Additionally, students from large urban areas were more worried about contracting the virus compared to students from rural locations. However, we found fewer differences in self-reported COVID-related stress responses within these students. Our findings can highlight the disparities among students’ worry based on gender, racial differences, and geographic origins, with potential implications for mental health of university students from diverse backgrounds. Our results support the inclusion of diverse voices in university decisioning making around the transition through the COVID-19 pandemic.

The ability to identify whether a user is zoning out (mind wandering) from video has many HCI (e.g., distance learning, high-stakes vigilance tasks). However, it remains unknown how well humans can perform this task, how they compare to automatic computerized approaches, and how a fusion of the two might improve accuracy. We analyzed videos of users' faces and upper bodies recorded 10s prior to self-reported mind wandering (i.e., ground truth) while they engaged in a computerized reading task. We found that a state-of-the-art machine learning model had comparable accuracy to aggregated judgments of nine untrained human observers (area under receiver operating characteristic curve [AUC] =.598 versus .589). A fusion of the two (AUC = .644) outperformed each, presumably because each focused on complementary cues. Furthermore, adding more humans beyond 3-4 observers yielded diminishing returns. We discuss implications of human-computer fusion as a means to improve accuracy in complex tasks.

Personalized learning stems from the idea that students benefit from instructional material tailored to their needs. Many online learning platforms purport to implement some form of personalized learning, often through on-demand tutoring or self-paced instruction, but to our knowledge none have a way to automatically explore for specific opportunities to personalize students' education nor a transparent way to identify the effects of personalization on specific groups of students. In this work we present the Automatic Personalized Learning Service (APLS). The APLS uses multi-armed bandit algorithms to recommend the most effective support to each student that requests assistance when completing their online work, and is currently used by ASSISTments, an online learning platform. The first empirical study of the APLS found that Beta-Bernoulli Thompson Sampling, a popular and effective multi-armed bandit algorithm, was only slightly more capable of selecting helpful support than randomly selecting from the relevant support options. Therefore, we also present Decision Tree Thompson Sampling (DTTS), a novel contextual multi-armed bandit algorithm that integrates the transparency and interpretability of decision trees into Thomson sampling. In simulation, DTTS overcame the challenges of recommending support within an online learning platform and was able to increase students' learning by as much as 10% more than the current algorithm used by the APLS. We demonstrate that DTTS is able to identify qualitative interactions that not only help determine the most effective support for students, but that also generalize well to new students, problems, and support content. The APLS using DTTS is now being deployed at scale within ASSISTments and is a promising tool for all educational learning platforms. [This paper was published in: "Proceedings of the Ninth ACM Conference on Learning @ Scale (L@S '22), June 1-3, 2022, New York City, NY, USA," ACM, 2022.]

Analyzing classroom video data provides valuable insights about the interactions between students and teachers, albeit often through time-consuming qualitative coding or the use of bespoke sensors to record individual movement information. We explore measuring classroom posture and movement in secondary classroom video data through computer vision methods (especially OpenPose), and introduce a simple but effective approach to automatically track movement via post-processing of OpenPose output data. Analysis of 67 videos of mathematics classes from middle school and high school levels highlighted the challenges associated with analyzing movement in typical classroom videos: occlusion from low camera angles, difficulty detecting lower body movement due to sitting, and the close proximity of students to one another and their teachers. Despite these challenges, our approach tracked person IDs across classroom videos for 93.0% of detected individuals. The tracking results were manually verified through randomly sampling 240 instances, which revealed notable OpenPose tracking inconsistencies. Finally, we discuss the implications for supporting more scalability of video data classroom movement analysis, and future potential explorations.

We investigated the generalizability of language-based analytics models across two collaborative problem solving (CPS) tasks: an educational physics game and a block programming challenge. We analyzed a dataset of 95 triads (N=285) who used videoconferencing to collaborate on both tasks for an hour. We trained supervised natural language processing classifiers on automatic speech recognition transcripts to predict the human-coded CPS facets (skills) of constructing shared knowledge, negotiation / coordination, and maintaining team function. We tested three methods for representing collaborative discourse: (1) deep transfer learning (using BERT), (2) n-grams (counts of words/phrases), and (3) word categories (using the Linguistic Inquiry Word Count [LIWC] dictionary). We found that the BERT and LIWC methods generalized across tasks with only a small degradation in performance (Transfer Ratio of.93 with 1 indicating perfect transfer), while the n-grams had limited generalizability (Transfer Ratio of.86), suggesting overfitting to task-specific language. We discuss the implications of our findings for deploying language-based collaboration analytics in authentic educational environments.