In order to support physics students in their future careers, there is a need to understand the relationship between undergraduate education and professional practice in physics-related fields. This study investigated high-level goal driven mathematical problem-solving activities that are found within two disciplinary cultures: physical science research labs in academia and photonics workplaces in industry. We conducted semistructured interviews with 10 Ph.D. students and 22 engineers and technicians. Math use in professional workplaces was characterized through an adaptation of epistemic games framework, which revealed six common epistemic games in these workplaces: conceptual math modeling, analytical-numerical math modeling, design-oriented math modeling, fabrication, improving processes, and making meaning out of data games. The workplace-specific epistemic games capture the goals, starting and ending conditions, constraints and contextual features, moves, tools, and representations. The games involve a broad spectrum of math that ranges from arithmetic to computational modeling. The games reveal how goals and particular contextual features impact approaches to mathematical problem solving. The findings extend prior work on mathematical problem solving in physics to a new population of professional researchers, engineers, and technicians in their workplaces. The research may guide new approaches for developing problems and explicitly teaching problem solving in diverse physics contexts, which may additionally benefit undergraduate students' preparation for their future careers.

Many studies have examined the structure and properties of the Force Concept Inventory (FCI); however, far less research has investigated the Force and Motion Conceptual Evaluation (FMCE). This study applied Multidimensional Item Response Theory (MIRT) to a sample of N=4528 FMCE post-test responses. Exploratory factor analysis showed that 5, 9, and 10-factor models optimized some fit statistics. The FMCE uses extensive blocking of items into groups with a common stem; these blocks factored together in most models. A confirmatory analysis, which constrained the MIRT models to a theoretical model constructed from expert solutions, produced a model requiring only 8 principles, fundamental reasoning steps. This was substantially fewer than the 19 principles identified in the FCI by a previous study. Correlation analysis also demonstrated that the two instruments were very dissimilar. The reduced number of principles and the repetition of items using a single principle allowed the extraction of eight single-principle subscales, seven with Cronbach's alpha greater than the 0.7 required for acceptable internal consistency. The differences between the FCI and the FMCE suggest that the two instruments could provide complementary, but different, information about student understanding of Newton's laws with the FCI measuring an integrated Newtonian force concept and the FMCE measuring details of that force concept. © 2019 authors. Published by the American Physical Society. Published by the American Physical Society under the terms of the "https://creativecommons.org/licenses/by/4.0/" Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

Purpose - This paper aims to lay out the goals and challenges in using information for ambitious learning practices. Design/methodology/approach - Through a review of the literature, the authors integrate across learning, information sciences and instructional design to identify challenges and possibilities for information searching and sense-making in ambitious learning practices (ALPs). Findings - Learners face a number of challenges in using information in ALPs such as a problem-based learning. These include searching and sourcing, selecting information and sense-making. Although ALPs can be effective, providing appropriate scaffolding, supports and resources is essential. Originality/value - To make complex ALPs available to a wide range of learners requires considering the information literacy demands and how these can be supported. This requires deep understanding and integration across different research literature areas to move toward solutions.

Purpose: The purpose of this study is to explore the experience of selecting and engaging in biological sciences laboratory rotations from the perspective of doctoral students. Design/methodology/approach: Within the socialization framework, this study uses a qualitative approach whereby 42 biological sciences students enrolled at highly selective US universities were interviewed in the first and second year of doctoral training about laboratory rotation experiences. Findings: The study revealed how doctoral students used formal and informal information networks, explored research topics, struggled with funding concerns and learned about the social aspect of the laboratories in which they rotated. Originality/value: While rotations are considered a signature pedagogy in the laboratory sciences, students’ experiences within them are understudied. This study offers new knowledge about what doctoral students experience while rotating that can be used to inform and improve rotation processes for both students and universities. © 2019, Emerald Publishing Limited.

Students' academic learning, performance, and motivation are ongoing topics in engineering education. Those studies that have attempted to understand the mechanisms of motivation in authentic classroom settings and scenarios are few and limited to the methods used (e.g., self-reports, observations). This Work-in-Progress study explores the utility of electrodermal activity (EDA) and temperature sensors in accurately informing scholars about student performance during an exam in real-time. Correlations between each factor were analyzed. Initial results suggest that peripheral skin temperature has a weak, positive hut significant correlation to exam question difficulty r=0.08; p<0.001). Also, electrodermal activity and temperature showed a weak, positive, but significant correlation (r-0.13; p<0.0.5). The electrodermal activity showed a weak, positive, but significant correlation to exam question difficulty (:-0.16; p<0.01). Also, skin temperature correlations with difficulty index (did not) changed across semesters (r-0.18; p<0.001). We also developed a multiple regression model and found moderately significant relationships between EDA, difficulty index, and skin temperature (r=0.45; p<0.05). The findings suggest that performance is tied to physiological responses among students during exam taking, indicating a possible connection between emotions and cognition via physiology.

The increased usage of computer-based learning platforms and online tools in classrooms presents new opportunities to not only study the underlying constructs involved in the learning process, but also use this information to identify and aid struggling students. Many learning platforms, particularly those driving or supplementing instruction, are only able to provide aid to students who interact with the system. With this in mind, student persistence emerges as a prominent learning construct contributing to students success when learning new material. Conversely, high persistence is not always productive for students, where additional practice does not help the student move toward a state of mastery of the material. In this paper, we apply a transfer learning methodology using deep learning and traditional modeling techniques to study high and low representations of unproductive persistence. We focus on two prominent problems in the fields of educational data mining and learner analytics representing low persistence, characterized as student stopout, and unproductive high persistence, operationalized through student wheel spinning, in an effort to better understand the relationship between these measures of unproductive persistence (i.e., stopout and wheel spinning) and develop early detectors of these behaviors. We find that models developed to detect each within and across-assignment stopout and wheel spinning are able to learn sets of features that generalize to predict the other. We further observe how these models perform at each learning opportunity within student assignments to identify when interventions may be deployed to best aid students who are likely to exhibit unproductive persistence.

Many applications of computer vision require robust systems that can parse complex structures as they evolve in time. Using a block construction task as a case study, we illustrate the main components involved in building such systems. We evaluate performance at three increasingly-detailed levels of spatial granularity on two multimodal (RGBD + IMU) datasets. On the first, designed to match the assumptions of the model, we report better than 90% accuracy at the finest level of granularity. On the second, designed to test the robustness of our model under adverse, real-world conditions, we report 67% accuracy and 91% precision at the mid-level of granularity. We show that this seemingly simple process presents many opportunities to expand the frontiers of computer vision and action recognition.

Questioning is a core component of formal pedagogy. Parents commonly question children, but do they use questions to teach? This article defines pedagogical questions as questions for which the questioner already knows the answer and intended to help the questionee learn. Transcripts of parent-child conversations were collected from the CHILDES database to examine the frequency and distribution of pedagogical questions. Analysis of 2,166 questions from 166 mother-child dyads and 64 father-child dyads (child's age between 2 and 6 years) showed that pedagogical questions are commonplace during day-to-day parent-child conversations and vary based on child's age, family environment, and historical era. The results serve as a first step toward understanding the role of parent-child questions in facilitating children's learning.

Whether and to what extent kindergarten children's executive functions (EF) constitute promising targets of early intervention is currently unclear. This study examined whether kindergarten children's EF predicted their second-grade academic achievement and behavior. This was done using (a) a longitudinal and nationally representative sample (N = 8,920, Mage = 97.6 months), (b) multiple measures of EF, academic achievement, and behavior, and (c) extensive statistical control including for domain-specific and domain-general lagged dependent variables. All three measures of EF—working memory, cognitive flexibility, and inhibitory control—positively and significantly predicted reading, mathematics, and science achievement. In addition, inhibitory control negatively predicted both externalizing and internalizing problem behaviors. Children's EF constitute promising targets of experimentally evaluated interventions for increasing academic and behavioral functioning. © 2018 Society for Research in Child Development

We hypothesize that effective collaboration is facilitated when individuals and environmental components form a synergy where they work together and regulate one another to produce stable patterns of behavior, or regularity, as well as adaptively reorganize to form new behaviors, or irregularity. We tested this hypothesis in a study with 32 triads who collaboratively solved a challenging visual computer programming task for 20 min following an introductory warm-up phase. Multidimensional recurrence quantification analysis was used to examine fine-grained (i.e., every 10 s) collective patterns of regularity across team members' speech rate, body movement, and team interaction with the shared user interface. We found that teams exhibited significant patterns of regularity as compared to shuffled baselines, but there were no systematic trends in regularity across time. We also found that periods of regularity were associated with a reduction in overall behavior. Notably, the production of irregular behavior predicted expert-coded metrics of collaborative activity, such as teams' ability to construct shared knowledge and effectively negotiate and coordinate execution of solutions, net of overall behavioral production and behavioral self-similarity. Our findings support the theory that groups can interact to form interpersonal synergies and indicate that information about system-level dynamics is a viable way to understand and predict effective collaborative processes.