Many undergraduate students encounter struggle as they navigate academic, financial, and social contexts of higher education. The transition to emergency online instruction during the Spring of 2020 due to the COVID-19 pandemic exacerbated these struggles. To assess college students’ struggles during the transition to online learning in undergraduate biology courses, we surveyed a diverse collection of students (n = 238) at an R2 research institution in the Southeastern United States. Students were asked if they encountered struggles and whether they were able to overcome them. Based on how students responded, they were asked to elaborate on (1) how they persevered without struggle, (2) how they were able to overcome their struggles, or (3) what barriers they encountered that did not allow them to overcome their struggles. Each open-ended response was thematically coded to address salient patterns in students’ ability to either persevere or overcome their struggle. We found that during the transition to remote learning, 67% of students experienced struggle. The most reported struggles included: shifts in class format, effective study habits, time management, and increased external commitments. Approximately, 83% of those struggling students were able to overcome their struggle, most often citing their instructor’s support and resources offered during the transition as reasons for their success. Students also cited changes in study habits, and increased confidence or belief that they could excel within the course as ways in which they overcame their struggles. Overall, we found no link between struggles in the classroom and any demographic variables we measured, which included race/ethnicity, gender expression, first-generation college students, transfer student status, and commuter student status. Our results highlight the critical role that instructors play in supporting student learning during these uncertain times by promoting student self-efficacy and positive-growth mindset, providing students with the resources they need to succeed, and creating a supportive and transparent learning environment. Copyright © 2022 Cannon, Clance, Correia, Morrison, Nelson and Henning.

IntroductionThe Framework for K-12 Science Education promotes supporting the development of knowledge application skills along previously validated learning progressions (LPs). Effective assessment of knowledge application requires LP-aligned constructed-response (CR) assessments. But these assessments are time-consuming and expensive to score and provide feedback for. As part of artificial intelligence, machine learning (ML) presents an invaluable tool for conducting validation studies and providing immediate feedback. To fully evaluate the validity of machine-based scores, it is important to investigate human-machine score consistency beyond observed scores. Importantly, no formal studies have explored the nature of disagreements between human and machine-assigned scores as related to LP levels. MethodsWe used quantitative and qualitative approaches to investigate the nature of disagreements among human and scores generated by two approaches to machine learning using a previously validated assessment instrument aligned to LP for scientific argumentation. ResultsWe applied quantitative approaches, including agreement measures, confirmatory factor analysis, and generalizability studies, to identify items that represent threats to validity for different machine scoring approaches. This analysis allowed us to determine specific elements of argumentation practice at each level of the LP that are associated with a higher percentage of misscores by each of the scoring approaches. We further used qualitative analysis of the items identified by quantitative methods to examine the consistency between the misscores, the scoring rubrics, and student responses. We found that rubrics that require interpretation by human coders and items which target more sophisticated argumentation practice present the greatest threats to the validity of machine scores. DiscussionWe use this information to construct a fine-grained validity argument for machine scores, which is an important piece because it provides insights for improving the design of LP-aligned assessments and artificial intelligence-enabled scoring of those assessments.

IntroductionThe Framework for K-12 Science Education (the Framework) and the Next- Generation Science Standards (NGSS) define three dimensions of science: disciplinary core ideas, scientific and engineering practices, and crosscutting concepts and emphasize the integration of the three dimensions (3D) to reflect deep science understanding. The Framework also emphasizes the importance of using learning progressions (LPs) as roadmaps to guide assessment development. These assessments capable of measuring the integration of NGSS dimensions should probe the ability to explain phenomena and solve problems. This calls for the development of constructed response (CR) or open-ended assessments despite being expensive to score. Artificial intelligence (AI) technology such as machine learning (ML)-based approaches have been utilized to score and provide feedback on open-ended NGSS assessments aligned to LPs. ML approaches can use classifications resulting from holistic and analytic coding schemes for scoring short CR assessments. Analytic rubrics have been shown to be easier to evaluate for the validity of ML-based scores with respect to LP levels. However, a possible drawback of using analytic rubrics for NGSS-aligned CR assessments is the potential for oversimplification of integrated ideas. Here we describe how to deconstruct a 3D holistic rubric for CR assessments probing the levels of an NGSS-aligned LP for high school physical sciences. MethodsWe deconstruct this rubric into seven analytic categories to preserve the 3D nature of the rubric and its result scores and provide subsequent combinations of categories to LP levels. ResultsThe resulting analytic rubric had excellent human- human inter-rater reliability across seven categories (Cohen's kappa range 0.82-0.97). We found overall scores of responses using the combination of analytic rubric very closely agreed with scores assigned using a holistic rubric (99% agreement), suggesting the 3D natures of the rubric and scores were maintained. We found differing levels of agreement between ML models using analytic rubric scores and human-assigned scores. ML models for categories with a low number of positive cases displayed the lowest level of agreement. DiscussionWe discuss these differences in bin performance and discuss the implications and further applications for this rubric deconstruction approach.

Dilated cardiomyopathy (DCM) is the third most common cause of heart failure and the primary reason for heart transplantation; upward of 70% of DCM cases are considered idiopathic. Our in-vitro experiments showed that reduced hybrid/complex N-glycosylation in mouse cardiomyocytes is linked with DCM. Further, we observed direct effects of reduced N-glycosylation on K-v gating. However, it is difficult to rigorously determine the effects of glycosylation on K-v activity, because there are multiple K-v isoforms in cardiomyocytes contributing to the cardiac excitation. Due to complex functions of K-v isoforms, only the sum of K+ currents (I-Ksum) can be recorded experimentally and decomposed later using exponential fitting to estimate component currents, such as I-Kto, I-Kslow, and I-Kss. However, such estimation cannot adequately describe glycosylation effects and K-v mechanisms. Here, we propose a framework of simulation modeling of K-v kinetics in mouse ventricular myocytes and model calibration using the in-vitro data under normal and reduced glycosylation conditions through ablation of the Mgat1 gene (i.e., Mgat1KO). Calibrated models facilitate the prediction of K-v characteristics at different voltages that are not directly observed in the in-vitro experiments. A model calibration procedure is developed based on the genetic algorithm. Experimental results show that, in the Mgat1KO group, both I-Kto and I-Kslow densities are shown to be significantly reduced and the rate of I-Kslow inactivation is much slower. The proposed approach has strong potential to couple simulation models with experimental data for gaining a better understanding of glycosylation effects on K-v kinetics.

Self-regulated learning (SRL) is critical for learning across tasks, domains, and contexts. Despite its importance, research shows that not all learners are equally skilled at accurately and dynamically monitoring and regulating their self-regulatory processes. Therefore, learning technologies, such as intelligent tutoring systems (ITSs), have been designed to measure and foster SRL. This paper presents an overview of over 10 years of research on SRL with MetaTutor, a hypermedia-based ITS designed to scaffold college students' SRL while they learn about the human circulatory system. MetaTutor's architecture and instructional features are designed based on models of SRL, empirical evidence on human and computerized tutoring principles of multimedia learning, Artificial Intelligence (AI) in educational systems for metacognition and SRL, and research on SRL from our team and that of other researchers. We present MetaTutor followed by a synthesis of key research findings on the effectiveness of various versions of the system (e.g., adaptive scaffolding vs. no scaffolding of self-regulatory behavior) on learning outcomes. First, we focus on findings from self-reports, learning outcomes, and multimodal data (e.g., log files, eye tracking, facial expressions of emotion, screen recordings) and their contributions to our understanding of SRL with an ITS. Second, we elaborate on the role of embedded pedagogical agents (PAs) as external regulators designed to scaffold learners' cognitive and metacognitive SRL strategy use. Third, we highlight and elaborate on the contributions of multimodal data in measuring and understanding the role of cognitive, affective, metacognitive, and motivational (CAMM) processes. Additionally, we unpack some of the challenges these data pose for designing real-time instructional interventions that scaffold SRL. Fourth, we present existing theoretical, methodological, and analytical challenges and briefly discuss lessons learned and open challenges.

Undergraduate students (N = 82) learned about microbiology with Crystal Island, a game-based learning environment (GBLE), which required participants to interact with instructional materials (i.e., books and research articles, non-player character [NPC] dialogue, posters) spread throughout the game. Participants were randomly assigned to one of two conditions: full agency, where they had complete control over their actions, and partial agency, where they were required to complete an ordered play-through of Crystal Island. As participants learned with Crystal Island, log-file and eye-tracking time series data were collected to pinpoint instances when participants interacted with instructional materials. Hierarchical linear growth models indicated relationships between eye gaze dwell time and (1) the type of representation a learner gathered information from (i.e., large sections of text, poster, or dialogue); (2) the ability of the learner to distinguish relevant from irrelevant information; (3) learning gains; and (4) agency. Auto-recurrence quantification analysis (aRQA) revealed the degree to which repetitive sequences of interactions with instructional material were random or predictable. Through hierarchical modeling, analyses suggested that greater dwell times and learning gains were associated with more predictable sequences of interaction with instructional materials. Results from hierarchical clustering found that participants with restricted agency and more recurrent action sequences had greater learning gains. Implications are provided for how learning unfolds over learners' time in game using a non-linear dynamical systems analysis and the extent to which it can be supported within GBLEs to design advanced learning technologies to scaffold self-regulation during game play.

Children ask many questions, but do not always receive answers to the questions they ask. We were interested in whether the act of generating questions, in the absence of an answer, is related to children’s later thinking. Two experiments examined whether children retain the questions they ask in working memory, and whether the type of questions asked relate to their categorization. Four to ten-year-old children (N = 42 in Experiment 1, N = 41 in Experiment 2) were shown 12 novel objects, asked three questions about each, and did not receive answers to their questions. Children recalled their questions in the first experiment and categorized variants of the novel objects in the second experiment. We found that children have robust working memory for their questions, indicating that these questions may relate to their subsequent thinking. Additionally, children generalize category boundaries more narrowly or broadly depending on the type of question they ask, indicating that children’s questions may reflect an underlying bias in how they think about the world. These findings suggest that future research should examine questions in the absence of answers to understand how inquiry affects children’s cognitive development. Copyright © 2022 Lazaroff and Vlach.

Disease transmission is a fruitful domain in which to examine how scientific and folk theories interrelate, given laypeople’s access to multiple sources of information to explain events of personal significance. The current paper reports an in-depth survey of U.S. adults’ (N = 238) causal reasoning about two viral illnesses: a novel, deadly disease that has massively disrupted everyone’s lives (COVID-19), and a familiar, innocuous disease that has essentially no serious consequences (the common cold). Participants received a series of closed-ended and open-ended questions probing their reasoning about disease transmission, with a focus on causal mechanisms underlying disease contraction, transmission, treatment, and prevention; non-visible (internal) biological processes; and ontological frameworks regarding what kinds of entities viruses are. We also assessed participants’ attitudes, such as their trust in scientific experts and willingness to be vaccinated. Results indicated complexity in people’s reasoning, consistent with the co-existence of multiple explanatory frameworks. An understanding of viral transmission and viral replication existed alongside folk theories, placeholder beliefs, and lack of differentiation between viral and non-viral disease. For example, roughly 40% of participants who explained illness in terms of the transmission of viruses also endorsed a non-viral folk theory, such as exposure to cold weather or special foods as curative. Additionally, participants made use of competing modes of construal (biological, mechanical, and psychological) when explaining how viruses operate, such as framing the immune system response (biological) as cells trying to fight off the virus (psychological). Indeed, participants who displayed greater knowledge about viral transmission were significantly more likely to anthropomorphize bodily processes. Although comparisons of COVID-19 and the common cold revealed relatively few differences, the latter, more familiar disease elicited consistently lower levels of accuracy and greater reliance on folk theories. Moreover, for COVID-19 in particular, accuracy positively correlated with attitudes (trusting medical scientists and taking the disease more seriously), self-protective behaviors (such as social distancing and mask-wearing), and willingness to be vaccinated. For both diseases, self-assessed knowledge about the disease negatively predicted accuracy. The results are discussed in relation to challenges for formal models of explanatory reasoning. Copyright © 2022 Labotka and Gelman.

Does experience with signed language impact the neurocognitive processes recruited by adults solving arithmetic problems? We used event-related potentials (ERPs) to identify the components that are modulated by operation type and problem size in Deaf American Sign Language (ASL) native signers and in hearing English-speaking participants. Participants were presented with single-digit subtraction and multiplication problems in a delayed verification task. Problem size was manipulated in small and large problems with an additional extra-large subtraction condition to equate the overall magnitude of large multiplication problems. Results show comparable behavioral results and similar ERP dissociations across groups. First, an early operation type effect is observed around 200 ms post-problem onset, suggesting that both groups have a similar attentional differentiation for processing subtraction and multiplication problems. Second, for the posterior-occipital component between 240 ms and 300 ms, subtraction problems show a similar modulation with problem size in both groups, suggesting that only subtraction problems recruit quantity-related processes. Control analyses exclude possible perceptual and cross-operation magnitude-related effects. These results are the first evidence that the two operation types rely on distinct cognitive processes within the ASL native signing population and that they are equivalent to those observed in the English-speaking population. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Using data from 12 studies, we meta-analyze correlations between parent number talk during interactions with their young children (mean sample age ranging from 22 to 79 months) and two aspects of family socioeconomics, parent education, and family income. Potential variations in correlation sizes as a function of study characteristics were explored. Statistically significant positive correlations were found between the amount of number talk in parent-child interactions and both parent education and family income (i.e., r = 0.12 for education and 0.14 for income). Exploratory moderator analyses provided some preliminary evidence that child age, as well as the average level of and variability in socioeconomic status, may moderate effect sizes. The implications of these findings are discussed with special attention to interpreting the practical importance of the effect sizes in light of family strengths and debate surrounding word gaps.