The nurturing of learners’ ways of knowing is vital for supporting their intellectual growth and their participation in democratic knowledge societies. This paper traces the development of two interrelated theoretical frameworks that describe the nature of learners’ epistemic thinking and performance and how education can support epistemic growth: the AIR and Apt-AIR frameworks. After briefly reviewing these frameworks, we discuss seven reflections on educational theory development that stem from our experiences working on the frameworks. First, we describe how our frameworks were motivated by the goal of addressing meaningful educational challenges. Subsequently, we explain why and how we infused philosophical insights into our frameworks, and we also discuss the steps we took to increase the coherence of the frameworks with ideas from other educational psychology theories. Next, we reflect on the important role of the design of instruction and learning environments in testing and elaborating the frameworks. Equally important, we describe how our frameworks have been supported by empirical evidence and have provided an organizing structure for understanding epistemic performance exhibited in studies across diverse contexts. Finally, we discuss how the development of the frameworks has been spurred by dialogue within the research community and by the need to address emerging and pressing real-world challenges. To conclude, we highlight several important directions for future research. A common thread running through our work is the commitment to creating robust and dynamic theoretical frameworks that support the growth of learners’ epistemic performance in diverse educational contexts. © The Author(s) 2024.

Self-regulated learning (SRL) is a cognitive and metacognitive process through which students develop the self-awareness necessary to direct their learning based on their needs to reach a desired outcome. Despite 40 years of literature, SRL has no singular definition, as it is often used in domain-specific research that is not always transferable to other fields. Regardless, much of the literature speaks to the importance of SRL regarding academic success. This paper details the development of an SRL instrument designed to identify key self-regulatory constructs in an undergraduate introductory physics classroom. Confirmatory factor analysis supported a four-factor model measuring Planning, Time & Environment Management, Comprehension Monitoring & Evaluation, and Peer Learning & Help-Seeking as unique facets of self-regulated learning. While most behaviors did not significantly evolve over one semester, students reported significantly lower scores on the Comprehension Monitoring & Evaluation factor between the beginning and end of the semester. Higher performing students, as measured by their average homework grades, scored significantly higher on the Time & Environment Management factor and the Peer Learning & Help-Seeking factor at both time points. Additionally, SRL behaviors were significantly predicted by personality facets from the Big Five Inventory, with Conscientiousness, Extraversion, and Openness being the most related to certain behaviors.

The COVID-19 pandemic in the United States has had a disproportionate impact on Black, low-income, and elderly individuals. We recruited 175 predominantly white children ages 5-12 and their parents (N = 112) and asked which of two individuals (differing in age, gender, race, social class, or personality) was more likely to get sick with either COVID-19 or the common cold and why. Children and parents reported that older adults were more likely to get sick than younger adults, but reported few differences based on gender, race, social class, or personality. Children predominantly used behavioral explanations, but older children used more biological and structural explanations. Thus, children have some understanding of health disparities, and their understanding increases with age.

A typical classroom exercise in hydrogeology is to develop a conceptual model of a contaminated site, identify groundwater flow direction(s), and predict the location and mass of a contaminant plume. This requires knowledge of key hydrogeological concepts and is highly visuospatial in nature. Among multiple discrete spatial thinking skills identified by cognitive science, the combination of visual penetrative ability and working in multiple frames of reference were identified to significantly predict performance on a hydrogeology task and showed that together with hydrogeology knowledge, these spatial thinking skills account for 49% of the variability on task performance. Seventy-two hydrogeology practitioners and students with varying levels of expertise were administered multiple spatial thinking tests and an assessment of hydrogeology knowledge before completing a hydrogeology task that was developed for the study. Using spatial thinking and knowledge test scores as predictor variables, a hierarchical regression analysis was conducted with performance on the hydrogeology task as the outcome variable. The resulting model predicts that at low levels of hydrogeology knowledge, the identified spatial thinking skills account for more than a 25% difference on the hydrogeology task. This study provides empirical evidence that visual penetrative ability and working in multiple frames of reference are important skills in hydrogeology; thus, instructors are encouraged to recognize that underdeveloped spatial thinking skills could present hurdles for students and that targeted spatial thinking training may yield positive results for both weak and strong spatial thinkers.

Student’s shift of attention away from a current learning task to task-unrelated thought, also called mind wandering, occurs about 30% of the time spent on education-related activities. Its frequent occurrence has a negative effect on learning outcomes across learning tasks. Automated detection of mind wandering might offer an opportunity to assess the attentional state continuously and non-intrusively over time and hence enable large-scale research on learning materials and responding to inattention with targeted interventions. To achieve this, an accessible detection approach that performs well for various systems and settings is required. In this work, we explore a new, generalizable approach to video-based mind wandering detection that can be transferred to naturalistic settings across learning tasks. Therefore, we leverage two datasets, consisting of facial videos during reading in the lab (N = 135) and lecture viewing in-the-wild (N = 15). When predicting mind wandering, deep neural networks (DNN) and long short-term memory networks (LSTMs) achieve F1 scores of 0.44 (AUC-PR = 0.40) and 0.459 (AUC-PR = 0.39), above chance level, with latent features based on transfer-learning on the lab data. When exploring generalizability by training on the lab dataset and predicting on the in-the-wild dataset, BiLSTMs on latent features perform comparably to the state-of-the-art with an F1 score of 0.352 (AUC-PR = 0.26). Moreover, we investigate the fairness of predictive models across gender and show based on post-hoc explainability methods that employed latent features mainly encode information on eye and mouth areas. We discuss the benefits of generalizability and possible applications. © The Author(s) 2024.

There is limited time for elementary teacher professional learning in science in order to meet the aspirational goals of current reform efforts. In this study, we investigated what and how teachers learn on-the-job to gain insight into modes of support less often included in teacher education design. Specifically, we studied elementary teachers' participation in a system-level organizational routine: curriculum materials adoption processes (CMAPs). Using a communities of practice framework, we explored teacher learning in a comparative case study of three U.S. school districts' CMAP routines, observing CMAP committee meetings and interviewing participants about their expeirences. We found that what teachers learned varied across each district's CMAP. We argue this variation can be traced to two CMAP features: (1) teachers' use of boundary objects and (2) their boundary spanning roles and structures. Results have implications for the design of educational systems' organizational routines to more intentionally serve a dual role as a professional learning opportunity.

Integrating microintervention strategies and the bystander intervention model, we examined social cognitive predictors (i.e., moral disengagement, empathy, and self-efficacy) of the five steps of the bystander intervention model (i.e., Notice, Interpret, Accept, Know, and Act) to address racial microaggressions in a sample of 452 racially diverse college students. Data were collected using an online survey. Path analyses showed that moral disengagement was significantly and negatively related to each step of the model for White students, but for students of color, it was only significantly negatively associated with Act. Empathy was significantly and positively associated with Interpret, Accept, and Act for White students. For student of color, however, there was a significant and positive association solely between Empathy and Act. For both White students and students of color, self-efficacy was positively associated with Notice, Interpret, Accept, Know, and Act. Finally, race did not significantly moderate any relationships. Strengths, limitations, future directions for research, and implications of the study findings are discussed.

Recent work suggests that the stereotype associating brilliance with men may underpin women's underrepresentation in prestigious careers, yet little is known about its development and consequences in non-Western contexts. The present research examined the onset of this stereotype and its relation to children's motivation in 5- to 7-year-old Korean children (N = 272, 50% girls, tested 2021 to 2022). At age 7, children attributed brilliance to men when evaluating Asians and Whites, and girls became less interested in participating in intellectually challenging tasks than boys. Notably, this gender difference in interest was mediated by children's endorsement of the stereotype. The generalizable early emergence of the gender brilliance stereotype and its detrimental implications press the need to tackle gender imbalance in early childhood. © 2023 The Authors. Child Development © 2023 Society for Research in Child Development.

Despite knowing physics and astronomy doctoral programs are laden with identity-based inequities, they continue to push minoritized students to the margins. This qualitative social network analysis of 100 women and/or lesbian, gay, bisexual, transgender, queer, and more (LGBT+) physics and astronomy Ph.D.'s explores how minoritized physics and astronomy students utilize social networks to navigate departmental exclusion. Our findings indicate that many of the participants' identities were often unacknowledged or negatively addressed within their graduate education, with only four participants reporting a positive or favorable experience during this period of their career. Direct support from peers, faculty, and identity-based affinity groups was necessary for participants to navigate their educations. This study demonstrated that generic best practices often cannot fully support the diverse range of persons who come to physics and that identity-neutral values in physics further isolate students by insinuating that their own minoritized experiences are not valid.

Modern assessment demands, resulting from educational reform efforts, call for strengthening diagnostic testing capabilities to identify not only the understanding of expected learning goals but also related intermediate understandings that are steppingstones on pathways to learning goals. An accurate and nuanced way of interpreting assessment results will allow subsequent instructional actions to be targeted. An appropriate psychometric model is indispensable in this regard. In this study, we developed a new psychometric model, namely, the diagnostic facet status model (DFSM), which belongs to the general class of cognitive diagnostic models (CDM), but with two notable features: (1) it simultaneously models students’ target understanding (i.e., goal facet) and intermediate understanding (i.e., intermediate facet); and (2) it models every response option, rather than merely right or wrong responses, so that each incorrect response uniquely contributes to discovering students’ facet status. Given that some combination of goal and intermediate facets may be impossible due to facet hierarchical relationships, a regularized expectation–maximization algorithm (REM) was developed for model estimation. A log-penalty was imposed on the mixing proportions to encourage sparsity. As a result, those impermissible latent classes had estimated mixing proportions equal to 0. A heuristic algorithm was proposed to infer a facet map from the estimated permissible classes. A simulation study was conducted to evaluate the performance of REM to recover facet model parameters and to identify permissible latent classes. A real data analysis was provided to show the feasibility of the model. © The Author(s), under exclusive licence to The Psychometric Society 2024. corrected publication 2024.