Fostering creativity is vital for tackling 21st-century challenges, and education plays a key role in nurturing this skill. According to the associative theory, creativity involves connecting distant concepts in semantic memory. Here, we explore how semantic memory changes following an educational intervention intended to promote creativity. Specifically, we examine how a scientific education curriculum-Scientific Creativity in Practice (SCIP) program-impacts the semantic memory networks of 10-18-year-old students in a chemistry class (n = 176). Students in an Intervention group who received the SCIP intervention, and a Control group who did not, completed creative thinking tests, as well as verbal fluency tasks to estimate semantic networks in science-specific (chemistry) and domain-general (animal) categories. Results showed that the SCIP intervention enhanced performance on one test of scientific creative thinking but showed no significant difference on another. Using network science methods, we observed increased interconnectedness in both science-specific and domain-general categories, with lower path distances between concepts and reduced modularity. These traits define a 'small-world' network, balancing connections between closely related and remote concepts. Notably, the chemistry semantic network showed substantially more reorganization, consistent with the chemistry contents of the SCIP intervention. The findings suggest that semantic memory reorganization may be a cognitive mechanism underlying successful creativity interventions in science education.

Although teachers are expected to teach mathematics ambitiously, it is challenging for them to do so. Given that the first year represents a critical juncture between teacher preparation and inservice teaching, it may be particularly challenging to teach ambitiously. Understanding the nuanced characteristics in lessons taught by first-year elementary teachers identified as being generally ambitious can inform how mathematics teacher educators and school mentors support them. A thematic analysis of 15 transcribed lessons revealed instructional moves that were more and less prevalent. Task-level findings indicated that while first-year teachers solicited student input, encouraged them to participate in discussions, and had them attend to cognitively demanding tasks, they seldom assigned open-ended and non-procedural tasks. Students also had few opportunities to grapple with the mathematics. Turn-level findings indicated that first-year teachers maintained the task potential, facilitated student participation, and promoted student authority and perspectives. They further aimed to deepen students’ mathematical thinking, albeit the subtle ways they did so sometimes were apparent across lessons (e.g., encourage students to continue to problem solve) and other times were limited (e.g., provide an opportunity for students to agree or disagree). © 2024 Research Council on Mathematics Learning.

Motivating girls to enroll in computer science (CS) courses is critically important. Stereotypes that girls are less interested than boys in CS may deter girls. Three preregistered experimental studies (N = 1,053) examined causal links between gender-interest stereotypes and middle school students' CS motivation. Experiment 1 showed that stereotypes reduced girls' motivation to enroll, mediated by a lower sense of belonging. Experiment 2 showed that underrepresentation is a cue to stereotypes. Experiment 3 demonstrated that providing information about other girls' interest countered stereotypes and promoted motivation. Directly addressing stereotypes may be instrumental in promoting equity for all in CS.

Transfer stigma refers to a type of stigma associated with students' transfer status and/or their community college background. It plays a significant role in post-transfer adjustment and may negatively impact post-transfer outcomes such as retention and obtaining a baccalaureate degree. This study focused on quantitatively measuring transfer stigma among transfer students attending a four-year institution. Drawing from previous studies, we developed a 13-item transfer stigma measure and included it in a transfer student survey. The survey data was collected from 450 current transfer students at a public, flagship four-year university in Louisiana. Through an exploratory factor analysis and a confirmatory factor analysis, we revealed a four-factor structure of transfer stigma measures: internalized self-stigma, perceptions about community colleges, lack of support, and perceived judgment. Subsequent statistical analyses examined group differences in the degree of transfer stigma across various transfer student subgroups defined by transfer type, gender, race/ethnicity, age, and major.

We propose expanding the authors' shared novelty-seeking basis for creativity and curiosity by emphasizing an underlying computational principle: Minimizing prediction errors (mismatch between predictions and incoming data). Curiosity is tied to the anticipation of minimizing prediction errors through future, novel information, whereas creative AHA moments are connected to the actual minimization of prediction errors through current, novel information.

Research on public attitudes toward science and technology policy has relied on surveys taken at single points in time. These surveys fail to indicate how these attitudes develop or change. In this study, we use data from the Longitudinal Study of American Life that has followed a national sample of Generation X for 33 years-from middle school to midlife. We demonstrate that the critical period for the formation of attitudes toward science and technology is the 15-18 years after high school-college, work, family, and career. The attitudes formed in this period remain stable for most individuals during midlife. This work provides an important perspective for scientists, engineers, and the leadership of the scientific community in their efforts to foster positive attitudes toward science and technology and to understand the roots of concerns and reservations about science.

While research has shown that students benefit from student-centered pedagogies, few studies have considered the benefits of this pedagogical approach for educators as they learn through teaching. In response to this need, we analyzed interviews, lesson plans, and video observations from five teachers in elementary schools across the United States who varyingly engaged student-centered and teacher-centered pedagogies. Our analyses revealed that the participating teachers developed a wide breadth of teacher knowledge regardless of their pedagogical approach. However, the teachers who employed student-centered teaching reported more pedagogical content knowledge gains for themselves than the teachers who used direct teaching.

Instructional activity recognition is an analytical tool for the observation of classroom education. One of the primary challenges in this domain is dealing with the intri- cate and heterogeneous interactions between teachers, students, and instructional objects. To address these complex dynamics, we present an innovative activity recognition pipeline designed explicitly for instructional videos, leveraging a multi-semantic attention mechanism. Our novel pipeline uses a transformer network that incorporates several types of instructional seman- tic attention, including teacher-to-students, students-to-students, teacher-to-object, and students-to-object relationships. This com- prehensive approach allows us to classify various interactive activity labels effectively. The effectiveness of our proposed algo- rithm is demonstrated through its evaluation on our annotated instructional activity dataset.

Children's performance on the number line estimation task, often measured by the percentage of absolute error, predicts their later mathematics achievement. This task may also reveal (a) children's ordinal understanding of the target numbers in relation to each other and the benchmarks (e.g., endpoints, midpoint) and (b) the ordinal skills that are a necessary precursor to children's ability to understand the interval nature of a number line as measured by percentage of absolute error. Using data from 104 U.S. kindergartners, we measured whether children's estimates were correctly sequenced across trials and correctly positioned relative to given benchmarks within trials at two time points. For both time points, we found that each ordinal error measure revealed a distinct pattern of data distribution, providing opportunities to tap into different aspects of children's ordinal understanding. Furthermore, children who made fewer ordinal errors scored higher on the Test of Early Mathematics Ability and showed greater improvement on their interval understanding of numbers as reflected by a larger reduction of percentage of absolute error from Time 1 to Time 2. The findings suggest that our number line measures reveal individual differences in children's ordinal understanding of numbers, and that such understanding may be a precursor to their interval understanding and later mathematics performance. (c) 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY -NC -ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Network analysis has become a well-recognized methodology in physics education research (PER), with study topics including student performance and persistence, faculty change, and the structure of conceptual networks. The social network analysis side of this work has focused on quantitative analysis of wholenetwork cases, such as the structure of networks in single classrooms. Egocentric or personal network approaches are largely unexplored, and qualitative methods are underdeveloped. In this paper, we outline theoretical and practical differences between two major network paradigms-whole-network and egocentric-and introduce theoretical frameworks and methodological considerations for egocentric studies. We also describe qualitative and mixed-methods approaches that are currently missing from the PER literature. We identify areas where these additional network methods may be of particular interest to physics education researchers and end by discussing example cases and implications for new PER studies.