The feelings of difficulty and familiarity (FOD and FOF) are two types of metacognitive experiences. Both may influence student engagement and the application of metacognitive strategies, but these relationships are not well understood, in part because many studies have relied on self-report measures of behaviors that may not accurately reflect students' actual behaviors. In this study, FOD and FOF were related to objective measures of off-task behaviors and metacognitive strategies. These measures were extracted from 88 sixth graders' action logs within a computer-based learning environment known as Betty's Brain. Pre- and post-tests were administered to assess learning. Results reveal that high-FOD students showed more off-task behaviors and fewer strategic behaviors than low-FOD students, particularly when this difference was measured in terms of the frequency (as opposed to proportion) of strategic behaviors. FOF was not associated with off-task behaviors and metacognitive strategies but emerged as a moderator in the relationship between FOD and learning gains. Low-FOD students learned more than high-FOD students in the low-FOF group, but such a difference was not found in the high-FOF group.

It is widely recognized that debugging is challenging for novice programmers and, as such, computing educators and researchers have called for explicit debugging instruction. Debugging requires various knowledge and skills, and different students may show different strengths and weaknesses. An understanding of such individual differences is important as it may guide personalized instruction. The current study investigated individual differences in debugging in an undergraduate introductory computer science course. We extracted variables related to debugging from students' submission traces to programming problems in the first month of the course. We applied latent profile analysis to these variables and identified three distinctive profiles. Profile A showed higher debugging accuracy and speed. Profile B showed lower debugging performance in runtime and logic errors, while profile C had lower performance in syntactic errors and tended to make large code edit every submission. Students' gender and self-rated programming ability predicted profile membership. Moreover, profile A got higher scores than the others in the first exam, and this difference persisted in the second and third exam, even controlling for background variables and score on the first exam. We investigated how students transitioned across debugging profiles over the duration of the course. From the beginning to the end of the course, a large part of students stayed in lower performance profiles. Overall, these findings support the call that debugging should be taught at an early stage and suggest that different groups may need different debugging instructions or support.

Children learn math concepts long before they enter school. Across all cultures, children are exposed to number and spatial language to varying degrees during everyday home routines. Yet most studies of math talk occur in the lab and target non-Hispanic, English-speaking families. We expanded inquiry to the spontaneous math language (i.e., number and spatial language) of Spanish-speaking mothers and their 1- to 2-year-olds (N = 50) during home activities. Mothers varied enormously in their use of math language, and mother math language related to toddler math language, whereas mother non-math language did not. Children's math language both preceded and followed mother math talk, suggesting imitation and reinforcement as important processes in children's math language learning. Children also produced math language outside the context of mother input. Findings advance an understanding of children's early math language in natural settings and have implications for interventions aimed at promoting math skills in toddlers from diverse backgrounds.

This paper proposes a novel multi-modal transformer network for detecting actions in untrimmed videos. To enrich the action features, our transformer network utilizes a new multi-modal attention mechanism that computes the correlations between different spatial and motion modalities combinations. Exploring such correlations for actions has not been attempted previously. To use the motion and spatial modality more effectively, we suggest an algorithm that corrects the motion distortion caused by camera movement. Such motion distortion, common in untrimmed videos, severely reduces the expressive power of motion features such as optical flow fields. Our proposed algorithm outperforms the state-of-the-art methods on two public benchmarks, THUMOS14 and ActivityNet. We also conducted comparative experiments on our new instructional activity dataset, including a large set of challenging classroom videos captured from elementary schools. © 2023

There is a need for a more robust conceptualization of engagement in mathematics education research. Investigating how teachers describe engagement can provide insight into relationships between purposes of engagement and dimensions of engagement. In this exploratory study, we examined how 28 secondary mathematics teachers in two states in the USA talked about their students' engagement. During interviews, we asked teachers to provide their definitions for engagement, describe their teaching strategies for engaging students, and describe their observations of engagement during a video clip from their own classroom. We interpreted teachers' talk to identify how they described the nature of mathematics engagement (dimensions such as behavioral, cognitive, affective, and/or social engagement) and purposes of engagement (engagement in learning or in schooling [Harris, 2011]). When teachers described the purpose of engagement as engagement in learning, they also tended to describe the nature of engagement with cognitive and social dimensions and with multiple dimensions of engagement.

Many STEM degree holders, especially women and minorities, are not employed in STEM occupations in the United States, and transitions into the STEM labor force among recent graduates have been declining since the 1980 ' s. We examine transitions from school to work at two large U.S. universities in 2015-16, focusing on the internship experiences and job search strategies of graduating chemistry and chemical engineering majors. Surprisingly, 28% of our STEM respondents had no post-graduation plans, though women were significantly more likely than men to already have a job. Overall race differences in post-graduation plans were insignificant, though Black and Hispanic students were more likely to have no post-graduation plans compared to Whites and Asians. While Black, Hispanic, and LGBT students reported fewer job search behaviors overall, potentially explaining this pattern, no gender differences in job search behaviors or internship experiences emerged to explain women's employment advantage. However, better grades led to early job offers, reducing most of women's initial hiring advantage along with positive internship experiences, which did not alter men's likeli-hood of a job offer but were associated with a higher likelihood of a job offer among women.

Women of Color (WoC) in science, technology, engineering, and math (STEM) leave doctoral programs at disproportionately high rates. Supportive mentorship is key to increasing belonging and rates of retention, yet little is known about how conversations between mentees and their mentors on academic and personal stress topics unfold in real-time. Applying the lens of Social Cognitive Career Theory to communication dynamics between mentees and mentors, the present study utilized a dynamic dyadic systems (DDS) perspective to examine observationally coded data from six mentee-mentor dyads. First, hierarchical clustering analysis was applied to identify speaking turn types. Then, sequence analysis was used to identify common multi-turn patterns or conversation motifs (CM). Results showed five predominant CMs: (CM1) support provision through listening; (CM2) focus on mentor's experience; (CM3) support provision through advice; (CM4) mentee's making a bid for support; and (CM5) mentor dominated conversations. This study demonstrates methods for identifying potentially meaningful patterns of support in stress conversations between mentees and mentors. The application of such methods with larger samples may aid in understanding ways to increase retention among WoC in STEM through mentor support provision.

Teachers' mathematical knowledge has important consequences for the quality of the learning environment they create for their students to learn mathematics. Yet relatively little is known about how teachers reason proportionally, despite the fact that proportional reasoning is foundational for several mathematics concepts and that ratios and proportional relationships constitute a major component of the middle school mathematics curriculum. In this study, we investigated how teachers reasoned proportionally on a nonroutine ratio task and the extent to which their proportional reasoning was able to predict their overall understanding of the relevant concepts: ratios and proportional relationships. Using data collected from 238 US mathematics teachers, we found that teachers' proportional reasoning could be grouped into four categories: incorrect, additive, relative, and proportional reasoning. Our results also indicated that teachers' overall knowledge of ratios and proportional relationships aligned with the way they reasoned proportionally, meaning that teachers who used incorrect reasoning on a separate task received the lowest scores on average on the ratios and proportional relationships measure, whereas those who reasoned proportionally had the highest mean scores on average. Implications of the study include the need to shift attention to the way teachers reason in relation to the two elements of proportional reasoning (covariance and invariance) to capture the nuances in their understanding of ratios and proportional relationships.

People often self-identify as allies to the lesbian, gay, bisexual, and transgender (LGBT) community. This research examined on what basis LGBT individuals perceive others to be allies and documents the consequences of perceived allyship. Studies 1a (n = 40) and 1b (n = 69) collected open-ended descriptions of allyship provided by LGBT participants. Coding of the responses suggested multiple components to being an ally: (a) being nonprejudiced toward the group, (b) taking action against discrimination and inequality, and (c) having humility about one’s perspective in discussions about LGBT issues. In Studies 2a (n = 161) and 2b (n = 319, with nationally representative characteristics), an allyship scale was developed and validated for general and specific relational contexts, respectively. Study 2b also showed that LGBT individuals’ perceptions of close others’ allyship were positively associated with their own well-being and relationship quality with the close other. Study 3, an experiment, demonstrated that nonprejudice and action had an interactive effect on perceived allyship, such that action increased perceived allyship more when prejudice was low (vs. high). Study 4 was a weekly experience study of LGBT participants and an outgroup roommate. Perceiving one’s roommate to be a good ally predicted higher self-esteem, greater subjective well-being, and better relationship quality with the roommate, both between and within participants. Furthermore, perceived allyship in 1 week was associated with increases in LGBT individuals’ mental health and relationship quality with the roommate the following week. This research advances knowledge about what allyship means to LGBT individuals and identifies intra-and interpersonal benefits of allyship. © 2023 American Psychological Association

Teachers' knowledge of students' mathematical thinking is a growing area of research in mathematics education. The literature has reported plentiful evidence of the interplays between teachers' mathematical knowledge and their knowledge of students' mathematical thinking. The present study builds on this body of work to explain how such interplays occur. Over a semester, I worked in partnership with a secondary school mathematics teacher on cycles of task design, interactions with a student, and in-depth reflection on the student's thinking about linear programming. Adopting and extending Piagetian constructs of assimilation and accommodation, I describe several key mental processes that illuminate the teacher's learning of mathematics and of the student's mathematical thinking. I conclude with a discussion of the study's empirical and theoretical contributions to understanding teachers' mathematical learning in relation to student thinking.