Many teachers have come to rely on the affordances that computer-based learning platforms offer in regard to aiding in student assessment, supplementing instruction, and providing immediate feedback and help to students as they work through assigned content. Similarly, researchers commonly utilize the large datasets of clickstream logs describing students' interactions with the platform to study learning. For the teachers that use this information to monitor student progress, as well as for researchers, this data provides limited insights into the learning process; this is particularly the case as it pertains to observing and understanding the effort that students are applying to their work. From the perspective of teachers, it is important for them to know which students are attending to and using computer-provided aid and which are taking advantage of the system to complete work without effectively learning the material. In this paper, we conduct a series of analyses based on response time decomposition (RTD) to explore student help-seeking behavior in the context of on-demand hints within a computer-based learning platform with particular focus on examining which students appear to be exhibiting effort to learn while engaging with the system. Our findings are then leveraged to examine how our measure of student effort correlates with later student performance measures.

Although online courses can provide students with a high-quality and flexible learning experience, one of the caveats is that they require high levels of self-regulation. This added hurdle may have negative consequences for first-generation college students. In order to better understand and support students' self-regulated learning, we examined a fully online Chemistry course with high enrollment (N = 312) and a high percentage of first-generation college students (65.70%). Using students' lecture video clickstream data, we created two indicators of self-regulated learning: lecture video completion and time management. Performing a k-means clustering on these indicators uncovered four distinct self-regulated learning patterns: (1) Early Planning, (2) Planning, (3) Procrastination, and (4) Low Engagement. Early Planning behaviors were especially important for course success-they consistently predicted higher final course grades, even after controlling for important demographic variables. Interestingly, first-generation college students classified as Early Planners achieved at similar levels as their non-first-generation peers, but first-generation students in the Low Engagement group had the lowest average grades among students. Overall, our results show that self-regulation may be an important skill for determining first-generation students' STEM achievement, and targeting these skills may serve as a useful way to support their specific learning needs.

Collaborative game-based learning environments offer significant promise for creating engaging group learning experiences. Online chat plays a pivotal role in these environments by providing students with a means to freely communicate during problem solving. These chat-based discussions and negotiations support the coordination of students' in-game learning activities. However, this freedom of expression comes with the possibility that some students might engage in undesirable communicative behavior. A key challenge posed by collaborative game-based learning environments is how to reliably detect disruptive talk that purposefully disrupt team dynamics and problem-solving interactions. Detecting disruptive talk during collaborative game-based learning is particularly important because if it is allowed to persist, it can generate frustration and significantly impede the learning process for students. This paper analyzes disruptive talk in a collaborative game-based learning environment for middle school science education to investigate how such behaviors influence students' learning outcomes and varies across gender and students' prior knowledge. We present a disruptive talk detection framework that automatically detects disruptive talk in chat-based group conversations. We further investigate both classic machine learning and deep learning models for the framework utilizing a range of dialogue representations as well as supplementary information such as student gender. Findings show that long short-term memory network (LSTM)-based disruptive talk detection models outperform competitive baseline models, indicating that the LSTM-based disruptive talk detection framework offers significant potential for supporting effective collaborative game-based learning through the identification of disruptive talk.

Despite the increasing number of women receiving bachelor's degrees in computing (i.e., Computer Science, Computer Engineering, Information Technology, etc.), a closer look reveals that the percentage of Black women in computing has significantly dropped in recent years, highlighting the underrepresentation of Black women and its negative impact on broadening participation in the field of computing. The literature reveals that several K-16 interventions have been designed to increase the representation of Black women and girls in computing. Despite these best efforts, the needle seems to have barely moved in increasing the representation or the retention of Black women in computing. Instead, the primary goals have been to recruit and retain women in the CS pipeline using gender-focused efforts intended to increase the number of women who also identify as members of racialized groups. However, these gender-focused efforts have fallen short of increasing the number of Black women in computing because they fail to acknowledge or appreciate how intersectionality (the overlapping social constructs of gender, race, ethnicity, class, etc.) has shaped the lived experiences of Black women navigating the computing pipeline. Without honest dialogue about how power operates in the field of computing, the push for racial equality and social justice in CS education remains an elusive goal. Leveraging intersectionality as a critical framework to address systemic oppression (i.e., racism, gender discrimination, power, and privilege), we interview 24 Black women in different phases of the computing pipeline about their experiences navigating the field of computing. An intersectional analysis of Black women's experiences reveals that CS education consists of saturated sites of violence in which interconnected systems of power converge to enact oppression. Findings reveal three primary saturated sites of violence within CS education: (1) traditional K-12 classrooms; (2) predominantly White institutions; and (3) internships as supplementary learning experiences. We conclude the article with implications for how the field of CS education can begin to address racial inequality that negatively impacts Black girls and women, thus contributing to a more equitable and socially just field of study that benefits all students.

Recent studies of creative cognition have revealed interactions between functional brain networks involved in the generation of novel ideas; however, the neural basis of creativity is highly complex and presents a great challenge in the field of cognitive neuroscience, partly because of ambiguity around how to assess creativity. We applied a novel computational method of verbal creativity assessment-semantic distance-and performed weighted degree functional connectivity analyses to explore how individual differences in assembly of resting-state networks are associated with this objective creativity assessment. To measure creative performance, a sample of healthy adults (n = 175) completed a battery of divergent thinking (DT) tasks, in which they were asked to think of unusual uses for everyday objects. Computational semantic models were applied to calculate the semantic distance between objects and responses to obtain an objective measure of DT performance. All participants underwent resting-state imaging, from which we computed voxel-wise connectivity matrices between all gray matter voxels. A linear regression analysis was applied between DT and weighted degree of the connectivity matrices. Our analysis revealed a significant connectivity decrease in the visual-temporal and parietal regions, in relation to increased levels of DT. Link-level analyses showed higher local connectivity within visual regions was associated with lower DT, whereas projections from the precuneus to the right inferior occipital and temporal cortex were positively associated with DT. Our results demonstrate differential patterns of resting-state connectivity associated with individual creative thinking ability, extending past work using a new application to automatically assess creativity via semantic distance.

Purpose: Principals are critical to school improvement and play a vital role in creating inclusive and high-performing schools. Yet, approximately one in five principals leave their school each year, and turnover is higher in schools that serve low-income students of color. Relatedly, high rates of teacher turnover exacerbate challenges associated with unstable learning environments. Our study examines the extent to which principal turnover influences teacher turnover. We build on past work by exploring how the relationship between teacher and principal turnover differs in urban, high-poverty settings and by examining the effects of chronic principal turnover. Research Methods/Approach: We draw on a student- and employee-level statewide longitudinal dataset from Texas that includes all public K-12 schools from school years 1999–2000 to 2016–17. We estimate teacher-level models with school fixed effects, allowing us to compare teacher turnover in schools leading up to and immediately following a principal exit, to otherwise similar schools that do not experience principal turnover. Findings: Teacher turnover spikes in schools experiencing leadership turnover, and these effects are greater among high-poverty and urban schools, in schools with low average teacher experience, and in schools experiencing chronic principal turnover. Implications: Improving leadership stability, especially in urban schools experiencing chronic principal turnover may be an effective approach to reducing teacher turnover. Principal and teacher turnover and their relationship with each other requires further investigation. The field would benefit from qualitative research that can provide important insights into the individual decisions and organizational processes that contribute to principal turnover.

Although a growing body of scholarship points to the importance of teacher education program coherence, few studies focus on the ways in which teacher education program directors, field placement coordinators, and methods course instructors foster program coherence. This mixed-methods study draws on interview data from four teacher education program directors, seven field placement coordinators, and 25 elementary mathematics and English language arts methods course instructors at four large, public research universities, as well as survey data from 305 elementary teaching candidates at those universities. Using a coherence framework, we analyze differences across programs in the degree to which teaching candidates perceived their programs as having clear visions and high levels of program coherence. We also describe ways in which program directors, field placement coordinators, and methods instructors described and promoted shared visions across courses and between courses and field experiences. Implications for teacher education programs and research are discussed. © 2019 American Association of Colleges for Teacher Education.

We ask whether patterns of racial ethnic and socioeconomic stratification in educational attainment are amplified or attenuated when we take a longer view of educational careers. We propose a model of staged advantage to understand how educational inequalities evolve over the life course. Distinct from cumulative advantage, staged advantage asserts that inequalities in education ebb and flow over the life course as the population at risk of making each educational transition changes along with the constraints they confront in seeking more education. Results based on data from the 2014 follow up of the sophomore cohort of High School and Beyond offer partial support for our hypotheses. The educational attainment process was far from over for our respondents as they aged through their 30s and 40s: More than 6 of 10 continued their formal training during this period, and 4 of 10 earned an additional credential. Patterns of educational stratification at midlife became more pronounced in some ways as women pulled further ahead of men in their educational attainments and parental education (but not income), and high school academic achievement continued to shape educational trajectories at the bachelor's degree level and beyond. However, African Americans gained on whites during this life phase through continued formal (largely academic) training and slightly greater conditional probabilities of graduate or professional degree attainment; social background fails to predict earning an associate's degree. These results, showing educational changes and transitions far into adulthood, have implications for our understanding of the complex role of education in stratification processes.

This meta-analysis evaluated theoretical predictions from balanced identity theory (BIT) and evaluated the validity of zero points of Implicit Association Test (IAT) and self-report measures used to test these predictions. Twenty-one researchers contributed individual subject data from 36 experiments (total N = 12,773) that used both explicit and implicit measures of the social-cognitive constructs. The meta-analysis confirmed predictions of BIT's balance-congruity principle and simultaneously validated interpretation of the IAT's zero point as indicating absence of preference between two attitude objects. Statistical power afforded by the sample size enabled the first confirmations of balance-congruity predictions with self-report measures. Beyond these empirical results, the meta-analysis introduced a within-study statistical test of the balance-congruity principle, finding that it had greater efficiency than the previous best method. The meta-analysis's full data set has been publicly archived to enable further studies of interrelations among attitudes, stereotypes, and identities.

Background: By attracting high-achieving college graduates and professional career changers, selective alternative certification programs, such as the New York City Teaching Fellows (NYCTF), promise to address pressing teacher shortages while also improving outcomes in hard-to-staff schools. Purpose: Looking at the main patterns in their careers before, during, and after completing NYCTF, the study provides insights into the short- and long-term impacts of mathematics teachers who entered as first- and second-career teachers on NYC public schools and the people in them. Participants: The study tracked the career trajectories and decision-making of more than 600 NYCTF mathematics teachers over a 9-year period. Research design: The longitudinal analysis of the teachers' career trajectories is illuminated by descriptive statistics and qualitative analyses of their responses to open-ended survey items. Results: The article provides a portrait of urban mathematics teachers' career decision-making as it unfolds over time. It challenges conventional understandings by demonstrating the stochastic nature of teachers' career decision-making and, as part of this, consequential amounts of involuntary and midyear turnover. It further shows that, although in many ways similar, the career trajectories of the career changers and recent college graduates differed in key regards. Recommendations: On their own, strategies designed to attract high-achieving recent graduates and professional career changers to teach core subjects like mathematics will not solve long-standing teacher turnover and shortage issues in in high-needs urban schools. Districts also should focus on retention strategies, including training and induction tailored to meet the different needs and career goals of first- and second-career teachers.