Researchers invested in K-12 education struggle not just to enhance pedagogy, curriculum, and student engagement, but also to harness the power of technology in ways that will optimize learning. Online learning platforms offer a powerful environment for educational research at scale. The present work details the creation of an automated system designed to provide researchers with insights regarding data logged from randomized controlled experiments conducted within the ASSISTments TestBed. The Assessment of Learning Infrastructure (ALI) builds upon existing technologies to foster a symbiotic relationship beneficial to students, researchers, the platform and its content, and the learning analytics community. ALI is a sophisticated automated reporting system that provides an overview of sample distributions and basic analyses for researchers to consider when assessing their data. ALI's benefits can also be felt at scale through analyses that crosscut multiple studies to drive iterative platform improvements while promoting personalized learning. © 2016 ACM.

Tensions between the demands of the knowledge-based economy and remaining, blue-collar jobs underlie renewed debates about whether schools should emphasize career and technical training or college-preparatory curricula. We add a gendered lens to this issue, given the male-dominated nature of blue-collar jobs and women's greater returns to college. Using the ELS:2002, this study exploits spatial variation in school curricula and jobs to investigate local dynamics that shape gender stratification. Results suggest a link between high school training and jobs in blue-collar communities that structures patterns of gender inequality into early adulthood. Although high school training in blue-collar communities reduced both men's and women's odds of four-year college enrollment, it had gender-divergent labor market consequences. Men in blue-collar communities took more blue-collar courses, had higher rates of blue-collar employment, and earned similar wages relative to otherwise comparable men from non-blue-collar communities. Women were less likely to work and to be employed in professional occupations, and they suffered severe wage penalties relative to their male peers and women from non-blue-collar communities. These relationships were due partly to high schools in blue-collar communities offering more blue-collar and fewer advanced college-preparatory courses. This curricular tradeoff may benefit men, but it appears to disadvantage women.

We introduce the SRI speech-based collaborative learning corpus, a novel collection designed for the investigation and measurement of how students collaborate together in small groups. This is a multi-speaker corpus containing high-quality audio recordings of middle school students working in groups of three to solve mathematical problems. Each student was recorded via a head-mounted noise-cancelling microphone. Each group was also recorded via a stereo microphone placed nearby. A total of 80 sessions were collected with the participation of 134 students. The average duration of a session was 20 minutes. All students spoke English; for some students, English was a second language. Sessions have been annotated with time stamps to indicate which mathematical problem the students were solving and which student was speaking. Sessions have also been hand annotated with common indicators of collaboration for each speaker (e.g., inviting others to contribute, planning) and the overall collaboration quality for each problem. The corpus will be useful to education researchers interested in collaborative learning and to speech researchers interested in children's speech, speech analytics, and speech diarization. The corpus, both audio and annotation, will be made available to researchers.

Collaborative learning is a key skill for student success, but simultaneous monitoring of multiple small groups is untenable for teachers. This study investigates whether automatic audiobased monitoring of interactions can predict collaboration quality. Data consist of hand-labeled 30-second segments from audio recordings of students as they collaborated on solving math problems. Two types of features were explored: speech activity features, which were computed at the group level; and prosodic features (pitch, energy, durational, and voice quality patterns), which were computed at the speaker level. For both feature types, normalized and unnormalized versions were investigated; the latter facilitate real-time processing applications. Results using boosting classifiers, evaluated by F-measure and accuracy, reveal that (1) both speech activity and prosody features predict quality far beyond chance using majority-class approach; (2) speech activity features are the better predictors overall, but class performance using prosody shows potential synergies; and (3) it may not be necessary to session-normalize features by speaker. These novel results have impact for educational settings, where the approach could support teachers in the monitoring of group dynamics, diagnosis of issues, and development of pedagogical intervention plans. © 2016, International Speech Communications Association. All rights reserved.

Studies of skill development often describe a process of cumulative advantage, in which small differences in initial skill compound over time, leading to increasing skill gaps between those with an initial advantage and those without. We offer evidence of a similar phenomenon accounting for differential patterns of research skill development in graduate students over an academic year and explore differences in socialization that accompany diverging developmental trajectories. As predicted, quantitative analysis indicated large effect sizes for skill gains after controlling for initial performance levels. Qualitative analyses indicated that students with initial advantages were more likely to report greater demands of independence by their advisors and see more extensive value in research tasks comparable to those assigned their less skilled peers.

Cumulatively, participation in optional science learning experiences in school, after school, at home, and in the community may have a large impact on student interest in and knowledge of science. Therefore, interventions can have large long-term effects if they change student choice preferences for such optional science learning experiences. To be able to track K-12 students' intentions to participate in optional science learning experiences, we developed a new measure of science choice preferences in early adolescence. The present study with 284 5th and 894 6th graders from diverse school contexts (i.e., from the Bay Area and the Pittsburgh area) illustrates the value of applying Item Response Theory analyses to develop a measurement instrument. These analyses established the overall reliability of the instrument and each item in the scale, as well as the generalizability of the scale and individual items across subgroups by gender, by ethnicity, and by achievement levels in science. Further, preferences to participate in science were shown to be separate from preferences to participate in mathematics, engineering, or medicine. Finally, the science choice preferences measure is validated through replicated positive correlations with levels of science interest, self-efficacy, and learning achievement. (c) 2015 Wiley Periodicals, Inc. J Res Sci Teach 52: 686-709, 2015.

this study, we examined the influence of achievement goals and scaffolding on self-regulated learning (SRL) and achievement within MetaTutor, a multi-agent intelligent tutoring system. Eighty-three (N = 83) undergraduate students were randomly assigned to either a control or prompt and feedback condition and engaged in a 1-h learning session with MetaTutor to learn about the human circulatory system. Process and product data were collected from all participants prior to, during, and following the session. MANCOVA analyses revealed that students in the prompt and feedback condition deployed more SRL strategies and spent more time viewing relevant science material compared to students in the control condition. Results also revealed a significant interaction between achievement goals and condition on achievement outcomes, such that learners adopting a dominant performance-approach demonstrated higher achievement in the prompt and feedback condition. Findings are discussed in relation to the role of motivation in self-regulated learning within computer-based learning environments. Implications for the design of pedagogical agents are also discussed. (C) 2015 Elsevier Ltd. All rights reserved.

The Open Quantum Materials Database (OQMD) is a high-throughput database currently consisting of nearly 300,000 density functional theory (DFT) total energy calculations of compounds from the Inorganic Crystal Structure Database (ICSD) and decorations of commonly occurring crystal structures. To maximise the impact of these data, the entire database is being made available, without restrictions, at www.oqmd.org/download. In this paper, we outline the structure and contents of the database, and then use it to evaluate the accuracy of the calculations therein by comparing DFT predictions with experimental measurements for the stability of all elemental ground-state structures and 1,670 experimental formation energies of compounds. This represents the largest comparison between DFT and experimental formation energies to date. The apparent mean absolute error between experimental measurements and our calculations is 0.096 eV/atom. In order to estimate how much error to attribute to the DFT calculations, we also examine deviation between different experimental measurements themselves where multiple sources are available, and find a surprisingly large mean absolute error of 0.082 eV/atom. Hence, we suggest that a significant fraction of the error between DFT and experimental formation energies may be attributed to experimental uncertainties. Finally, we evaluate the stability of compounds in the OQMD (including compounds obtained from the ICSD as well as hypothetical structures), which allows us to predict the existence of similar to 3,200 new compounds that have not been experimentally characterised and uncover trends in material discovery, based on historical data available within the ICSD.

This article provides an overview of learning progressions (LP) and assesses the potential of this line of research to improve geography education. It presents the merits and limitations of three of the most common approaches used to conduct LP research and draws on one approach to propose a first draft of a LP on map reading and interpretation. It also highlights findings from LP research that may be especially significant for future work in geography education. The article concludes with a description of a new research project, GeoProgressions, to build capacity for LP research in geography.