Predicting Second and Third Graders' Reading Comprehension Gains: Observing Students' and Classmates Talk during Literacy Instruction using COLT.

This paper introduces a new observation system that is designed to investigate students' and teachers' talk during literacy instruction, Creating Opportunities to Learn from Text (COLT). Using video-recorded observations of 2nd-3rd grade literacy instruction (N=51 classrooms, 337 students, 151 observations), we found that nine types of student talk ranged from using non-verbal gestures to generating new ideas. The more a student talked, the greater were his/her reading comprehension (RC) gains. Classmate talk also predicted RC outcomes (total effect size=0.27). We found that 11 types of teacher talk ranged from asking simple questions to encouraging students' thinking and reasoning. Teacher talk predicted student talk but did not predict students' RC gains directly. Findings highlight the importance of each student's discourse during literacy instruction, how classmates' talk contributes to the learning environments that each student experiences, and how this affects RC gains, with implications for improving the effectiveness of literacy instruction

Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202

Sensitivity Analysis for a Scenario-Based Reliability Prediction Model

As a popular means for capturing behavioural requirements, scenariosshow how components interact to provide system-level functionality.If component reliability information is available, scenarioscan be used to perform early system reliability assessment. Inprevious work we presented an automated approach for predictingsoftware system reliability that extends a scenario specificationto model (1) the probability of component failure, and (2) scenariotransition probabilities. Probabilistic behaviour models ofthe system are then synthesized from the extended scenario specification.From the system behaviour model, reliability predictioncan be computed. This paper complements our previous work andpresents a sensitivity analysis that supports reasoning about howcomponent reliability and usage profiles impact on the overall systemreliability. For this purpose, we present how the system reliabilityvaries as a function of the components reliabilities and thescenario transition probabilities. Taking into account the concurrentnature of component-based software systems, we also analysethe effect of implied scenarios prevention into the sensitivity analysisof our reliability prediction technique

Generative Roles: Assessing Sustained Involvement in Generativity

Abstract Generative roles refer to observable, behavioral community positions that embody aspects of teaching and nurturing that are central to the concept of generativity. Two studies are presented that describe generative roles in a community sample and provide psychometric data for a short index of generative roles. The first study also provides reliability and validity data from a second informant. The second study examines generative roles at different stages of adolescence and adulthood. Participants were asked 8 yes/no questions about a variety of community roles. The validity of the GRI was supported by significant correlations with the Loyola Generativity Scale, a widely used measure of generative concern (r=.33), and measures of related constructs. The correlations were similar across age categories. The Generative Roles Index has good psychometric qualities and complements existing measures of generativity by providing behavioral, observable data on roles

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science