The Experts below are selected from a list of 99255 Experts worldwide ranked by ideXlab platform
Peter Brusilovsky - One of the best experts on this subject based on the ideXlab platform.
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AIED (1) - Annotated Examples and Parameterized Exercises: Analyzing Students’ Behavior Patterns
Lecture Notes in Computer Science, 2019Co-Authors: Mehrdad Mirzaei, Shaghayegh Sahebi, Peter BrusilovskyAbstract:Recent studies of Student problem-solving Behavior have shown stable Behavior patterns within Student groups. In this work, we study patterns of Student Behavior in a richer self-organized practice context where Student worked with a combination of problems to solve and worked examples to study. We model Student Behavior in the form of vectors of micro-patterns and examine Student Behavior stability in various ways via these vectors. To discover and examine global Behavior patterns associated with groups of Students, we cluster Students according to their Behavior patterns and evaluate these clusters in accordance with Student performance.
Isaac A. Friedman - One of the best experts on this subject based on the ideXlab platform.
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Student Behavior Patterns Contributing to Teacher Burnout
The Journal of Educational Research, 1995Co-Authors: Isaac A. FriedmanAbstract:Abstract This article reports on two studies that examined how typical Student Behavior patterns contribute to predicting burnout among teachers in general (Study 1) and among male and female teachers possessing different pupil control ideologies (Study 2). The sample for Study 1 involved 348 teachers from both religious and secular schools in Israel and 356 of their Students. The sample for Study 2 involved 391 elementary and secondary schoolteachers (122 were classified “humanistic” and 119 “custodial”). The teachers sampled completed a questionnaire composed of an adapted version of the Maslach Burnout Inventory, the Pupil Behavior Patterns Scale (Studies 1 and 2), and an adapted version of the Pupil Control Ideology scale (Study 2). The Students in Study 1 filled out an open-ended questionnaire. The typical Student Behaviors—disrespect, inattentiveness and sociability—accounted for 22% of teacher burnout variance for the whole sample and for 33% of burnout variance in teachers in religious schools. Hu...
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Conceptualizing and Measuring Teacher-Perceived Student Behaviors: Disrespect, Sociability, and Attentiveness:
Educational and Psychological Measurement, 1994Co-Authors: Isaac A. FriedmanAbstract:This research investigated the patterns of Student classroom Behavior as perceived by their teachers and validated a scale for measuring such Behavior called Pupil Behavior Patterns (PBP). Facet theory analytic techniques and factor analysis were used. It was found that two facets provide the conceptual basis for analyzing Student Behavior patterns: (a) the adaptability of the Student Behavior (responsive or unresponsive) and (b) the foci of such Behavior (internal: within the peer group; or external: geared toward the teacher or other school authority). The scale for measuring Student Behaviors (PBP) consists of three subscales: (a) Disrespect (Students' respect or lack of respect for both teachers and members of their peer group), (b) Sociability (the informal interpersonal relationships among Students and between teachers and Students), and (c) Attentiveness (Student willingness and receptiveness to learning as well as learning ability).
Gautam Biswas - One of the best experts on this subject based on the ideXlab platform.
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mining Student Behavior models in learning by teaching environments
Educational Data Mining, 2008Co-Authors: Hogyeong Jeong, Gautam BiswasAbstract:Abstract. This paper discusses our approach to building models and analyzing Student Behaviors in different versions of our learning by teaching environment where Students learn by teaching a computer agent named Betty using a visual concept map representation. We have run studies in fifth grade classrooms to compare the different versions of the system. Students’ interactions on the system, captured in log files represent their performance in generating the causal concept map structures and their activities in using the different tools provided by the system. We discuss methods for analyzing Student Behaviors and linking them to Student performance. At the core of this approach is a hidden Markov model methodology that builds Students’ Behavior models from data collected in the log files. We discuss our modeling algorithm and the interpretation of the models.
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EDM - Mining Student Behavior Models in Learning-by-Teaching Environments
2008Co-Authors: Hogyeong Jeong, Gautam BiswasAbstract:Abstract. This paper discusses our approach to building models and analyzing Student Behaviors in different versions of our learning by teaching environment where Students learn by teaching a computer agent named Betty using a visual concept map representation. We have run studies in fifth grade classrooms to compare the different versions of the system. Students’ interactions on the system, captured in log files represent their performance in generating the causal concept map structures and their activities in using the different tools provided by the system. We discuss methods for analyzing Student Behaviors and linking them to Student performance. At the core of this approach is a hidden Markov model methodology that builds Students’ Behavior models from data collected in the log files. We discuss our modeling algorithm and the interpretation of the models.
Elise T. Pas - One of the best experts on this subject based on the ideXlab platform.
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examining how proactive management and culturally responsive teaching relate to Student Behavior implications for measurement and practice
School Psychology Review, 2018Co-Authors: Kristine E Larson, Elise T. Pas, Catherine P. Bradshaw, Michael S Rosenberg, Norma L DayvinesAbstract:Abstract The discipline gap between White Students and African American Students has increased demand for teacher training in culturally responsive and Behavior management practices. Extant research, however, is inconclusive about how culturally responsive teaching practices relate to Student Behavior or how to assess using such practices in the classroom. Identifying proactive Behavior management and culturally responsive teaching practices that are associated with positive Student Behavior may inform teacher training and bolster efforts to reduce disparities in Behavioral and academic performance. The current study examined the association between Student Behaviors and the observed use of and teacher self-reported efficacy in using culturally responsive teaching and proactive Behavior management practices. Data were collected from 274 teachers in 18 schools. Structural equation modeling indicated a statistically significant association between observations of culturally responsive teaching and proactive...
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Examining the validity of office discipline referrals as an indicator of Student Behavior problems
Psychology in the Schools, 2011Co-Authors: Elise T. Pas, Catherine P. Bradshaw, Mary M. MitchellAbstract:Office discipline referral (ODR) data are increasingly used to monitor Student Behavior problems and the impact of interventions, but there has been limited research examining their validity. The current study examined the concordance of ODRs with teacher ratings of Student Behavior using data on 8,645 children in 335 classrooms at 21 elementary schools. The results of a variety of analyses (e.g., correlations, multivariate analysis of variance, receiver operating characteristics) suggested that ODRs are moderately valid and reliable. Multilevel analyses revealed that teacher ratings of disruptive Behaviors were significantly associated with ODRs, even after controlling for other Student-, classroom-, and school-level factors. These findings suggest that ODRs are moderately valid indicators of Student Behavior problems and may be an efficient source of information for use in school-based research and data-based decision-making. © 2011 Wiley Periodicals, Inc.
Ruimin Shen - One of the best experts on this subject based on the ideXlab platform.
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ICASSP - Intelligent Student Behavior Analysis System for Real Classrooms
ICASSP 2020 - 2020 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2020Co-Authors: Rui Zheng, Fei Jiang, Ruimin ShenAbstract:In this paper, we design an intelligent Student Behavior analysis system for recorded classrooms, which automatically detects hand-raising, standing, and sleeping Behaviors of Students. Detecting these Behaviors is quite challenging mainly due to various scale Behaviors, low resolution, and imbalanced Behavior samples. To overcome the above-mentioned challenges, we first build a large-scale Student Behavior corpus from thirty schools, labeling these Behaviors using bounding boxes frame-by-frame, which changes the Behavior recognition problem into object detections. Then, we propose an improved Faster R-CNN, a classical object detection model, for Student Behavior analysis. Specifically, we first present a novel scale-aware detection head to overcome scale variations. Secondly, we propose a new feature fusion strategy to detect low-resolution Behaviors while introduces little computation overhead. Thirdly, we utilize OHEM (Online Hard Example Mining) to alleviate severe class imbalances. Experiment results on our real corpus are increased by 3.4% mAP while maintaining a fast speed.
