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Rogiers, Amelie; Merchie, Emmelien; van Keer, Hilde – Frontline Learning Research, 2020
The current study uncovers secondary school students' actual use of text-learning strategies during an individual learning task by means of a concurrent self-reported thinking aloud procedure. Think-aloud data of 51 participants with different learning strategy profiles, distinguished based on a retrospective self-report questionnaire (i.e., 15…
Descriptors: Secondary School Students, Learning Strategies, Protocol Analysis, Research Methodology
Kinnebrew John S.; Killingsworth, Stephen S.; Clark, Douglas B.; Biswas, Gautam; Sengupta, Pratim; Minstrell, James; Martinez-Garza, Mario; Krinks, Kara – IEEE Transactions on Learning Technologies, 2017
Digital games can make unique and powerful contributions to K-12 science education, but much of that potential remains unrealized. Research evaluating games for learning still relies primarily on pre- and post-test data, which limits possible insights into more complex interactions between game design features, gameplay, and formal assessment.…
Descriptors: Computer Games, Educational Games, Data Analysis, Science Education
Pennings, Helena J. M. – Complicity: An International Journal of Complexity and Education, 2017
In the present study, complex dynamic systems theory and interpersonal theory are combined to describe the teacher-student interactions of two teachers with different interpersonal styles. The aim was to show and explain the added value of looking at different steps in the analysis of behavioral time-series data (i.e., observations of teacher and…
Descriptors: Teacher Student Relationship, Case Studies, Interpersonal Relationship, Questionnaires
Sorensen, Lucy C. – Educational Administration Quarterly, 2019
Purpose: In an era of unprecedented student measurement and emphasis on data-driven educational decision making, the full potential for using data to target resources to students has yet to be realized. This study explores the utility of machine-learning techniques with large-scale administrative data to identify student dropout risk. Research…
Descriptors: At Risk Students, Dropouts, Data Collection, Data Analysis
Fröjd, Sari; Saaristo, Vesa; Ståhl, Timo – School Leadership & Management, 2014
Monitoring bullying behaviours is the key aspect of a successful anti-bullying intervention. Questionnaires among pupils and principals of the same schools were utilised to measure the agreement between pupil-reported frequency and principals' estimations of the prevalence of frequent bullying in the same schools and to identify monitoring methods…
Descriptors: Bullying, Student Behavior, Intervention, Questionnaires
Lee, Jihyun – Written Communication, 2013
Based on eighth-grade writing assessment data from the 1998 (N = 20,586) and 2007 (N = 139,900) National Assessment of Educational Progress (NAEP), this study examines the relationships among students' writing attitudes, learning-related behaviors, and gender in relation to writing performance. Overall, the effects of attitudes were slightly…
Descriptors: National Competency Tests, Writing Tests, Data Analysis, Writing Evaluation
A Contextualized, Differential Sequence Mining Method to Derive Students' Learning Behavior Patterns
Kinnebrew, John S.; Loretz, Kirk M.; Biswas, Gautam – Journal of Educational Data Mining, 2013
Computer-based learning environments can produce a wealth of data on student learning interactions. This paper presents an exploratory data mining methodology for assessing and comparing students' learning behaviors from these interaction traces. The core algorithm employs a novel combination of sequence mining techniques to identify deferentially…
Descriptors: Data Analysis, Middle School Students, Information Retrieval, Student Behavior
Sabourin, Jennifer L.; Rowe, Jonathan P.; Mott, Bradford W.; Lester, James C. – Journal of Educational Data Mining, 2013
Over the past decade, there has been growing interest in real-time assessment of student engagement and motivation during interactions with educational software. Detecting symptoms of disengagement, such as off-task behavior, has shown considerable promise for understanding students' motivational characteristics during learning. In this paper, we…
Descriptors: Student Behavior, Classification, Learner Engagement, Data Analysis
Rai, Dovan; Gong, Yue; Beck, Joseph E. – International Working Group on Educational Data Mining, 2009
Student modeling is a widely used approach to make inference about a student's attributes like knowledge, learning, etc. If we wish to use these models to analyze and better understand student learning there are two problems. First, a model's ability to predict student performance is at best weakly related to the accuracy of any one of its…
Descriptors: Data Analysis, Statistical Analysis, Probability, Models
Feldman, Betsy J.; Masyn, Katherine E.; Conger, Rand D. – Developmental Psychology, 2009
Analyzing problem-behavior trajectories can be difficult. The data are generally categorical and often quite skewed, violating distributional assumptions of standard normal-theory statistical models. In this article, the authors present several currently available modeling options, all of which make appropriate distributional assumptions for the…
Descriptors: Structural Equation Models, Behavior Problems, Student Behavior, Adolescents
Welker, Heidi Stevenson – ProQuest LLC, 2010
Cyberbullying on social networking sites such as MySpace and Facebook has had negative effects on children at school. Cyberbullying disruption during the school day adds to the complexity of maintaining school operations, safety, and academic achievement. With the advancement of technology, there is a gap in the literature on the disruption in…
Descriptors: Suburban Schools, Student Behavior, Social Change, Data Analysis
Lynch, Collin F., Ed.; Merceron, Agathe, Ed.; Desmarais, Michel, Ed.; Nkambou, Roger, Ed. – International Educational Data Mining Society, 2019
The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…
Descriptors: Data Collection, Data Analysis, Information Retrieval, Content Analysis
Barnes, Tiffany, Ed.; Desmarais, Michel, Ed.; Romero, Cristobal, Ed.; Ventura, Sebastian, Ed. – International Working Group on Educational Data Mining, 2009
The Second International Conference on Educational Data Mining (EDM2009) was held at the University of Cordoba, Spain, on July 1-3, 2009. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented…
Descriptors: Data Analysis, Educational Research, Conferences (Gatherings), Foreign Countries