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Direct linkSorensen, 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
Gobert, Janice D.; Sao Pedro, Michael; Raziuddin, Juelaila; Baker, Ryan S. – Journal of the Learning Sciences, 2013
We present a method for assessing science inquiry performance, specifically for the inquiry skill of designing and conducting experiments, using educational data mining on students' log data from online microworlds in the Inq-ITS system (Inquiry Intelligent Tutoring System; www.inq-its.org). In our approach, we use a 2-step process: First we use…
Descriptors: Intelligent Tutoring Systems, Science Education, Inquiry, Science Process Skills
Bark, Katie; Stenberg, Molly; Sutherland, Shelly; Hayes, Dayle – Journal of Child Nutrition & Management, 2010
Purpose/Objectives: The purpose of the "Montana Recess Before Lunch Survey" was to explore benefits, challenges, and factors associated with successful implementation of Recess Before Lunch (RBL), from the perspective of school principals. Methods: An online written questionnaire was distributed to all (N = 661) Montana elementary and…
Descriptors: Student Behavior, Nutrition, Wellness, Principals
Tresidder, Gwen – Mathematics Teaching Incorporating Micromath, 2006
When marking GCSE data handling coursework, the author was repeatedly reminded just how poor the level of statistical understanding is among students. In response to a feeling that the teaching of handling data topics was limited, the author and her colleague designed a project with Y8 students to try to teach statistics for a deeper…
Descriptors: Statistics, Mathematics Instruction, Mathematics Teachers, Classroom Techniques
Download full textLynch, 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
L'Engle, Kelly Ladin; Pardun, Carol J.; Brown, Jane D. – Journal of Early Adolescence, 2004
This article describes a protocol that was effective in collecting media-use pattern and sensitive health data from young adolescents. Students from three public school districts in the southeastern United States were recruited to participate in a study of the impact of the media on adolescents' sexual health. Using a 34-page mailed survey,…
Descriptors: Grade 8, Grade 7, Early Adolescents, Sexuality
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
