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Gitinabard, Niki; Gao, Zhikai; Heckman, Sarah; Barnes, Tiffany; Lynch, Collin F. – Journal of Educational Data Mining, 2023
Few studies have analyzed students' teamwork (pairwork) habits in programming projects due to the challenges and high cost of analyzing complex, long-term collaborative processes. In this work, we analyze student teamwork data collected from the GitHub platform with the goal of identifying specific pair teamwork styles. This analysis builds on an…
Descriptors: Cooperative Learning, Computer Science Education, Programming, Student Projects
Edwards, John; Hart, Kaden; Shrestha, Raj – Journal of Educational Data Mining, 2023
Analysis of programming process data has become popular in computing education research and educational data mining in the last decade. This type of data is quantitative, often of high temporal resolution, and it can be collected non-intrusively while the student is in a natural setting. Many levels of granularity can be obtained, such as…
Descriptors: Data Analysis, Computer Science Education, Learning Analytics, Research Methodology
Zimmermann, Judith; Brodersen, Kay H.; Heinimann, Hans R.; Buhmann, Joachim M. – Journal of Educational Data Mining, 2015
The graduate admissions process is crucial for controlling the quality of higher education, yet, rules-of-thumb and domain-specific experiences often dominate evidence-based approaches. The goal of the present study is to dissect the predictive power of undergraduate performance indicators and their aggregates. We analyze 81 variables in 171…
Descriptors: Undergraduate Students, Graduate Students, Academic Achievement, Prediction
Miller, L. Dee; Soh, Leen-Kiat; Samal, Ashok; Kupzyk, Kevin; Nugent, Gwen – Journal of Educational Data Mining, 2015
Learning objects (LOs) are important online resources for both learners and instructors and usage for LOs is growing. Automatic LO tracking collects large amounts of metadata about individual students as well as data aggregated across courses, learning objects, and other demographic characteristics (e.g. gender). The challenge becomes identifying…
Descriptors: Comparative Analysis, Data Analysis, Hierarchical Linear Modeling, Electronic Learning
Saarela, Mirka; Karkkainen, Tommi – Journal of Educational Data Mining, 2015
Curricula for Computer Science (CS) degrees are characterized by the strong occupational orientation of the discipline. In the BSc degree structure, with clearly separate CS core studies, the learning skills for these and other required courses may vary a lot, which is shown in students' overall performance. To analyze this situation, we apply…
Descriptors: Data Analysis, Academic Achievement, Undergraduate Students, Core Curriculum
Amershi, Saleema; Conati, Cristina – Journal of Educational Data Mining, 2009
In this paper, we present a data-based user modeling framework that uses both unsupervised and supervised classification to build student models for exploratory learning environments. We apply the framework to build student models for two different learning environments and using two different data sources (logged interface and eye-tracking data).…
Descriptors: Supervision, Classification, Models, Educational Environment