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Cleuziou, Guillaume; Flouvat, Frédéric – International Educational Data Mining Society, 2021
Improving the pedagogical effectiveness of programming training platforms is a hot topic that requires the construction of fine and exploitable representations of learners' programs. This article presents a new approach for learning program embeddings. Starting from the hypothesis that the function of a program, but also its "style", can…
Descriptors: Programming, Computer Science Education, Electronic Learning, Data Analysis
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Bulathwela, Sahan; Verma, Meghana; Pérez-Ortiz, María; Yilmaz, Emine; Shawe-Taylor, John – International Educational Data Mining Society, 2022
This work explores how population-based engagement prediction can address cold-start at scale in large learning resource collections. The paper introduces: (1) VLE, a novel dataset that consists of content and video based features extracted from publicly available scientific video lectures coupled with implicit and explicit signals related to…
Descriptors: Video Technology, Lecture Method, Data Analysis, Prediction
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Gabbay, Hagit; Cohen, Anat – International Educational Data Mining Society, 2022
The challenge of learning programming in a MOOC is twofold: acquiring programming skills and learning online, independently. Automated testing and feedback systems, often offered in programming courses, may scaffold MOOC learners by providing immediate feedback and unlimited re-submissions of code assignments. However, research still lacks…
Descriptors: Automation, Feedback (Response), Student Behavior, MOOCs
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Tomkins, Sabina; Ramesh, Arti; Getoor, Lise – International Educational Data Mining Society, 2016
With the success and proliferation of Massive Open Online Courses (MOOCs) for college curricula, there is demand for adapting this modern mode of education for high school courses. Online and open courses have the potential to fill a much needed gap in high school curricula, especially in fields such as computer science, where there is shortage of…
Descriptors: Prediction, Pretests Posttests, Electronic Learning, Student Behavior