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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
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
Wang, Lisa; Sy, Angela; Liu, Larry; Piech, Chris – International Educational Data Mining Society, 2017
Modeling student knowledge while students are acquiring new concepts is a crucial stepping stone towards providing personalized automated feedback at scale. We believe that rich information about a student's learning is captured within her responses to open-ended problems with unbounded solution spaces, such as programming exercises. In addition,…
Descriptors: Online Courses, Knowledge Level, Pedagogical Content Knowledge, Scaffolding (Teaching Technique)
Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection