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Download full textCohausz, Lea; Tschalzev, Andrej; Bartelt, Christian; Stuckenschmidt, Heiner – International Educational Data Mining Society, 2023
Demographic features are commonly used in Educational Data Mining (EDM) research to predict at-risk students. Yet, the practice of using demographic features has to be considered extremely problematic due to the data's sensitive nature, but also because (historic and representation) biases likely exist in the training data, which leads to strong…
Descriptors: Information Retrieval, Data Processing, Pattern Recognition, Information Technology
Direct linkPoitras, Eric; Butcher, Kirsten R.; Orr, Matthew; Hudson, Michelle A.; Larson, Madlyn – Interactive Learning Environments, 2022
This study mined student interactions with visual representations as a means to automate assessment of learning in a complex, inquiry-based learning environment. Log trace data of 143 middle school students' interactions with an interactive map in Research Quest (an inquiry-based, online learning environment) were analyzed. Students used the…
Descriptors: Middle School Students, Electronic Learning, Maps, Science Instruction
Viljaranta, Jaana; Nurmi, Jari-Erik; Aunola, Kaisa; Salmela-Aro, Katariina – Journal of Research on Adolescence, 2009
The present study examined what kinds of patterns of task-values adolescents show, and whether these patterns predict their educational and occupational expectations and school track. Six hundred and fourteen adolescents were examined twice before their transition to secondary education and once thereafter. The clustering-by-cases analyses…
Descriptors: Foreign Countries, Adolescents, Pattern Recognition, Secondary Education
