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Picones, Gio; PaaBen, Benjamin; Koprinska, Irena; Yacef, Kalina – International Educational Data Mining Society, 2022
In this paper, we propose a novel approach to combine domain modelling and student modelling techniques in a single, automated pipeline which does not require expert knowledge and can be used to predict future student performance. Domain modelling techniques map questions to concepts and student modelling techniques generate a mastery score for a…
Descriptors: Prediction, Academic Achievement, Learning Analytics, Concept Mapping
Fang, Ying; Nye, Benjamin; Pavlik, Philip; Xu, Yonghong Jade; Graesser, Arthur; Hu, Xiangen – International Educational Data Mining Society, 2017
Student persistence in online learning environments has typically been studied at the macro-level (e.g., completion of an online course, number of academic terms completed, etc.). The current examines student persistence in an adaptive learning environment, ALEKS (Assessment and LEarning in Knowledge Spaces). Specifically, the study explores the…
Descriptors: Learning Processes, Academic Persistence, Correlation, Academic Achievement
Rollinson, Joseph; Brunskill, Emma – International Educational Data Mining Society, 2015
At their core, Intelligent Tutoring Systems consist of a student model and a policy. The student model captures the state of the student and the policy uses the student model to individualize instruction. Policies require different properties from the student model. For example, a mastery threshold policy requires the student model to have a way…
Descriptors: Prediction, Models, Educational Policy, Intelligent Tutoring Systems