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Käser, Tanja; Schwartz, Daniel L. – International Educational Data Mining Society, 2019
Open-ended learning environments (OELEs) allow students to freely interact with the content and to discover important principles and concepts of the learning domain on their own. However, only some students possess the necessary skills for efficient and effective exploration. Guidance in the form of targeted interventions or feedback therefore has…
Descriptors: Educational Environment, Interaction, Cluster Grouping, Models
Niu, Ke; Niu, Zhendong; Zhao, Xiangyu; Wang, Can; Kang, Kai; Ye, Min – International Educational Data Mining Society, 2016
User clustering algorithms have been introduced to analyze users' learning behaviors and help to provide personalized learning guides in traditional Web-based learning systems. However, the explicit and implicit coupled interactions, which means the correlations between user attributes generated from learning actions, are not considered in these…
Descriptors: Web Based Instruction, Student Needs, User Needs (Information), Mathematics
Trivedi, Shubhendu; Pardos, Zachary A.; Sarkozy, Gabor N.; Heffernan, Neil T. – International Educational Data Mining Society, 2012
Learning a more distributed representation of the input feature space is a powerful method to boost the performance of a given predictor. Often this is accomplished by partitioning the data into homogeneous groups by clustering so that separate models could be trained on each cluster. Intuitively each such predictor is a better representative of…
Descriptors: Homogeneous Grouping, Prediction, Tutors, Cluster Grouping