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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
Weiss, Michael J.; Lockwood, J. R.; McCaffrey, Daniel F. – Journal of Research on Educational Effectiveness, 2016
In the "individually randomized group treatment" (IRGT) experimental design, individuals are first randomly assigned to a treatment arm or a control arm, but then within each arm, are grouped together (e.g., within classrooms/schools, through shared case managers, in group therapy sessions, through shared doctors, etc.) to receive…
Descriptors: Randomized Controlled Trials, Error of Measurement, Control Groups, Experimental Groups
Sherin, Bruce – Journal of the Learning Sciences, 2013
A large body of research in the learning sciences has focused on students' commonsense science knowledge--the everyday knowledge of the natural world that is gained outside of formal instruction. Although researchers studying commonsense science have employed a variety of methods, 1-on-1 clinical interviews have played a unique role. The data…
Descriptors: Informal Education, Computational Linguistics, Transcripts (Written Records), Interviews
Rudner, Lawrence M.; Guo, Fanmin – Journal of Applied Testing Technology, 2011
This study investigates measurement decision theory (MDT) as an underlying model for computer adaptive testing when the goal is to classify examinees into one of a finite number of groups. The first analysis compares MDT with a popular item response theory model and finds little difference in terms of the percentage of correct classifications. The…
Descriptors: Adaptive Testing, Instructional Systems, Item Response Theory, Computer Assisted Testing
Hedges, Larry V. – Journal of Educational and Behavioral Statistics, 2007
A common mistake in analysis of cluster randomized trials is to ignore the effect of clustering and analyze the data as if each treatment group were a simple random sample. This typically leads to an overstatement of the precision of results and anticonservative conclusions about precision and statistical significance of treatment effects. This…
Descriptors: Statistical Significance, Computation, Cluster Grouping, Statistics