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50 Years of ERIC
50 Years of ERIC
The Education Resources Information Center (ERIC) is celebrating its 50th Birthday! First opened on May 15th, 1964 ERIC continues the long tradition of ongoing innovation and enhancement.

Learn more about the history of ERIC here. PDF icon

Showing all 6 results
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
Koedinger, Kenneth R.; McLaughlin, Elizabeth A.; Stamper, John C. – International Educational Data Mining Society, 2012
Student modeling plays a critical role in developing and improving instruction and instructional technologies. We present a technique for automated improvement of student models that leverages the DataShop repository, crowd sourcing, and a version of the Learning Factors Analysis algorithm. We demonstrate this method on eleven educational…
Descriptors: Educational Technology, Intelligent Tutoring Systems, Educational Improvement, Mathematics
Xu, Yanbo; Mostow, Jack – International Educational Data Mining Society, 2012
A long-standing challenge for knowledge tracing is how to update estimates of multiple subskills that underlie a single observable step. We characterize approaches to this problem by how they model knowledge tracing, fit its parameters, predict performance, and update subskill estimates. Previous methods allocated blame or credit among subskills…
Descriptors: Teaching Methods, Comparative Analysis, Prediction, Mathematics
Beheshti, Behzad; Desmarais, Michel C.; Naceur, Rhouma – International Educational Data Mining Society, 2012
Identifying the skills that determine the success or failure to exercises and question items is a difficult task. Multiple skills may be involved at various degree of importance, and skills may overlap and correlate. In an effort towards the goal of finding the skills behind a set of items, we investigate two techniques to determine the number of…
Descriptors: Prediction, Evaluation, Algebra, Mathematics
Rau, Martina A.; Pardos, Zachary A. – International Educational Data Mining Society, 2012
The goal of this paper is to use Knowledge Tracing to augment the results obtained from an experiment that investigated the effects of practice schedules using an intelligent tutoring system for fractions. Specifically, this experiment compared different practice schedules of multiple representations of fractions: representations were presented to…
Descriptors: Intelligent Tutoring Systems, Mathematics, Knowledge Level, Scheduling
Wang, Yutao; Heffernan, Neil T. – International Educational Data Mining Society, 2012
The field of educational data mining has been using the Knowledge Tracing model, which only look at the correctness of student first response, for tracking student knowledge. Recently, lots of other features are studied to extend the Knowledge Tracing model to better model student knowledge. The goal of this paper is to analyze whether or not the…
Descriptors: Reaction Time, Students, Knowledge Level, Models