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Showing all 6 results
Download full textBouchet, Francois; Azevedo, Roger; Kinnebrew, John S.; Biswas, Gautam – International Educational Data Mining Society, 2012
Identification of student learning behaviors, especially those that characterize or distinguish students, can yield important insights for the design of adaptation and feedback mechanisms in Intelligent Tutoring Systems (ITS). In this paper, we analyze trace data to identify distinguishing patterns of behavior in a study of 51 college students…
Descriptors: Tutoring, Feedback (Response), Intelligent Tutoring Systems, Academic Achievement
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
Gonzalez-Brenes, Jose P.; Mostow, Jack – International Educational Data Mining Society, 2012
This work describes a unified approach to two problems previously addressed separately in Intelligent Tutoring Systems: (i) Cognitive Modeling, which factorizes problem solving steps into the latent set of skills required to perform them; and (ii) Student Modeling, which infers students' learning by observing student performance. The practical…
Descriptors: Intelligent Tutoring Systems, Academic Achievement, Bayesian Statistics, Tutors
Baker, Ryan S. J. d.; Gowda, Sujith M.; Wixon, Michael; Kalka, Jessica; Wagner, Angela Z.; Salvi, Aatish; Aleven, Vincent; Kusbit, Gail W.; Ocumpaugh, Jaclyn; Rossi, Lisa – International Educational Data Mining Society, 2012
In recent years, the usefulness of affect detection for educational software has become clear. Accurate detection of student affect can support a wide range of interventions with the potential to improve student affect, increase engagement, and improve learning. In addition, accurate detection of student affect could play an essential role in…
Descriptors: Academic Achievement, Algebra, Tutors, Computer Software
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
Pardos, Zachary A.; Wang, Qing Yang; Trivedi, Shubhendu – International Educational Data Mining Society, 2012
In recent years, the educational data mining and user modeling communities have been aggressively introducing models for predicting student performance on external measures such as standardized tests as well as within-tutor performance. While these models have brought statistically reliable improvement to performance prediction, the real world…
Descriptors: High Stakes Tests, Prediction, Standardized Tests, Simulation
