Conference Paper

Analyzing Fine-Grained Skill Models Using Bayesian and Mixed Effects Methods.

Conference: Artificial Intelligence in Education, Building Technology Rich Learning Contexts That Work, Proceedings of the 13th International Conference on Artificial Intelligence in Education, AIED 2007, July 9-13, 2007, Los Angeles, California, USA
Source: DBLP

ABSTRACT Two modelling methods were employed to answer the same research question of how accurate the various grained WPI 1, 5, 39 and 106 skill models are at assessing student knowledge in the ASSISTment online tutoring system and predicting their performance on the 2005 state MCAS test. One method, used by the second author, is mixed effect statistical modelling. The first author evaluated the problem with a Bayesian networks machine learning approach. We compare the two results to identify benefits and drawbacks of either method and to find out if the two results agree. We report that both methods showed compelling similarity in results especially with regard to residuals on the test. Our analysis of these residuals and our online skills allows us to better understand our model and conclude with recommendations for improving the tutoring system, as well as implications for state testing programs.

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    Educational Data Mining 2008, The 1st International Conference on Educational Data Mining, Montreal, Québec, Canada, June 20-21, 2008. Proceedings; 01/2008
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    ABSTRACT: Composite concepts result from the integration of multiple basic concepts by students to form high- level knowledge, so information about how students learn composite concepts can be used by instructors to facilitate students' learning, and the ways in which computational techniques can assist the study of the integration process are therefore intriguing for learning, cognition, and computer scientists. We provide an exploration of this problem using heuristic methods, search methods, and machine-learning techniques, while employing Bayesian networks as the language for representing the student models. Given experts' expectation about students and simulated students' responses to test items that were designed for the concepts, we try to find the Bayesian-network structure that best represents how students learn the composite concept of interest. The experiments were conducted with only simulated students. The accuracy achieved by the proposed classification methods spread over a wide range, depending on the quality of collected input evidence. We discuss the experimental procedures, compare the experimental results observed in certain experiments, provide two ways to analyse the influences of Q-matrices on the experimental results, and we hope that this simulation-based experience may contribute to the endeavours in mapping the human learning process.
    I. J. Artificial Intelligence in Education. 01/2008; 18:237-285.

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