Abstract
In typical assessment student are not given feedback, as it is harder to predict student knowledge if it is changing during testing. Intelligent Tutoring systems, that offer assistance while the student is participating, offer a clear benefit of assisting students, but how well can they assess students? What is the trade off in terms of assessment accuracy if we allow student to be assisted on an exam. In a prior study, we showed the assistance with assessments quality to be equal. In this work, we introduce a more sophisticated method by which we can ensemble together multiple models based upon clustering students. We show that in fact, the assessment quality as determined by the assistance data is a better estimator of student knowledge. The implications of this study suggest that by using computer tutors for assessment, we can save much instructional time that is currently used for just assessment.
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References
Feng, M., Heffernan, N.T., Koedinger, K.R.: Addressing the assessment challenge in an online system that tutors as it assesses. User Modeling and User-Adapted Interaction: The Journal of Personalization Research 19(3) (2009)
Feng, M., Heffernan, N.T.: Can We Get Better Assessment From A Tutoring System Compared to Traditional Paper Testing? Can We Have Our Cake (better assessment) and Eat it too (student learning during the test). In: Proceedings of the 3rd International Conference on Educational Data Mining?, pp. 41–50 (2010)
Corbett, A.T., Anderson, J.R.: Knowledge Tracing: Modeling the Acquisition of Procedural Knowledge. User Modeling and User Adapted Interaction 4, 253–278 (1995)
Pardos, Z.A., Heffernan, N.T.: Using HMMs and bagged decision trees to leverage rich features of user and skill from an intelligent tutoring system dataset. Journal of Machine Learning Research C & WP (in press 2011)
Baker, R.S.J.d, Corbett, A. T., Aleven, V.: More Accurate Student Modeling Through Contextual Estimation of Guess and Slip Probabilities in Bayesian Knowledge Tracing. In: Proceedings of the 14th International Conference on Artificial Intelligence in Education, Brightion, UK, pp. 531–538.
Grigerenko, E.L., Steinberg, R.J.: Dynamic Testing. Psychological Bulletin 124, 75–111 (1998)
Campione, J. C., Brown, A. L.: Dynamic Assessment: One Approach and some Initial Data. Technical Report. No. 361. Cambridge, MA. Illinois University, Urbana, Center for the Study of Reading. ED 269735 (1985)
Fuchs, L.S., Compton, D.L., Fuchs, D., Hollenbeck, K.N., Craddock, C.F., Hamlett, C.L.: Dynamic Assessment of Algebraic Learning in Predicting Third Graders’ of Mathematical Problem Solving. Journal of Educational Psychology 100(4), 829–850 (2008)
Fuchs, D., Fuchs, L.S., Compton, D.L., Bouton, B., Caffrey, E., Hill, L.: Dynamic Assessment as Responsiveness to Intervention. Teaching Exceptional Children 39(5), 58–63 (2007)
Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)
Baker, R.S., Corbett, A.T., Koedinger, K.R., Wagner, A.Z.: Off-task behaviour in the Cognitive Tutor Classroom: When Students “game the system”. In: Proceedings of the ACM CHI 2004: Computer - Human Interaction, pp. 383–390. ACM, New York (2004)
Dietterich, T.G.: Ensemble Methods in Machine Learning. In: Kittler, J., Roli, F. (eds.) First International workshop on Multiple Classifier Systems. LNCS, pp. 1–15. Springer, New York (2000)
Dietterich, T.G.: An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization. Machine Learning 40, 139–157 (2000)
Brown, G., Wyatt, J.L., Tino, P.: Managing Diversity in Regression Ensembles. Journal of Machine Learning Research 6, 1621–1650 (2005)
Trivedi, S., Pardos, Z.A., Heffernan, N.T.: The Utility of Clustering in Prediction Tasks. In: Submission to the 17th Conference on Knowledge Discovery and Data Mining (in submission, 2011)
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Trivedi, S., Pardos, Z.A., Heffernan, N.T. (2011). Clustering Students to Generate an Ensemble to Improve Standard Test Score Predictions. In: Biswas, G., Bull, S., Kay, J., Mitrovic, A. (eds) Artificial Intelligence in Education. AIED 2011. Lecture Notes in Computer Science(), vol 6738. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21869-9_49
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DOI: https://doi.org/10.1007/978-3-642-21869-9_49
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