Abstract
In our previous works, we presented Logic-Muse as an Intelligent Tutoring System that helps learners improve logical reasoning skills in multiple contexts. Logic-Muse components were validated and argued by experts throughout the designing process (ITS researchers, logicians and reasoning psychologists). A Bayesian network with expert validation has been developed and used in a Bayesian Knowledge Tracing (BKT) process that allows the inference of the learner’s behaviour. This paper presents an evaluation of the learner components of Logic-Muse. We conducted a study and collected data from nearly 300 students who processed 48 reasoning activities. This data was used in the development a psychometric model, a key element for initializing the learner’s model and for validating and improve the structure of the initial Bayesian network built with experts.
NSERC Discovery Grant.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Baker, R.S.J., Corbett, A.T., Aleven, V.: More accurate student modeling through contextual estimation of slip and guess probabilities in bayesian knowledge tracing. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 406–415. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69132-7_44
Beck, J.E., Chang, K.: Identifiability: a fundamental problem of student modeling. In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 137–146. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73078-1_17
Chen, J., de la Torre, J., Zhang, Z.: Relative and absolute fit evaluation in cognitive diagnosis modeling. J. Educ. Meas. 50(2), 123–140 (2013)
Conati, C., Gertner, A., Vanlehn, K.: Using bayesian networks to manage uncertainty in student modeling. User Model. User-Adapt. Interact. 12(4), 371–417 (2002)
Corbett, A.T., Anderson, J.R.: Knowledge tracing: modeling the acquisition of procedural knowledge. User Model. User-Adapt. Interact. 4(4), 253–278 (1994)
Cummins, D.D., Lubart, T., Alksnis, O., Rist, R.: Conditional reasoning and causation. Memory Cogn 19(3), 274–282 (1991)
De La Torre, J.: A cognitive diagnosis model for cognitively based multiple-choice options. Appl. Psychol. Meas. 33(3), 163–183 (2009)
De Neys, W., Schaeken, W., D’Ydewalle, G.: Inference suppression and semantic memory retrieval: every counterexample counts. Memory Cogn. 31(4), 581–595 (2003)
Gilovich, T., Griffin, D., Kahneman, D.: Heuristics and Biases: The Psychology of Intuitive Judgment. Cambridge University Press, Cambridge (2002)
Groß, J., Robitzsch, A., George, A.: Cognitive diagnosis models for baseline testing of educational standards in math. J. Appl. Stat. 43(1), 229–243 (2016)
Guilford, J.P., Lyons, T.C.: On determining the reliability and significance of a tetrachoric coefficient of correlation. Psychometrika 7(4), 243–249 (1942)
Kasurinen, J., Nikula, U.: Estimating programming knowledge with bayesian knowledge tracing. In: ACM SIGCSE Bulletin, vol. 41, pp. 313–317. ACM (2009)
Markovits, H.: The development of abstract conditional reasoning. In: The Development of Thinking and Reasoning, pp. 83–104. Psychology Press (2013)
Markovits, H., Vachon, R.: Reasoning with contrary-to-fact propositions. J. Exp. Child Psychol. 47(3), 398–412 (1989)
Nkambou, R., Mizoguchi, R., Bourdeau, J.: Advances in Intelligent Tutoring Systems, vol. 308. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14363-2
Tato, A., Nkambou, R., Brisson, J., Robert, S.: Predicting learner’s deductive reasoning skills using a bayesian network. In: André, E., Baker, R., Hu, X., Rodrigo, M.M.T., du Boulay, B. (eds.) AIED 2017. LNCS (LNAI), vol. 10331, pp. 381–392. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61425-0_32
Thompson, V.A.: Interpretational factors in conditional reasoning. Memory Cogn. 22(6), 742–758 (1994)
Yudelson, M.V., Koedinger, K.R., Gordon, G.J.: Individualized bayesian knowledge tracing models. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS (LNAI), vol. 7926, pp. 171–180. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39112-5_18
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Nkambou, R., Brisson, J., Robert, S., Tato, A. (2021). Learning Logical Reasoning : Improving the Student Model with a Data Driven Approach. In: Cristea, A.I., Troussas, C. (eds) Intelligent Tutoring Systems. ITS 2021. Lecture Notes in Computer Science(), vol 12677. Springer, Cham. https://doi.org/10.1007/978-3-030-80421-3_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-80421-3_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-80420-6
Online ISBN: 978-3-030-80421-3
eBook Packages: Computer ScienceComputer Science (R0)