Educational processes’ guidance based on evolving context prediction in intelligent tutoring systems

Considerable attention has been paid to content adaptation in ITS. However, process-oriented adaptation has been neglected and none of ITS addressed the correlation between the learning and the teaching process. Indeed, uncertainty coming from the dynamic preferences of learners and tutors, the evolutionary cognitive state of the learner and the choice of goals and educational strategies is not well considered in ITS. So, in this paper, a new proposal for guiding and adapting the construction of both learning and pedagogical processes named educational processes in intelligent tutoring system is described and detailed. The research strategy followed is based on a dynamic Bayesian network to predict the educational context, a strategic perspective for modeling educational processes, and guidance algorithms for their development and support. This aims to generate an individualized learning process for each learner by selecting the most appropriate pedagogical process according to the actual preferences of the tutor. Experimental results are given to illustrate the applicability of the proposed solution. The results show an improvement in guiding the learner and the teacher/tutor by considering the evolving and uncertain educational context. Learners will be therefore able to achieve the learning goal more efficiently when the pedagogical process is more adapted to their individual differences. The evaluation of knowledge improvement, the appropriateness of educational recommended context and the prediction effectiveness shows promising results.

[1]  Analía Amandi,et al.  eTeacher: Providing personalized assistance to e-learning students , 2008, Comput. Educ..

[2]  Radek Pelánek,et al.  Bayesian knowledge tracing, logistic models, and beyond: an overview of learner modeling techniques , 2017, User Modeling and User-Adapted Interaction.

[3]  Jim Reye,et al.  Student Modelling Based on Belief Networks , 2004, Int. J. Artif. Intell. Educ..

[4]  Ioannis Hatzilygeroudis.  Using a hybrid rule-based approach in developing an intelligent tutoring system with knowledge acquisition and update capabilities , 2004, Expert Syst. Appl..

[5]  Cristina Conati,et al.  Using Bayesian Networks to Manage Uncertainty in Student Modeling , 2002, User Modeling and User-Adapted Interaction.

[6]  Michael C. Frank,et al.  Three ideal observer models for rule learning in simple languages , 2011, Cognition.

[7]  Bruce Edmonds,et al.  Adaptive Tutoring in an Intelligent Conversational Agent System , 2012, Trans. Comput. Collect. Intell..

[8]  Colette Rolland,et al.  Modeling Goals and Reasoning with Them , 2005 .

[9]  Glenn D. Blank,et al.  Individualizing Tutoring with Learning Style Based Feedback , 2008, Intelligent Tutoring Systems.

[10]  Adrianna Kozierkiewicz-Hetmanska,et al.  Personalisation of Learning Process in Intelligent Tutoring Systems Using Behavioural Measures , 2016, MISSI.

[11]  Peter Brusilovsky,et al.  General Features in Knowledge Tracing to Model Multiple Subskills, Temporal Item Response Theory, and Expert Knowledge , 2014, EDM.

[12]  Joshua B. Tenenbaum,et al.  Bayesian models of human action understanding , 2005, NIPS.

[13]  Helder Coelho,et al.  AMPLIA: A Probabilistic Learning Environment , 2008, Int. J. Artif. Intell. Educ..

[14]  C. Rolland.  Capturing System Intentionality with Maps , 2007 .

[15]  Joachim M. Buhmann,et al.  Modeling Engagement Dynamics in Spelling Learning , 2011, AIED.

[16]  Learning What Works in its from Non-Traditional Randomized Controlled Trial Data , 2011, Int. J. Artif. Intell. Educ..

[17]  Shu-Ling Wang,et al.  The role of feedback and self-efficacy on web-based learning: The social cognitive perspective , 2008, Comput. Educ..

[18]  Neil T. Heffernan,et al.  Learning Bayesian Knowledge Tracing Parameters with a Knowledge Heuristic and Empirical Probabilities , 2014, Intelligent Tutoring Systems.

[19]  Colette Rolland,et al.  A Multi-Model View of Process Modelling , 1999, Requirements Engineering.

[20]  Analía Amandi,et al.  Evaluating Bayesian networks' precision for detecting students' learning styles , 2007, Comput. Educ..

[21]  Ivon Arroyo,et al.  Tracing Knowledge and Engagement in Parallel in an Intelligent Tutoring System , 2014, EDM.

[22]  Beverly Park Woolf,et al.  Inferring learning and attitudes from a Bayesian Network of log file data , 2005, AIED.

[23]  Antonija Mitrovic,et al.  Optimising ITS Behaviour with Bayesian Networks and Decision Theory , 2001 .

[24]  Chen Lin,et al.  Intervention-BKT: Incorporating Instructional Interventions into Bayesian Knowledge Tracing , 2016, ITS.

[25]  Zachary A. Pardos,et al.  Modeling Individualization in a Bayesian Networks Implementation of Knowledge Tracing , 2010, UMAP.

[26]  Alexander G. Schwing,et al.  Dynamic Bayesian Networks for Student Modeling , 2017, IEEE Transactions on Learning Technologies.

[27]  Carlos Delgado Kloos,et al.  An adaptive and innovative question-driven competition-based intelligent tutoring system for learning , 2012, Expert Syst. Appl..

[28]  Kinshuk,et al.  Towards a framework definition for learning process engineering supported by an adaptive learning system , 2012, 2012 IEEE International Conference on Technology Enhanced Education (ICTEE).

[29]  Joachim M. Buhmann,et al.  Towards a Framework for Modelling Engagement Dynamics in Multiple Learning Domains , 2013, Int. J. Artif. Intell. Educ..

[30]  Jun Xu,et al.  A problem solving oriented intelligent tutoring system to improve students' acquisition of basic computer skills , 2015, Comput. Educ..

[31]  Zachary A. Pardos,et al.  How Should Intelligent Tutoring Systems Sequence Multiple Graphical Representations of Fractions? A Multi-Methods Study , 2013, International Journal of Artificial Intelligence in Education.

[32]  Vojkan Mihajlovic,et al.  Dynamic Bayesian Networks: A State of the Art , 2001 .

[33]  Donald A. Norman,et al.  Accretion, tuning and restructuring: Three modes of learning , 1976 .

[34]  Luigi Portinale,et al.  A dynamic Bayesian network based framework to evaluate cascading effects in a power grid , 2012, Eng. Appl. Artif. Intell..

[35]  Radek Pelánek,et al.  Analysis of a Simple Model of Problem Solving Times , 2012, ITS.

[36]  Yong Se Kim,et al.  Learning Styles Diagnosis Based on User Interface Behaviors for the Customization of Learning Interfaces in an Intelligent Tutoring System , 2006, Intelligent Tutoring Systems.

[37]  Najoua Essoukri Ben Amara,et al.  Characterization of Dynamic Bayesian Network-The Dynamic Bayesian Network as temporal network , 2011 .

[38]  Changhe Yuan,et al.  Importance sampling algorithms for Bayesian networks: Principles and performance , 2006, Math. Comput. Model..

[39]  Brett van de Sande,et al.  Properties of the Bayesian Knowledge Tracing Model , 2013, EDM 2013.