A new student model for an intelligent tutoring system using analytical hierarchy process

  • Authors

    • Dr D. Lakshmi
    • U Chandrasekhar
    https://doi.org/10.14419/ijet.v7i3.29.19285
  • Analytical Hierarchy Process (AHP), Intelligent Tutoring System, Thinking Style, Learning Style, Multiple Intelligence, Psychometric Assessment.

  • Understanding student’s thinking ability, strengths, weaknesses, learning behavior and their learning capacity are essential considerations in the virtual learning environment (VLE). The prime objective of this research study is to design a ‘Student Model’ based on individual’s ‘bio-psychological potential’. Defining a student model is crucial for an Intelligent Tutoring System (ITS) to adapt to the needs and knowledge of an individual student. Psychometric Assessments were used as diagnostic tools to understand student’s cognitive and personality traits. These assessments have to fulfill three major criteria, which are standardization, reliability and Validity. The first phase of this research study focuses on the primary data compilation using psychometric assessments, to categorize the cognitive traits and personality traits of the individual. A sample size of 1145 was gathered from 22 engineering colleges of South Indian states. Primary data are collected by administering suitable psychometric inventories such as Benziger Thinking Style Assessment (BTSA) for Brain Dominance Analysis, Kolb’s Learning Style Inventory (LSI) for the learning style identification, Howard Gardner’s MI inventory for multiple intelligence identification and Paul Costa R. Robert McCrae’s Big Five personality identification. This study consists of three major components namely, Personalized Profiling System (PPS), Mean-Difference clustering algorithm and the Analytical Hierarchy Process (AHP) algorithm. The study evaluates the performance of PPS through a feedback mechanism. Due to subjective nature of this process, the achieved accuracy is about 70%. The best decision is done based on the priorities provided by the AHP decision maker.

     

  • References

    1. [1] San Diego, J 2013, ‘Do Moodle analytics have a role to play in learning design, feedback and assessment?’ First Moodle Research Conference.

      [2] Butz, BP, Duarte M & Miller SM 2006 ‘An Intelligent Tutoring System for Circuit Analysis’, IEEE Transactions on Education, vol. 49, no. 2, pp. 216-223.

      [3] De Moura FF, Franco LM, De Melo SL & Fernandes MA, 2013, ‘Development of learning styles and multiple intelligences through particle swarm optimization’, IEEE InSystems, Man, and Cybernetics (SMC), pp. 835-840.

      [4] Benziger, K & Holmes, S 1995, ‘The Human brain, A reservoir of diverse flexible strength or chaotic raging violence’, Proceedings of the International Conference on Human Efficiency, Berlin.

      [5] Katherine Benziger, I, 1992, ‘the art of Using Your Whole Brain: Extroversion’, Introversion and Adaption, Keg and Brew, Australia, pp. 16-19.

      [6] Kolb, AY & Kolb, DA 2005, ‘The Kolb learning style inventory-version 3.1’, Technical Specifications, Experience Based Learning Systems. Boston, M.A, Hay Resource Direct.

      [7] Gardner, H 1987, ‘Developing the spectrum of human intelligences, Harvard Educational Review’, vol. 57, no. 2, pp. 187-193.

      [8] Costa Jr PT & McCrae, RR 1997, ‘Stability and change in personality assessment: The revised NEO personality inventory in the Year 2000’, Journal of Personality Assessment, vol. 68, no. 1, pp. 86-94.

      [9] Janet I Newcomb 2004, ‘the Benziger Thinking Styles Assessment: A Useful Tool for Coaches and Clients’, CA: Professional Coaches and Mentors Association, pp. 261-265.

      [10] Mai S Saleh, Zeinab M Monir & Eman Eltahlawy 2013, ‘Learning style preferences and academic achievement among researchers’, Asian Journal of Education and e-Learning’, vol. 1, no. 4.

      [11] Kolb, DA, Joyce S Osland & Inwin M Rubin 1995, ‘Organizational behaviour: an experiential approach to human behaviour in organizations’, 6th edition, Englewood Cliffs, NJ: Prentice Hall.

      [12] Kolb, DA 1984, ‘Experiential Learning experience as a source of learning and development’, New Jersey: Prentice Hall.

      [13] Renzulli, JS & Dai, DY 2001, ‘Abilities, interests, and styles as aptitudes for learning: A person-situation interaction perspective’ Perspectives on Thinking, Leilrning, and Cognitive Styles, pp. 23-45.

      [14] Ehrman, ME, 1996, ‘Understanding Second Language Learning Difficulties’, California, Sage Publications.

      [15] Chamorro-Premuzic, T & Furnham, A 2003a, ‘Personality traits and academic exam performance’, European Journal of Personality, vol. 17, no. 3, pp. 237-250.

      [16] Chamorro-Premuzic, T & Furnham, a 2003b. ‘Personality predicts academic performance: evidence from two longitudinal studies on British University samples’, Journal of Research in Personality, vol. 37, no. 4, pp. 319-338.

      [17] Ishizaka, A. & Labib, A., 2009, ‘Analytic hierarchy process and expert choice: Benefits and limitations. Or Insight’, vol.22, no.4, pp.201-220.

      [18] Çetin, A, Işık, AH & Güler, İ 2010, ‘Learning management system selection with analytic hierarchy process’, In Proc. 13th Int. Conf. on Interactive Computer Aided Learning (ICL2010), Hasselt, Belgium, pp. 921-926.

      [19] Mahnane L, Laskri MT & Trigano P, 2013, ‘A model of adaptive e-learning hypermedia system based on thinking and learning styles’, International Journal of Multimedia and Ubiquitous Engineering, vol. 18, no. 3, pp. 339-50.

      [20] Hank S & Chikh A, 2013, ‘Towards a collaborative learning scenario adapted to the multiple learner's intelligence’, IEEE InISKO-Maghreb, third International Symposium, pp. 1-7.

      [21] Calmbach L & Bodendorf F 2014, ‘The Impact of Personality Traits on the Individual Preference of E-Learning Tools’, IEEE InGlobal Conference (SRII), pp. 139-143.

      [22] Wood, KC, Smith, H & Grossniklaus, D 2013, ‘Piaget's Stages. Emerging Perspectives on Learning’, Teaching and Technology.

      [23] Kaur, P, Atan, H, Fong, SF, Idruz, RM & Dzakiria, H 2005, ‘the combination of different modalities in the Web-based learning environment: a comparative analysis of the perceptual instructional outcomes’. In Advanced Learning Technologies, ICALT 2005. Fifth IEEE International Conference on IEEE, pp. 764-768.

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    D. Lakshmi, D., & Chandrasekhar, U. (2018). A new student model for an intelligent tutoring system using analytical hierarchy process. International Journal of Engineering & Technology, 7(3.29), 433-442. https://doi.org/10.14419/ijet.v7i3.29.19285