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

  • Abstract
  • Keywords
  • References
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  • Abstract

    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.


  • Keywords

    Analytical Hierarchy Process (AHP); Intelligent Tutoring System; Thinking Style; Learning Style; Multiple Intelligence; Psychometric Assessment.

  • References

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Article ID: 19285
DOI: 10.14419/ijet.v7i3.29.19285

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