Implementation and Evaluation of Personalized Intelligent Tutoring System
Ninni Singh1, Amit Kumar2, Neelu Jyothi Ahuja3
1Ninni Singh, Department of Computer Science and Engineering, University of Petroleum and Energy Studies, Dehradun, Uttrakhand, India.
2Amit Kumar, Department of Computer Science and Engineering, University of Petroleum and Energy Studies, Dehradun, Uttrakhand, India.
3Neelu Jyothi Ahuja, Department of Computer Science and Engineering, University of Petroleum and Energy Studies, Dehradun, Uttrakhand, India.
Manuscript received on 03 April 2019 | Revised Manuscript received on 10 April 2019 | Manuscript Published on 13 April 2019 | PP: 46-55 | Volume-8 Issue-6C April 2019 | Retrieval Number: F12180486C19/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This research article illustrates a generic architecture for intelligent tutoring system christened as Seis Tutor. Seis Tutor adapts itself according to the learner learning preferences by determining the learning style and pre knowledge level. The aim of Seis Tutor is to mimic similar the human intelligence by implicitly adjudge the tutoring strategy prior to tutoring session and custom-tailored the tutoring concepts to enhance the learning gain. Seis Tutor was implemented using I2A 2 index of learning style model. An Empirical analysis has been performed for graduation pursing students. The experimental analysis reveals that learning style model were accurately predicted with an accuracy of 61-100 %. The applicants found Seis Tutor is helpful with an average of 13 % learning gain, attains 24 % engagement at the beginning of the tutoring session.
Keywords: Learning Style, Pedagogy Flipping, Intelligent Tutoring System, E-Learning System, Domain Knowledge, Knowledge Management.
Scope of the Article: E-Learning