Implementation and Evaluation of Intelligence Incorporated Tutoring System
Ninni Singh1, Neelu Jyothi Ahuja2
1Ninni Singh, her department is school of computer science University of Petroleum and Energy Studies, Bidholi , Dehradun,248007 India
2Neelu Jyothi Ahuja, her department is school of computer science University of Petroleum and Energy Studies, Bidholi , Dehradun,248007 India
Manuscript received on 01 August 2019 | Revised Manuscript received on 05 August 2019 | Manuscript published on 30 August 2019 | PP: 4548-4548 | Volume-8 Issue-10, August 2019 | Retrieval Number: J98490881019/19©BEIESP | DOI: 10.35940/ijitee.J9849.0881019
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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 elaborates the explication of domain knowledge into a tutorable form and illustrates the prediction of tutoring strategy prior to beginning of the tutoring session. The proposed intelligent tutoring system is christened as Seis Tutor. It impersonates human intelligence by adjudging best tutoring strategy for the learner. An empirical analysis has been performed on group of applicants having fundamental knowledge of the domain. Experimental analysis reveals that the learners who have undergone tutoring through ‘Seis Tutor’ embedded with intelligence present an overall 44.5 % learning gain as against that of 24.8 % shown by learners who underwent tutoring through the ‘Seis Tutor’, while it does not impersonate intelligence by choosing content and style for the learner.
Keywords: Course Dependency Graph, , Fuzzy Inference System, Intelligent tutoring system Knowledge management, Knowledge Repository
Scope of the Article: Fuzzy Logics