Performance Evaluation of SeisTutor Using Cognitive Intelligence‐Based “Kirkpatrick Model”
Author(s) -
Ninni Singh,
Vinit Kumar Gunjan,
Kadiyala Ramana,
Qin Xin,
Thippa Reddy Gadekallu
Publication year - 2022
Publication title -
computational intelligence and neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.605
H-Index - 52
eISSN - 1687-5273
pISSN - 1687-5265
DOI - 10.1155/2022/5092962
Subject(s) - computer science , tutor , cognition , curriculum , human intelligence , prime (order theory) , focus (optics) , artificial intelligence , human–computer interaction , psychology , pedagogy , neuroscience , physics , programming language , mathematics , optics , combinatorics
The classroom learning environment facilitates human tutors to interact with every learner and get the opportunity to understand the learner’s psychology and then provide learning material (access learner prior knowledge and well align the learning material as per learner requirement) to them accordingly. Implementing this cognitive intelligence in intelligent tutoring system is quite tricky. This research has focused on mimicking human tutor cognitive intelligence in the computer-aided system of offering an exclusive curriculum to the learners. The prime focus of this research article is to evaluate the proposed SeisTutor using Kirkpatrick’s four-phase evaluation model. Experimental results depicting the enhanced learning gain through intelligence incorporated SeisTutor as against the intelligence absence are demonstrated.
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