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KLSAS—An adaptive dynamic learning environment based on knowledge level and learning style
Author(s) -
Dhakshinamoorthy Anitha,
Dhakshinamoorthy Kavitha
Publication year - 2019
Publication title -
computer applications in engineering education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.478
H-Index - 29
eISSN - 1099-0542
pISSN - 1061-3773
DOI - 10.1002/cae.22076
Subject(s) - computer science , adaptation (eye) , adaptive learning , set (abstract data type) , active learning (machine learning) , adaptive system , learning environment , artificial intelligence , human–computer interaction , multimedia , mathematics education , physics , mathematics , optics , programming language
Adaptive learning environment, the extension of E‐learning, has gained its importance in the present education scenario with the growth in information and communication technology. To develop such an environment, suitable learner characteristics have to be selected and learning materials are to be adapted to the selected characteristics with intelligent mechanisms. The effectiveness of the adaptive systems improves with increased number of learner characteristics considered to build the systems. However, the observations from the existing adaptive systems reveal that the development of systems that adapts to more than one learner characteristic is more challenging than systems that adapt to single learner characteristic. This paper proposes an adaptive learning system, KLSAS: Knowledge and Learning Style based Adaptive System, which is based on two significant learner characteristics, knowledge level and learning style. The adaptation model of KLSAS is proposed as a dynamic system which updates itself with learner performance and preferences. The evaluation of the system is done through the test performance of two different set of learners. The results are encouraging and ensuring that the adaptive strategy based on the selected learner characteristics leads to improvement in learning.