Abstract
We introduce a novel three stepwise model of adaptive e-learning using multiple learner characteristics. We design a model of a learner attributes enlisting the study domain, summary details of the student and the requirements of the student. We include the theories of learning style to categorize and identify specific individuals so as to improve their experience on the online learning platform and apply it in the model. The affective state extraction model which extracts learner emotions from text inputs during the platform interactions. We finally pass the system extracted information the adaptivity domain which uses the off-policy Q-learning model free algorithm (Jang et al., 2019) to structure the learning path into tutorials, lectures and workshops depending on predefined constraints of learning. Simulated results show better adaptivity incases of multiple characteristics as opposed to single learner characteristics. Further research to include more than three characteristics as in this research.
Highlights
IntroductionIncrease in learner enrolment has forced higher education institutions to look for effective ways in which they can reach many learners
We include the theories of learning style to categorize and identify specific individuals so as to improve their experience on the online learning platform and apply it in the model
Increase in learner enrolment has forced higher education institutions to look for effective ways in which they can reach many learners
Summary
Increase in learner enrolment has forced higher education institutions to look for effective ways in which they can reach many learners. One of the strategies deployed by higher education institutions is the use of e-learning. There is a need to improve the quality of e-learning. Effort in research has gone into personalizing learning by making learning management systems adaptive (Sethi & S Lomte, 2017). In the effort to improve the quality of e-learning to cater for learners needs, researchers have made and developed adaptive learning systems. During that time researchers were looking at two major areas: hypertext and user modelling (Ennouamani & Mahani, 2017). One of the major areas of researches in adaptivity in e-learning at current is in learner modelling (Premlatha & Geetha, 2015). One of the major areas of researches in adaptivity in e-learning at current is in learner modelling (Premlatha & Geetha, 2015). Chrysafiadi and Virvou (2013), and Raj and Renumol (2021) listed the different approaches for modelling
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