Adaptive User Generated Content Based on Integration of Learner Characteristics

Abstract

Intelligent tutoring systems are increasing in the degree of both intelligence and precision over time as the pressure for personalization grows and procedures become more standardized. It is gradually pushing any kind of learning contents into the Web as a result of widespread of e-learning. These kind of learning contents may be referred as open educational resources created by crowdsourcing methods. Present study is aimed to explore the precision of knowledge level classification methods and comparing the effect size of recommendation with or without learning preference inclusion. A user-generated and micro-lesson project was developed as an alternative for crowdsourcing. Students created their learning objects in the subject: Introduction to the algorithm. In order to classify user knowledge level, two classifier models are evaluated and integrated to an adaptive system. Finally, their accuracy levels have been compared. For evaluation of the effect size of the proposed adaptive system an experiment was conducted with three instructional groups. Findings suggest satisfactory achievements on control groups. Furthermore, the results lead to suggest positive achievements in the content recommendation based on knowledge level and content difficulty level. Properly addressing of learner preferences lead to enhance student’s behavior and performance on e-learning systems.

Keywords: online education, learning preference, adaptive learning, student model