Personalizing e-Learning environment based on learner’s self-efficacy

Zarrin, F.; Montazer, Gh.

doi:10.22061/jte.2018.3137.1911

Document Type : Original Research Paper

Authors

Information Technology, Tarbiat Modares University, Tehran, Iran

https://doi.org/10.22061/jte.2018.3137.1911

Abstract

Background and Objective:One of the features used to personalize the e-learning environment is self-efficacy. This feature refers to people's belief in their ability to do tasks. Experts believe that academic self-efficacy is one of the important factors that has a positive effect on effective learning and academic achievement of learners. The concept Self-efficacy began with the work of Albert Bandura. According to Bandura, self-efficacy is a person's beliefs or judgments about his or her ability to perform tasks and responsibilities. This concept is not about having a skill or ability, but about believing in the ability to do work in different situations. The purpose of this article is to design an intelligent tutoring system. The learning model of the proposed system includes features of academic self-efficacy and learning style.
Methods:Academic self-efficacy has been automatically identified by designing of a fuzzy system based on learners' behavior and learning style through the questionnaire of Felder-Silverman which contains 44 question. After identification of these features, Proportional education strategies are presented and implemented in tutoring system in a real environment. The effectiveness of the proposed tutoring system is evaluated in terms of learners' operation by investigation of their satisfaction from system.
Findings: The results show that considering functional characteristics in learning model, presenting some learning objects and proportional recommendations to the characteristics, results in 75% learners' educational progress and their educational satisfaction. Moreover, evaluation of the time passed in the e-Learning environment before and after using Perles does not show a significant difference. Results show that the designed intelligent tutoring system based on the learner model and educational strategies, has led not only to the educational success of the learners but also to increase in their enthusiasm in using the system. Considering other effective and cognitive features in learning is highly recommended in order to provide a personalized environment.
Conclusion:The purpose of this study was to personalize the e-learning environment. For this purpose, the characteristics of academic self-efficacy and learning style, which are two effective characteristics in learning, were selected to model learners in a networked environment. Learning style was obtained through a questionnaire and an attempt was made to identify academic self-efficacy indirectly by using learner behaviors in the e-learning environment. To do this, the self-efficacy identification system was designed and evaluated using fuzzy set theory. Comparison of the output results of the identifier system with the academic self-efficacy questionnaire shows that the accuracy of the system is 88.2%. This indicates that the learner's behaviors in the e-learning environment can be an acceptable indicator of his/her academic self-efficacy and these behaviors can be considered as a basis for identifying academic self-efficacy. It is suggested that in future research, other effective features in education such as cognitive style, emotion, and personality be considered in order to provide a personalized environment for learners.

Keywords

20.1001.1.20080441.1398.13.4.9.6

Main Subjects

E-Lerning

COPYRIGHTS
©2019 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.

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