Personalizing the e-learning environment based on students' self-efficacy
Subject Areas :
1 -
2 - PHD in Educational Administration Smart University of Medical Sciences (Corresponding Author ) Tehran Iran
Keywords: Personalization, Learning Environment, Self-Efficacy, Students,
Abstract :
The aim of the present study is to personalize the e-learning environment based on the self-efficacy of master's students at Azad University, Electronic Branch. The research method is descriptive-survey and the statistical population includes male and female students of the electronic branch of Islamic Azad University, Tehran Electronic Branch in the academic year 1402-1403, of which 30 people were selected and studied by purposive sampling. To measure the performance of the designed system, academic performance and the amount of use of the system in two control and experimental groups were compared before and after personalization. Considering that the main method of measuring self-efficacy is to use a questionnaire, in this study an implicit method was used to identify the learner's academic self-efficacy. This research was carried out in the form of a fuzzy recommender system within the Smart Education Assistant system in one academic semester on a number of students of e-learning courses in two stages; Thus, in the first step and on half of the lessons, recommendations based on learning style were used, and in the next step and on the second half of the lessons, recommendations based on learning style and cognitive style were used together. The sample studied consisted of 30 people with an average age of 23.56 years. The evaluation of the personalized environment shows that considering the practical features in the learner model and providing lessons and recommendations appropriate to these features led to the academic progress of 75% of the learners and resulted in their academic satisfaction. In all stages of the lessons, the learners' capabilities were monitored and evaluated based on the fuzzy question-answer theory. Using this method made the educational recommendations more personalized and, as a result, people were more interested in continuing to work with the proposed system, reduced the amount of unnecessary referrals, and reduced learning time.
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