Matching Course Content with Learner Preferences in E-learning Systems Based on the Memetic Algorithm

Delaramifar, Mansoureh; Sargazi Moghadam, Tayebeh

doi:10.22059/jolr.2024.379570.666890

Document Type : Research Paper

Authors

¹ teaching English language department, Farhangian University, Tehran, Iran

² Department of Computer Science, University of Sistan and Baluchestan, Sistan and Baluchestan, Iran

https://doi.org/10.22059/jolr.2024.379570.666890

Abstract

Since, due to the advancement of technology, mankind solves many of its educational needs, such as learning foreign languages, through electronic education, upgrading electronic learning systems with educational infrastructure become very important. Therefore, this research aims to introduce an efficient method to provide the most appropriate educational content for the learner by discovering her interests and preferences and adapting it to educational and cultural issues by using artificial intelligence techniques. In this regard, The present study uses a decision-making framework using a Memetic optimization algorithm to extract the best match between available learning paths and activities. It also provides the best possible response, which is the system's best decision for each individual's learning, using a linear formula and determining personal factors such as the learner's knowledge level and preferences. The resulting educational framework was tested on 40 students, 12-15 year old. In this experiment, a control group of 20 and an experimental group of 20 were considered, and the framework was given to the experimental group and the control group learned using traditional methods such as books. SPSS software was used for data analysis. In addition, in the last class session, a survey was used to examine the experimental group on issues such as interest in e-learning, willingness to continue the class, need for more breaks, and willingness to continue learning English in this way. The findings showed that the average post-test score of the control group after training through textbook-based content was 14.8750 and the average post-test score of the experimental group that was trained with diverse content considering the learner's preferences was 16.7500. On the other hand, the significance level test indicates that the use of an e-learning program based on learner preferences had a significant (p < 0.05) effect on the experimental group, and as a result, it refers to the greater effect of e-learning with considering learner preferences compared to traditional learning.The final achievement was evaluated using multi-part software plugins from the point of view of flexibility, efficiency and interoperability through user satisfaction testing. Considering that more than seventy percent of users were satisfied with the learning efficiency and flexibility of the system, the results indicate that the system's output will have a more favorable effect on individual learning

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Language Research

Matching Course Content with Learner Preferences in E-learning Systems Based on the Memetic Algorithm

References

References

Volume 15, Issue 2 - Serial Number 26
January 2025
Pages 125-156

Matching Course Content with Learner Preferences in E-learning Systems Based on the Memetic Algorithm

References

References

Volume 15, Issue 2 - Serial Number 26January 2025Pages 125-156

Volume 15, Issue 2 - Serial Number 26
January 2025
Pages 125-156