Bridging Technology and Language: Exploring Soft Computing Solutions for Effective English Language Teaching in Iran
محورهای موضوعی : مهندسی نرم افزار (طراحی معماری، ارزیابی، آزمون و مدل سازی)
Ali Jahanbakhsh
1
,
Mehdi Jahangiri
2
1 - College of Skills and Entrepreneurship, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran
2 - Energy Research Center, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran
کلید واژه: Soft Computing, English Language Teaching, Optimization Algorithms, Learning-Oriented Assessment, Fuzzy logic, Artificial Neural Networks.,
چکیده مقاله :
The rapid evolution of technology and the increasing complexity of educational environments necessitate innovative approaches to language instruction. This paper explores the intersection of optimization techniques in soft computing and their application to English language teaching (ELT) in Iran. The necessity and importance of this review stem from the challenges faced by educators in adapting traditional teaching methodologies to meet the diverse needs of learners in a rapidly changing digital landscape. This study highlights their potential to enhance personalized learning experiences, improve curriculum design, and facilitate adaptive assessment strategies by synthesising existing literature on soft computing methods such as fuzzy logic, neural networks, and genetic algorithms. The work innovatively integrates these optimization techniques into ELT frameworks, proposing a model that leverages data-driven insights to tailor instructional strategies according to individual learner profiles. Key findings reveal significant improvements in student engagement, retention rates, and language proficiency when soft computing methods are employed. Moreover, the results indicate that such approaches can address the unique linguistic and cultural challenges faced by Iranian learners, fostering a more inclusive and effective educational environment. This paper contributes to the ongoing discourse on technology-enhanced language education by providing evidence of the benefits of optimization in soft computing. It underscores the imperative for educators and policymakers in Iran to embrace these methodologies to transform ELT, ultimately equipping learners with the skills necessary to thrive in an interconnected world.
The rapid evolution of technology and the increasing complexity of educational environments necessitate innovative approaches to language instruction. This paper explores the intersection of optimization techniques in soft computing and their application to English language teaching (ELT) in Iran. The necessity and importance of this review stem from the challenges faced by educators in adapting traditional teaching methodologies to meet the diverse needs of learners in a rapidly changing digital landscape. This study highlights their potential to enhance personalized learning experiences, improve curriculum design, and facilitate adaptive assessment strategies by synthesising existing literature on soft computing methods such as fuzzy logic, neural networks, and genetic algorithms. The work innovatively integrates these optimization techniques into ELT frameworks, proposing a model that leverages data-driven insights to tailor instructional strategies according to individual learner profiles. Key findings reveal significant improvements in student engagement, retention rates, and language proficiency when soft computing methods are employed. Moreover, the results indicate that such approaches can address the unique linguistic and cultural challenges faced by Iranian learners, fostering a more inclusive and effective educational environment. This paper contributes to the ongoing discourse on technology-enhanced language education by providing evidence of the benefits of optimization in soft computing. It underscores the imperative for educators and policymakers in Iran to embrace these methodologies to transform ELT, ultimately equipping learners with the skills necessary to thrive in an interconnected world.
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