Bridging Technology and Language: Exploring Soft Computing Solutions for Effective English Language Teaching in Iran
Subject Areas : ُSoftware engineering (architectural design, evaluation, testing and Modelling)
Ali Jahanbakhsh
1
,
Mehdi Jahangiri
2
1 -
2 -
Keywords: Soft Computing, English Language Teaching, Optimization Algorithms, Learning-Oriented Assessment, Fuzzy logic, Artificial Neural Networks.,
Abstract :
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.
[1] Applications of Soft Computing, WisdomPlexus, 2019. https://wisdomplexus.com/blogs/applications-soft-computing [Accessed 05 August 2024]
[2] Soft computing, WIKIPEDIA, 2024. https://en.wikipedia.org/wiki/Soft_computing [Accessed 05 August 2024]
[3] Kumar, K., Roy, S. and Davim, J.P., 2019. Soft computing techniques for engineering optimization. 1st Edition, CRC press.
[4] Kumar, K., Roy, S. and Davim, J.P., 2019. Chapter: Introduction to Optimization and Relevance of Soft Computing towards Optimal Solution. 1st Edition, CRC press.
[5] Manhal, M., 2007. Fuzzy logic and its application in linguistics. Journal of the College of Languages (JCL) Mağallaẗ kulliyyaẗ al-luġāt, (17), pp. 1-32.
[6] Ashtarian, S., 2016. Integration of ICT in English Classrooms in Iran: Affordances and Problems. https://blog.nus.edu.sg/celcblog/2016/12/01/integration-of-ict-in-english-classrooms-in-iran-affordances-and-problems [Accessed 05 August 2024]
[7] Xu, X., Liu, F. and Liang, H., 2023. Role of situational pedagogics in English teaching in the context of soft computing and fuzzy sets. Journal of Computational Methods in Sciences and Engineering, 23(5), pp. 2389-2399.
[8] Derakhshan, A. and Ghiasvand, F., 2022. Demystifying Iranian EFL teachers’ perceptions and practices of learning-oriented assessment (LOA): Challenges and prospects in focus. Language Testing in Asia, 12(1), p. 55.
[9] Mostafavi, S., Mohseni, A. and Abbasian, G.R., 2021. The pedagogical efficacy of ESP courses for Iranian students of engineering from students’ and instructors’ perspectives. Asian-Pacific Journal of Second and Foreign Language Education, 6, pp. 1-20.
[10] Alikovich Eshbayev, O., Xamidovich Maxmudov, A. and Urokovich Rozikov, R., 2021, December. An overview of a state of the art on developing soft computing-based language education and research systems: a survey of engineering English students in Uzbekistan. In Proceedings of the 5th International Conference on Future Networks and Distributed Systems, pp. 447-452.
[11] Tian, M., 2024. Evaluation model of English Informatization Teaching Quality in Universities Based on Particle Swarm. Journal of Electrical Systems, 20(1), p. 139.
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Journal of Optimization in Soft Computing (JOSC) Vol. 2, Issue 3, pp: (1-6), Autumn-2024 Journal homepage: https://sanad.iau.ir/journal/josc |
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Paper Type (Research paper)
Bridging Technology and Language: Exploring Soft Computing Solutions for Effective English Language Teaching in Iran
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.
Article Info |
| Abstract |
Article History: Received: 2024/08/08 Revised: 2024/08/20 Accepted: 2024/10/05
DOI: |
| 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. |
Keywords: Soft Computing; English Language Teaching; Optimization Algorithms; Learning-Oriented Assessment; Fuzzy logic; Artificial Neural Networks.
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*Corresponding Author’s Email Address: Mehdi_Jahangeri@yahoo.com |
1. Introduction
Optimization in Soft Computing and its relation with ELT in Iran examines the interdisciplinary application of advanced computational techniques to enhance educational outcomes in English language instruction [1]. Soft computing, an umbrella term encompassing methodologies like Fuzzy Logic (FL), Evolutionary Computation (EC), Machine Learning (ML), and Probabilistic Reasoning (PR), offers flexible and approximate solutions to complex, high-level problems in contrast to rigid traditional computing approaches [2]. This versatility makes soft computing particularly valuable in fields requiring nuanced decision-making and problem-solving, including education [3].
The integration of soft computing in ELT in Iran is notable for its potential to transform traditional pedagogical practices [4]. English proficiency plays a crucial role in the effective use of technology in education, as it helps teachers and students navigate and utilize digital tools [5]. By applying optimization algorithms, educators can create more personalized and efficient learning environments that cater to the specific needs of students. For instance, the use of Learning-Oriented Assessment (LOA) has shown to improve language acquisition by aligning continuous assessment with learning goals, thereby fostering a more engaging and contextualized learning experience [6].
Despite its benefits, the adoption of soft computing techniques in ELT faces challenges, such as time constraints, large class sizes, and a lack of proper training for teachers [6]. Additionally, the prevailing exam-oriented culture can conflict with the formative and continuous nature of LOA practices, posing further obstacles to its widespread implementation [6]. Addressing these issues requires a concerted effort to provide adequate training and resources for educators, along with a shift towards more holistic and integrative assessment methods.
The future prospects of applying optimization in soft computing to ELT in Iran are promising, with potential advancements in data mining algorithms and deep learning techniques like Non-Convex Particle Swarm Optimization (NC-PSOO) combined with Generative Adversarial Networks (GANs) offering new avenues for personalized instruction [7]. By leveraging these technologies, educators can optimize teaching strategies and enhance the overall quality of English education, ultimately fostering a more adaptive and effective learning environment.
The novelty of this work lies in its potential to enhance the quality of English language education in Iran and bridge the gap between technology and language. Additionally, by utilizing the proposed solutions, the learning experience for language learners can be made richer and more effective.
The flowchart of the current work is displayed in Figure 1. Outlining the various sections and subsections aids in a better understanding of the article and helps to solidify its structure in the reader's mind.
Figure 1. Flowchart of present study
2. Overview of Soft Computing
Soft computing is an umbrella term used to describe types of algorithms that produce approximate solutions to high-level, unsolvable problems in computer science. Unlike traditional hard-computing algorithms that heavily rely on concrete data and rigid mathematical models, soft computing leverages a more flexible approach to problem-solving, akin to the human brain's operation, which allows for approximate reasoning and partial truths [2].
2.1. Constituents of Soft Computing
Soft computing encompasses various techniques and methodologies, including FL, EC, ML, and PR. These components work together to handle the uncertainty, ambiguity, and approximation inherent in many real-world problems [2].
2.1.1. Fuzzy Logic
Fuzzy logic was introduced by Lotfi Zadeh in 1965 and provides a computational paradigm that handles uncertainties in data by using levels of truth rather than rigid binary values of 0 and 1 [8]. It has been utilized to create decision systems capable of predicting risks and managing uncertainties in various fields such as engineering and healthcare [2].
2.1.2. Artificial Neural Networks (ANNs)
ANNs are computational models that mimic the structure and functioning of the human brain. These networks consist of interconnected nodes, or perceptrons, that process information using complex mathematical operations.
Through training, ANNs can adjust parameters based on input data, making them highly flexible and capable of handling high-level problems like pattern recognition, predictive modeling, and data analysis [2]. ANNs have found applications in image recognition, natural language processing, and speech recognition, enhancing the versatility and efficiency of soft computing [8].
2.1.3. Evolutionary Computation
Evolutionary computation includes algorithms that mimic natural processes such as evolution and natural selection to find optimal solutions. Techniques like crossover, mutation, and selection drive genetic programming to diversify data and prevent premature conclusions, ensuring the survival of the fittest solutions within a set [8]. These methods are particularly useful in fields such as data mining, robotics, optimization, and engineering [8].
2.2. Applications of Soft Computing
Soft computing techniques are employed across a wide range of industries to solve complex problems efficiently and cost-effectively. For instance, in the medical field, soft computing assists in image analysis, aiding in the recognition and bifurcation of patterns from medical images and X-rays [2]. In the automobile industry, fuzzy logic is used to develop control systems for engine management, automatic transmissions, and antiskid steering [2].
Moreover, the construction industry utilizes Distributed Artificial Intelligence and fuzzy genetic agents to create intelligent buildings that can adapt to changing environmental conditions [2]. Soft computing also plays a crucial role in handwritten script recognition, allowing for the translation and sorting of multilingual documents [2].
3. Optimization in Soft Computing
Optimization in soft computing involves the application of algorithms and techniques to find the best solution to a given problem, within defined constraints and in the shortest possible time. This process is crucial in various fields such as engineering, management, and artificial intelligence, where complex decision-making and real-life problem-solving are required.
Soft computing provides low-cost, efficient solutions by leveraging algorithms, databases, Fuzzy Sets, and ANNs. These techniques modify uncertainties and indifferences in data, thereby enhancing the decision-making process and minimizing costs [2].
In contrast to traditional hard-computing methods that rely on precise and logical computations, soft computing aims to produce approximate solutions by emulating the human brain's ability to deal with ambiguities and uncertainties. This approach involves several components, including Fuzzy FL, EC, ML, and PR [8].
The book on optimization in engineering highlights the concept of optimization and its relevance to soft computing techniques. It presents various soft computing methods, sample problems, and related software programs, providing a step-by-step guide for obtaining optimal solutions to engineering and management problems [9]. The book emphasizes the broad application of these techniques in solving real-life complex problems using a heuristic approach, covering multidisciplinary areas such as physics, chemistry, biology, and material science [10].
The use of soft computing techniques is particularly beneficial in industries that require the management of vast resources and computations. For instance, soft computing methods are applied in the power system industry to predict voltage instability using ANNs, providing a low-cost solution to ensure system stability [2]. Moreover, these techniques aid in designing optimal maintenance strategies for electrical transmission networks, thereby improving current preventive maintenance programs [11].
4. Application to ELT
The integration of optimization in soft computing techniques within ELT in Iran has been increasingly recognized for its potential to enhance educational outcomes. The role of English competence in facilitating the use of computers in educational settings is paramount. For instance, interviewees have highlighted that proficiency in English aids significantly in navigating technological challenges, such as understanding instructions during software installation and usage, which are crucial for integrating technology into teaching practices [5]. This underscores the necessity for English language training for teachers of other subjects, positioning English teachers as pioneers in the technological integration process.
Moreover, the pedagogical efficacy of incorporating novel technologies and blended learning patterns into ELT has been supported by research. These methods create a more meaningful learning environment, catering to the evolving educational needs driven by technological advancements [12]. Emerging technologies are becoming a staple in students' daily lives, necessitating an adaptive educational framework that embraces these changes.
The application of soft computing in ELT is further illustrated through various studies on assessment and learning processes. For example, LOA has been shown to be effective in enhancing language acquisition. This approach, which focuses on continuous assessment aligned with learning goals, supports the development of language skills in a more contextualized and engaging manner [6].
Additionally, the evaluation of English for Specific Purposes (ESP) courses for Iranian engineering students has revealed positive outcomes in terms of fostering learner autonomy, providing authentic instructional content, and meeting students' real needs [13]. The adoption of soft computing techniques in these courses has demonstrated potential in creating customized and effective learning experiences that resonate with students' professional and academic requirements.
5. Case Studies in Iran
The study of technical English pedagogy specific to the students of engineering in Iran appears to be limited despite the significant amount of research conducted on the effectiveness of tertiary-level ESP courses within the Iranian academic context [13]. Several investigations have explored the effectiveness of ESP courses across various disciplines (e.g., Abbasian & Mahdavi, 2011; Iranmehr, Atai, & Babaii, 2018; Khoshsima & Khosravani, 2014; Mahdavi Zafarghandi et al., 2014; Malmir & Bagheri, 2019; Mashhadi Heidar & Abassy Delvand, 2015; Mazdayasna & Tahririan, 2008; Mostafaei Alaei & Ershadi, 2017; Sherkatolabbasi & Mahdavi Zafarghandi, 2012; Zand-Moghadam, Meihami, & Ghiasvand, 2018), but few have specifically focused on the needs of engineering students [13].
The limited studies addressing technical English for engineering students (e.g., Danaye-Tous & Haghighi, 2014; Hatam & Shafiei, 2012) often adopt a fragmental approach, evaluating only specific aspects of ESP courses, such as textbooks or language learning skills [13]. This piecemeal approach overlooks the comprehensive evaluation necessary to address the multifaceted needs of engineering students comprehensively.
A detailed examination of various case studies reveals several key findings. For instance, a statistical comparison was drawn between students' and instructors’ attitudes toward ESP courses. The data indicated a significant difference in perceptions, particularly in areas such as content authenticity and the satisfaction of target situation needs (F(1, 848) = 8.215, p < .0125, ·² = .010;F(1, 848) = 9.389, p < .0125, ·² = .011) [13]. The results showcased that students generally expressed dissatisfaction with the authenticity of the speaking, writing, vocabulary, and grammar materials/tasks used in ESP textbooks, with instructors' attitudes being even more negative [13].
Moreover, the analysis of needs satisfaction revealed that both students and instructors agreed on several aspects of the courses, such as the assessment system, task type, teacher expertise, scaffolding type, place of the course in the curriculum, and training hours [13]. However, there was a notable difference in perception regarding instructional materials, with a significant number of instructors disagreeing that courses utilized both prescribed textbooks and up-to-date online resources [13].
A closer look at the capability of the courses to foster learner autonomy showed that most participants believed the courses aimed to promote autonomous ESP learning by introducing extra materials along with the prescribed content [13]. Despite this, the majority of respondents did not confirm the use of other techniques to promote autonomy [13].
6. Benefits and Challenges
6.1. Benefits
The implementation of LOA in English as a Foreign Language (EFL) classes in Iran has shown several significant benefits. One of the most notable advantages is the enhancement of classroom interaction, collaboration, and engagement among students. According to findings derived from MAXQDA analysis, these factors are frequently cited by educators as key benefits of LOA [6]. Furthermore, LOA facilitates a more relaxed classroom atmosphere, effectively integrating assessment with teaching and learning processes, and enabling a more comprehensive monitoring of learner progress [6]. Teachers have also pointed out that LOA provides a process-oriented assessment which not only improves teaching and learning but also boosts students' motivation and fairness in evaluations [6].
For instance, Teacher 11 emphasized that LOA's integration of assessment, teaching, and learning significantly enhances classroom interaction and self-monitoring among students [6]. Teacher 36 noted that LOA's individualized approach helps in understanding students' needs, strengths, and weaknesses, thereby creating a productive learning environment [6]. Additionally, Teacher 40 observed that LOA reduces the stress associated with traditional testing and motivates students by allowing them to track their own progress [6]. Collectively, these insights underscore the multifaceted benefits of LOA in fostering a dynamic and supportive EFL classroom environment.
6.2. Challenges
Despite its benefits, the implementation of LOA in EFL classes in Iran is not without challenges. Key issues identified include time constraints, large class sizes, and a lack of proper training for teachers on LOA principles and practices [6]. The exam-oriented culture prevalent in many educational contexts further complicates the adoption of LOA, as it often conflicts with the formative and continuous nature of LOA practices [6].
Teacher 23 highlighted that time limits, class size, and insufficient training are significant barriers to effectively applying LOA [6]. Moreover, there is a recognized bias in some LOA practices, such as self and peer assessments, which can undermine the fairness and reliability of evaluations [6]. The findings align with Alsowat (2022), who also identified similar challenges in the context of Saudi Arabia, pointing out the inadequacies in infrastructure and resources required to support LOA [6]. These challenges reflect broader issues in EFL contexts, where traditional assessment approaches still dominate due to their perceived reliability and ease of implementation [6].
7. Future Prospects
The field of optimization in soft computing holds significant potential for transforming ELT in Iran through innovative assessment approaches and personalized instruction. Future studies are encouraged to explore various aspects to enhance the effectiveness and acceptance of LOA among Iranian EFL teachers. These aspects include investigating the role of teaching experience and educational background in teachers’ understanding of LOA, as well as the influence of cultural factors on their perceptions and practices. Cross-cultural studies may offer valuable insights into these dimensions [6].
Additionally, there is a need to examine specific assessment techniques, such as teacher questioning strategies, within the framework of LOA. Such techniques can provide deeper insights into how teachers can effectively integrate LOA into their instructional practices to foster better learning outcomes [6]. Another promising area for future research is the alignment between EFL students' perceptions of LOA and teachers' actual practices, which could lead to a more cohesive and supportive educational environment [6].
In the realm of data analysis and system design, the development of novel data mining algorithms based on fuzzy functional dependencies and linguistic interpretations is a promising avenue. These algorithms support domain experts, including linguists, in making informed decisions during data analysis by expressing useful knowledge from big data in linguistic terms [14]. The application of these algorithms in intelligent tutoring systems can significantly enhance the quality of English education by personalizing instruction based on individual learning patterns, strengths, and weaknesses [7].
Moreover, the integration of deep learning techniques, such as NC-PSOO with GANs, presents new opportunities for improving teaching quality. These advanced computational methods can optimize the teaching and learning process by analyzing various educational variables, ultimately leading to more effective and tailored educational strategies [7].
8. Conclusions
In conclusion, this paper highlights the potential of soft computing solutions in enhancing ELT in Iran by effectively bridging the gap between technology and pedagogy. By integrating adaptive learning systems, natural language processing, and intelligent tutoring systems, educators can create a more personalized and engaging learning environment that caters to diverse student needs. Regarding the novelties of this work, several scientific and practical advancements can be highlighted that may contribute to improving English language teaching in Iran. These include personalized learning systems, sentiment and interaction analysis, interactive simulations, data analysis for education, and the development of multimedia content. The findings suggest that leveraging these innovative technologies not only improves language acquisition but also fosters learner autonomy and motivation. Ultimately, this approach paves the way for a more effective and dynamic English language education system in Iran, preparing students for a globalized world.
Acknowledgement
The authors would like to thank all the organizations that provided data for this work.
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