Paper #2

Smart Transformation of EFL Teaching and Learning Approaches

Md. Russell Talukder

Abstract: The calibration of the EFL teaching and learning approaches with Artificial Intelligence can potentially facilitate a smart transformation, fostering a personalized and engaging experience in teaching and learning among the stakeholders. The paper focuses on developing an EFL Big Data Ecosystem that is based on Big Data, Analytics, Machine Learning and cluster domain of EFL teaching and learning contents. The framework has been developed on the basis of the theory that machine learning algorithms, when exposed to structured or semi-structure data stored in the cluster domains of EFL Big Data ecosystem, can cull out the patterns, similarities, and differences existing in the contents of the domains. Later these machine learning algorithms can apply these already identified patterns to perform new tasks on open Big Data platform and identify similar contents to be stored in the respective cluster domain of EFL Bigdata Ecosystem without being supervised. Accordingly, the paper uses two membranes to construe its framework, namely (i) Open Big Data Membrane that stores random data collected from various source domains and (ii) Machine Learning Membrane that stores specially prepared structured and semi-structured data. Theoretically, the structured and semi structured data are to be prepared skill-wise, attribute-wise, method-wise, and preference-wise to accommodate the personalized preferences and diverse teaching and learning needs of different individuals. Within the machine learning membrane, the paper includes a number of stages such as knowledge building, development of cluster domain of the EFL contents, integration of skill-wise cluster domain with the CEFR attribute-wise teaching and learning approaches, machine learning of the personalized preferences, resonating, machine learning of the cluster domain for proximity development and sustainable operation. The ultimate goal is to optimize the learning experience by leveraging machine learning to create tailored content that aligns with the diverse teaching and learning needs of the EFL communities. Developing a prototype following the framework exerts the potentials to provide an ‘alternative to methods’, transforming the process of learning into a process of acquisition.

Keywords: Acquisition; Analytics; Big Data; Cluster Domain; EFL Big Data Ecosystem; EFL Teaching and Learning Approach; Framework; Machine Learning; Personalized Experience; Proximity Development; Smart Transformation.