Overview Mobile Learning Methodologies for English Courses
Learning to communicate successfully in English is critical for developing students' abilities and vision (Qureshi et al., 2020). Course designers currently encounter various challenges while creating English language instruction (Ramprasad et al., 2014). Learning via mobile devices, tablets and smart phones, mobile apps, social interactions, and online educational hubs is known as mobile learning (m-learning)(Kumar et al., 2021).Mobile learning is characterized by ubiquity, portability, slenderness, privacy, interactive, collaboration, and near-instantaneous access to information (Chung et al., 2019). Students can be in the right place at the right time, which means they are in a position to experience the genuine delight of learning (Gao et al., 2020).
College English teaching has received a second chance at life because of mobile teaching, a unique approach that combines education with information technology(Amudha et al., 2019). Exploring phone-based M-learning in today's blended learning environment positively impacts proficiency and growth in professional capabilities of pupils in a field (Zhonggen et al., 2019). Mobile learning is viewed as a model of how education will develop in the future(Saravanan et al., 2021). Mobile learning is not new, as learning may enable students to learn textbooks well in traditional printed textbooks (Yang et al., 2021). As a result, textbooks have long since evolved into supporting tools for mobile learning, and they have been around since the dawn of time (Vu et al., 2020).
Mobile learning utilizes all of the advantages of mobile communication to its fullest extent, such as the ability to transmit data and the portability of devices (Onwubere et al., 2019). Computers or networks receive learning resources and offer them to learners via terminal devices, increasing the amount of interaction between students and the information (Manogaran et al., 2019). Learning is an active process of acquiring new skills and knowledge while working in a cooperative environment (Gao et al., 2020).
Furthermore, it includes the opportunity for sudden, radical conceptual shifts and a continuous process of self-development and enrichment (Gomathi et al., 2021). Education is going through a period of transition, and how students are taught and learned must alter to keep up with the changing needs of society (Zhang et al., 2021). This necessitates active teaching methods and the integration of Information and Communication Technology (ICT) in the classroom (Manogaran et al., 2020). Specialization in a subject of knowledge is another factor to consider when incorporating mobile devices (Zhou et al., 2021). Over time, the user gains knowledge by revisiting previously acquired knowledge in new contexts, and more broadly, through concepts and tactics developed in previous years that serve as a foundation for continued learning throughout life (Yu et al., 2021).
Mobile device integration requires institutional support, and the institution (public or private) has played a role(Yıldız et al., 2020). In today's world, mobile technologies are indispensable because of their ubiquity, flexibility, ease of use, and wide range of capabilities (Nguyen et al., 2016).. Yet, they are underutilized in the educational process (Jin et al., 2017). Mobile technologies presented a challenge to educators and academics as they tried to figure out how to make them work for students. Mobile devices make it possible to learn at any time and from any location, even if one is not at school (Yassine et al., 2016). These options allow adult learners to maximize their productivity while minimizing unproductive time, improving their work-educational harmony. Online education has proven to be a valuable tool for skill development in the no schooling situations. Notably absent universal access to infrastructure and inadequate teacher and student preparation for the unique demands of online teaching and learning, there is still concern that online learning may have been a sub-optimal substitute for traditional classroom instruction. Students can overcome some of the difficulties of online learning by cultivating positive attitudes toward learning.
Students would benefit from frequent mobile technologies in English classes by improving their abilities, and mobile learning applications would benefit from this (Balaanand et al., 2019). The use of English in the classroom promotes the development of individualized learning, engages students' curiosity, and allows for the integration of class time with mobile learning.
Marzano released the new version of taxonomy called the New Taxonomy of Educational Objectives. A two-dimensional system is the New Taxonomy. Levels of mental processing are addressed in one dimension. Mental process information is divided into six degrees of processing knowledge with three different areas of expertise(Irvine et al., 2020). In terms of resource classification, Gagne's learning Category theory offers fresh insights based on the levels of complexity into eight classes and three levels(Rivest et al., 2021).
Modern advances in big data, computing power, the cloud, and algorithms have made AI more accessible and widespread than it was even a decade ago. With AI and Machine Learning, computers are now capable of reasoning, understanding, and interacting in new ways. Knowledge and understanding can be gained through the senses, experience, and thought in cognition. The cognitive learning theory combines cognition and learning to explain the various processes involved in learning effectively Using Cognitive web Services, developers can create AI-enhanced applications without the need for specialised knowledge in AI, machine learning, or data science. Data is not retained by Cognitive Services after processing, making it easier to meet the requirements of data privacy laws and regulations.
The main contributions of the paper are listed below.
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Designing an Intelligent Classification of English Educational Resource Information Based on Mobile Learning (CEERI-ML) to categorize the digital resources of the English language.
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The classification of the resources available digitally into various categories through CMKT and CGLCT.
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A Cognitive Web Service based Machine Learning Algorithm (CWS-ML) is introduced to classify the resources more precisely
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Analysis of the classification model under consideration.
The remaining parts of the paper are structured as follows: Section 2 elaborates existing models of classification of educational resources. In section 3, CEERI-ML has been designed and explained in detail. In section 4, the evaluation results of the proposed technique have been discussed. Finally, section 5 concludes the research article.