PART ONE: CLEARING THE GROUND PART TWO: HISTORICAL PERSPECTIVES PART THREE: CONCEPTS OF LANGUAGE PART FOUR: CONCEPTS OF SOCIETY PART FIVE: CONCEPTS OF LANGUAGE LEARNING PART SIX: CONCEPTS OF LANGUAGE TEACHING

语言教学的基本概念 = Fundamental concepts of language teaching

Individual differences in second language acquisition

Online learning has become essential in education as the spread of coronavirus 19 (COVID-19) pandemic has brought significant changes to the field. However, education should remain welcoming and supportive to all the learners as it is important to keep the students’ behavioural intention in any form of learning environment. Thus, this study is significant as online learning is leading the trend of education now. Past studies revealed that the factors of performance expectancy, effort expectancy, social influence, and facilitating conditions under the unified theory of acceptance and use of technology (UTAUT) model affect learners’ behavioural intention to use online learning. This study intended to identify the most significant factors that influence TESL postgraduate students’ behavioural intention towards the use of online learning during the COVID-19 pandemic. It also examined the relationship amongst the four factors and postgraduate students’ behavioural intention to use online learning. The participants consisted of 169 postgraduate students at a public university in Malaysia. To achieve the aim, the study utilized a survey design using a questionnaire. The results from regression analysis revealed that all of the factors have positive effects on postgraduate students’ behavioural intention to use online learning. Meanwhile, performance expectancy has the greatest influence on postgraduate students’ behavioural intention. Hence, this study concludes that the practicality and the usefulness of online learning should be highlighted by the authorities.

Factors Affecting Teaching English as a Second Language (TESL) Postgraduate Students’ Behavioural Intention for Online Learning during the COVID-19 Pandemic

Education sector in Malaysia had put emphasis on the use of online learning or e-learning with technology and devices as a mediator of communication to replace face-to-face learning during the COVID-19 pandemic. Subsequently, with the improvement of learning technologies, English as a Second Language (ESL) teachers faced various challenges in language teaching. In this regard, this study aimed to investigate the challenges ESL teachers faced in implementing e-learning during COVID-19. Using a qualitative approach, this research was a case study which involved 20 primary ESL teachers. Data collection was done through an in-depth interview to explore teachersâ€™ e-learning experiences and challenges in teaching the English language. Based on the findings, the majority of teachers expressed that the use of e-learning was effective with various limitations such as teachersâ€™ readiness to adopt e-learning, accessibility to mobile phones and Internet connectivity, classroom management in term of low studentsâ€™ participation and assessment. Thus, the researcher recommended more studies to highlight teachersâ€™ insight regarding the significance of e-learning. ESL teachers, stakeholders, policy makers, and institutions can benefit from the results of the study and come out with practical strategies to utilize online sources for education in the pandemic situation. https://doi.org/10.26803/ijlter.20.2.18

http://ijlter.org/index.php/ijlter/article/download/3362/pdf

ESL teachers’ challenges in implementing e-learning during COVID-19

The impact of COVID-19 pandemic on students' learning in higher education in Afghanistan.

Covid-19 proved a pandemic that has affected the whole world on a large scale. Every walk of life got disturbed by this pandemic. Educational institutions not only in Pakistan but all over the globe remain close, which causes a loss of study for the students of all Grades, notably Higher education (Postgraduate Level), which directly affected education, learners,  and teachers in terms of learning, time, and economically. Virtual Teaching (VT) is proving an emerging method of teaching in the field of education all over the world. Developed countries have opted for this method of teaching much before. In Pakistan, universities under the directions of HEC started Virtual Teaching VT (Online Teaching) for the students, which was an attempt to cover the loss on an experimental basis. This study is conducted to know the impact of VT on ESL students' behavior. For this purpose among 100 students of KFUEIT, RYK University distributed a questionnaire to measure their behavior level. Students' participation was inspiriting, and their response found positive in this new field of Teaching.

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Impact of Virtual Teaching on ESL Learners' Attitudes under Covid-19 Circumstances at Post Graduate Level in Pakistan

The availability of educational data obtained by technology-assisted learning platforms can potentially be used to mine student behavior in order to address their problems and enhance the learning process. Educational data mining provides insights for professionals to make appropriate decisions. Learning platforms complement traditional learning environments and provide an opportunity to analyze students’ performance, thus mitigating the probability of student failures. Predicting students’ academic performance has become an important research area to take timely corrective actions, thereby increasing the efficacy of education systems. This study proposes an improved conditional generative adversarial network (CGAN) in combination with a deep-layer-based support vector machine (SVM) to predict students’ performance through school and home tutoring. Students’ educational datasets are predominantly small in size; to handle this problem, synthetic data samples are generated by an improved CGAN. To prove its effectiveness, results are compared with and without applying CGAN. Results indicate that school and home tutoring combined have a positive impact on students’ performance when the model is trained after applying CGAN. For an extensive evaluation of deep SVM, multiple kernel-based approaches are investigated, including radial, linear, sigmoid, and polynomial functions, and their performance is analyzed. The proposed improved CGAN coupled with deep SVM outperforms in terms of sensitivity, specificity, and area under the curve when compared with solutions from the existing literature.

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Robina Saleem

Papers

Impact of Virtual Teaching on ESL Learners' Attitudes under Covid-19 Circumstances at Post Graduate Level in Pakistan

Predicting Students’ Academic Performance with Conditional Generative Adversarial Network and Deep SVM