Motivation, Learning Strategies, and Outcomes in Mobile English Language Learning

Yu, Zhonggen; Xu, Wei; Sukjairungwattana, Paisan

doi:10.1007/s40299-022-00675-0

Cited by 26 publications

(11 citation statements)

References 65 publications

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“…Researchers also investigated the effect of chatbots on learning motivation. Motivation, unlike engagement, refers to the possibility of engaging in learning tasks and maintaining learning behavior [39]. Essentially, engagement highlighted action, while motivation emphasized intent [40].…”

Section: Learners' Engagement Motivation and Interestmentioning

confidence: 99%

A Meta-Analysis and Systematic Review of the Effect of Chatbot Technology Use in Sustainable Education

Deng

2023

Sustainability

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The development of artificial intelligence in recent years has promoted the use of chatbot technology in sustainable education. Many studies examined the effect of chatbots on learning outcomes. However, scant studies summarized the effectiveness of chatbots in education. The aim of the study is to investigate the effect of chatbot-assisted learning on various components and how different moderator variables influenced the effectiveness. This study, through a meta-analysis, reviewed 32 empirical studies with 2201 participants published between 2010 and 2022. The results showed that chatbot technology exerted a medium-to-high effect on overall learning outcomes regardless of moderator variables, i.e., intervention duration, chatbot roles, and learning content. In terms of learning components, chatbots could significantly improve explicit reasoning, learning achievement, knowledge retention, and learning interest despite negative findings in critical thinking, learning engagement, and motivation. Future research could expand chatbot research by including different learning components.

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Section: Learners' Engagement Motivation and Interestmentioning

confidence: 99%

A Meta-Analysis and Systematic Review of the Effect of Chatbot Technology Use in Sustainable Education

Deng

2023

Sustainability

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“…In subsequent research endeavors, conducting more comprehensive investigations that concentrate on diverse approaches to teaching critical thinking in varying cultural and contextual settings would be advantageous, as the concept of critical thinking and its implications may differ across cultures. Moreover, the results of the study conducted by Yu (2023) revealed better results in gamified English vocabulary learning, and also the results of the study by Yu et al (2023) supported better language learning strategies in mobile English language learning classes; however, future studies can explore the effect of gamified critical thinking, and the use of mobile English language learning strategies to enhance critical thinking. In addition, more longitudinal research should be conducted by concentrating on whether these teaching approaches and strategies that are used to enhance critical thinking in language classroom, would have long term benefits for students.…”

Section: Discussionmentioning

confidence: 89%

A meta-synthesis study on the effect of critical thinking on improving language skills

Taherkhani,

Gholizadeh

2024

Forum Linguist. Stud.

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The current meta-synthesis study investigated the effective strategies for improving critical thinking ability among EFL/ESL learners, and the relationship between language learning skills and critical thinking ability. To achieve this aim, meta-synthesis was selected as the design of the study. Therefore, some databases were searched using the defined key terms, in order to select the related qualitative, quantitative, and mixed-method studies. As a result, 550 articles were found, 43 articles of which were included in the final review. Using thematic analysis, the obtained data from these 43 articles were analyzed in 6 steps and then coded for each research question. Although no single method was proposed in this study as the best to improve critical thinking, it was indicated that a number of them together can be effective when properly implemented. It was shown that all 4 language skills can be improved by enhancing the level of critical thinking ability among EFL/ESL learners.

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“…To understand why language conveys meaning, semantics, a discipline of linguistics, studies how language interacts with several "linguistic categories, syntax, phonology, and lexicon". Semantic analysis, in this sense, focuses on the meaning of words and phrases as components of the world [3].…”

Section: Introductionmentioning

confidence: 99%

Advancing Automated and Adaptive Educational Resources Through Semantic Analysis with BERT and GRU in English Language Learning

Jayakumar,

Rajakumari,

Sarwar

et al. 2024

IJACSA

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Semantics describe how language and its constituent parts are understood or interpreted. Semantic analysis is the computer analysis of language to derive connections, meaning, and context from words and sentences. In English language learning, dynamic content generation entails developing instructional materials that adjust to the specific requirements of each student, delivering individualized and contextually appropriate information to boost understanding and engagement. To tailor instructional materials to the various requirements of students, dynamic content creation is essential in English language learning (ELL). This work is a unique method for automatic and adaptive content production in ELL that uses Gated Recurrent Unit (GRU) and Bidirectional Encoder Representations from Transformers (BERT) together. The suggested approach uses BERT enabling content selection, adaption, and adaptive educational content production, and GRU for semantic extraction of features and contextual information captured from textual input. The article presents a novel approach to creating automated and adaptable educational tools for ELL that uses GRU for semantic feature extraction. Using persuasive essays collected in the PERSUADE 2.0 corpus annotated with discourse components and competency scores, this is an extensive dataset. After extensive testing, this approach shows outstanding outcomes, with high accuracy reaching 97% when compared to the current Spiking Neural Network (SNN) &Convolutional Neural Network (CNN), Logistic Regression (LR), and Convolutional Bidirectional Recurrent Neural Network (CBRNN). Python is used to implement the suggested work. The suggested strategy improves ELL engagement and understanding by providing individualized, contextually appropriate learning resources to each student. In addition, the flexibility of the system allows for real-time modifications to suit the changing needs and preferences of the learners. By providing instructors and students in a variety of educational contexts with a scalable and effective approach, this study advances automated content development in ELL. The model architecture will be improved in the future, along with the application's expansion into other domains outside of ELL and the investigation of new language aspects.

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Motivation, Learning Strategies, and Outcomes in Mobile English Language Learning

Cited by 26 publications

References 65 publications

A Meta-Analysis and Systematic Review of the Effect of Chatbot Technology Use in Sustainable Education

A Meta-Analysis and Systematic Review of the Effect of Chatbot Technology Use in Sustainable Education

A meta-synthesis study on the effect of critical thinking on improving language skills

Advancing Automated and Adaptive Educational Resources Through Semantic Analysis with BERT and GRU in English Language Learning

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