Factors Influencing Vietnamese Teenagers’ Intention to Use Mobile Devices for English Language Learning

Hoang, Quy; Pham, Thach; Dang, Quan; Nguyễn, Thắng

doi:10.2991/assehr.k.211224.023

Cited by 3 publications

(8 citation statements)

References 31 publications

(87 reference statements)

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“…In many studies of mobile-assisted language learning (MALL), EE correlated with BI [23,26,27]. However, some studies found contradictory results with EE neither influencing BI nor being a significant predictor of BI [28,29]. Therefore, it was meaningful to examine whether EE could determine BI in gamified English vocabulary apps.…”

Section: Effort Expectancymentioning

confidence: 99%

“…The definition of facilitating conditions was the degree to which a person felt that organizational and technological infrastructure existed to facilitate system utilization [25]. Some researchers found FC not correlated with BI in MALL [28,29] while others offered the opposite outcome [23]. In addition, FC also had an impact on AU [31,32].…”

Section: Facilitating Conditionsmentioning

confidence: 99%

“…Although not included in the UTAUT, the attitudes construct was found to be an essential and appropriate element in information technology and information system (IT/IS) [16] as well as in education [13]. It was proved that ATB affected BI [26,28], and some studies even revealed that ATB was the only factor influencing BI [31]. Besides, ATB was also related to PE [31], SI [26,31], EE [26], and FC [13].…”

Section: Attitudes Towards Behaviormentioning

confidence: 99%

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Extending the UTAUT Model of Gamified English Vocabulary Applications by Adding New Personality Constructs

Zhang

2022

Sustainability

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Learning vocabulary through mobile applications has gained momentum in recent years. However, little is known about what elements motivate or demotivate learners to use the applications. This research thus aims at finding out factors that may influence users’ intention to use certain gamified English vocabulary apps and their actual use of the applications based on the unified theory of acceptance and use of technology (UTAUT). This study complements the missing link through structural equation modeling based on the data collected from a large-scale online questionnaire survey. The results show that performance expectancy (PE), facilitating conditions (FC), and attitudes towards behavior (ATB) are positively correlated with behavioral intention (BI) while effort expectancy (EE), social influence (SI), and openness (OP) are negatively correlated with BI. However, no significant correlation was found between emotional stability (ES), positive competition (PC), and perseverance of effort (POE) and BI as predicted. In addition, behavioral intention (BI) and actual use (AU) are strongly correlated. However, unlike some of the previous studies, the result of this study does not present a significant relationship between FC and actual use (AU). Future research may include participants with diversified cultural backgrounds and extend the constructs further to psychology.

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Section: Effort Expectancymentioning

confidence: 99%

Section: Facilitating Conditionsmentioning

confidence: 99%

Section: Attitudes Towards Behaviormentioning

confidence: 99%

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Extending the UTAUT Model of Gamified English Vocabulary Applications by Adding New Personality Constructs

Zhang

2022

Sustainability

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“…The COVID-19 pandemic increased the use of mobile devices and consumption of mobile data, and it also illuminated a better future for the development of the language learning field (Jonnatan et al, 2022). According to Hoang et al (2021), language instructional gadgets are utilized increasingly in the field of language teaching. Recent research has shown that productive use of language learning apps for just 30 min a day significantly improved English language proficiency (Booton et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

An experimental study on the influence of instructional mobile applications in enhancing listening comprehension of rural students in India

Raj,

Tomy

2024

Front. Educ.

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IntroductionMobile learning (M-learning) is integral to language education, offering accessibility, portability, and diverse resources. It offers real-life language use with synchronous learning, flexibility, and an opportunity to engage with the global community. It allows learners to practice language skills from anywhere. This study aims to evaluate linguistic instructional mobile applications' effectiveness on listening comprehension among Indian rural students.MethodsPedagogic mobile applications were employed in an experimental study with 149 tertiary-level students from rural colleges in Tamil Nadu, India. Participants were divided into control (n = 75) and experimental (n = 74) groups. Pre and post-tests assessed listening comprehension levels, considering demographic variables. Different types of audio files were used in the intervention to improve the listening skills of the experimental group.ResultsStatistical analyses, including paired t-tests, independent t-tests, and ANOVA, revealed significant improvement in listening skills for the experimental group. Gender, locality, and parental income were considered as significant variables, showing that students from diverse backgrounds benefited from mobile applications.DiscussionThe results indicate that M-learning effectively aids rural students in overcoming language barriers, emphasizing the potential of mobile applications in language education.

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“…Among them, language learning is one of the important fields in mobile learning research [4]. The academic research on the application of mobile technology in language learning has undergone nearly 20 years of development, and has formed a rich research content and relatively independent research field, namely, Mobile Assisted Language Learning (MALL) [5]. MALL has not only received widespread attention and attention in academic circles, but also aroused great interest in the commercial market.…”

Section: Introductionmentioning

confidence: 99%

Mobile User English Learning Pattern Recognition Model based on Integrated Learning

Zhang,

Ren

2024

SCPE

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The research in the field of mobile assisted language learning has a long history. It basically follows the route from theory to application practice, but there are few process studies on learners’ individual language skills learning behavior based on mobile platform data. This research takes vocabulary learning as the starting point, and constructs a mobile user English learning pattern recognition model with improved Stacking integration algorithm. The purpose of this study is to identify different learning modes by analyzing the learning behavior and learning data of mobile users, and to provide personalized learning suggestions for users. The evaluation goal of this study is the accuracy and robustness of the mobile users’ English learning pattern recognition model, and the accuracy is the classification accuracy of the model for different learning patterns. Robustness is the stability and consistency of the model in different situations. In order to evaluate the integrated English learning mode of mobile users, the study first divides the collected learning data set into training set and test set. In this step, the method used in the study is cross-validation, which aims to reduce the difference of evaluation results caused by different data sets. For the relevant features in mobile users’ learning data, the features extracted by the research include learning behavior and learning progress, which can accurately reflect the learning mode. Then, the integrated learning method is used to train the model, and the best parameter combination is selected through the training set. Finally, the study uses the test set to evaluate the trained model, and calculates the accuracy and recall index of the model on the test set. Through this evaluation method, the evaluation results of the integrated learning pattern recognition model in terms of accuracy and robustness are obtained, and reference is provided for the improvement of the model. The model proposed in this study is suitable for a large number of user data. Because the learning behavior of users is influenced by personal habits, there are limitations in obtaining enough high-quality data, so the labeling of data is subjective. It is still a challenge to select the most representative features of user learning behavior extracted from the model. The feature selection method can lead to different results, and the process requires a lot of human intervention. The experiment conducted mining and analysis on user learning behavior data of a domestic English vocabulary learning APP. Compared with the confusion matrix of the traditional Stacking model, the improved Stacking model has a stronger ability to distinguish user learning patterns. According to the formula, the accuracy of the improved Stacking model is 91.29%; The accuracy of traditional Stacking model is 90.71%. The ROC curve of the improved Stacking model is smoother than the three single models. Its AUC value is 0.85, which is the same as that of XGBoost. The function is also improved compared with the traditional Stacking model, Logistic Regression (LR) and Random Forest (RF) model. Therefore, the Stacking integrated model owns the best forecast performance and can accurately predict the long-term learning mode of users. In this study, the English learning patterns of mobile users are identified by the method of integrated learning, so that the prediction results of multiple basic learners can be integrated, and the complementarity between different learners can be effectively dealt with, thus improving the generalization ability of the model. This model aims at identifying the English learning patterns of mobile users, and can accurately identify the learning patterns of users by analyzing their learning behaviors on mobile devices. This is of great significance for personalized English learning recommendation. The English learning pattern recognition model for mobile users proposed in this study can identify users’ learning patterns by analyzing their learning behaviors, thus providing personalized learning support and suggestions. In the process of digital manufacturing, they can learn from the idea of learning pattern recognition model and identify the patterns and laws in the production process by analyzing production data and process parameters, so as to optimize the production process and improve production efficiency.

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Factors Influencing Vietnamese Teenagers’ Intention to Use Mobile Devices for English Language Learning

Cited by 3 publications

References 31 publications

Extending the UTAUT Model of Gamified English Vocabulary Applications by Adding New Personality Constructs

Extending the UTAUT Model of Gamified English Vocabulary Applications by Adding New Personality Constructs

An experimental study on the influence of instructional mobile applications in enhancing listening comprehension of rural students in India

Mobile User English Learning Pattern Recognition Model based on Integrated Learning

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