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"Learning Motivation"
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The impact of Online and Offline Learning motivation on learning performance: the mediating role of positive academic emotion
Learning performance is an important indicator of online learning, is related to the quality of online education and the performance of students. Previous studies have found that learners’ learning performance is related to learning motivation and academic emotion, but the role of non-intellectual factors such as academic emotion has received less attention in empirical research. In order to make up for this gap, this study explored the mediating effect of positive academic emotions on college students’ online learning motivation and online learning performance, as well as the differences in mediating effects under different learning methods (i.e., online learning and offline learning). The data comes from 1088 college students who participate in online courses and offline courses in China. This study adopts correlation analysis and multiple mediation analysis to analyze the intermediary effect of online and offline students and multi-group comparisons. The results showed that the (1) online learning motivation is positively affecting online learning performance and positive academic emotions, and positive academic emotions are related to online learning performance. There is also a positive correlation between the dimensions of the three; (2) positive academic emotions part of the intermediary role between college students’ online learning motivation and online learning performance; (3) the intermediary effect of positive academic emotions in offline learning is significantly greater than that of online learning. This study is not only conducive to understanding academic emotions, learning motives and learning performance, but also provides important enlightenment online learning performance management in promoting open curriculum construction.
Journal Article
The Relationship between English Language Learner Characteristics and Online Self-regulation: A Structural Equation Modeling Approach
Learner beliefs, anxiety, and motivation are three common learner characteristics. They have consistently been found to account for language learning performance. Meanwhile, self-regulation is critical in sustaining online learners’ continuous efforts and predicting their learning outcomes. Despite the massive and rapidly increasing number of online English learners, few studies have clarified the assumed relationships between learner characteristics (learner beliefs, anxiety, motivation) and self-regulation in the online English learning context. This study aims to fill the gap by conducting structural equation modeling analysis to examine their relations. To fulfill the research purpose, we adopted the previous questionnaires with sufficient reliability as instruments to evaluate students’ online English learner beliefs, learning anxiety, learning motivation and online self-regulated English learning. The valid responses collected from 425 Chinese undergraduate university students enrolled in an online academic English writing course provided the data source. The results indicated that learner beliefs positively predicted, while learning anxiety negatively predicted, online self-regulated English learning. Online English learning motivation was a mediator in these associations. The findings suggested that stronger learner beliefs of self-efficacy and perceived value of English learning promoted learning motivation and self-regulation. In contrast, higher learning anxiety, such as test anxiety and fear of negative evaluation, harmed learners’ motivation and their online self-regulated English learning.
Journal Article
The relationship between inert thinking and ChatGPT dependence: An I-PACE model perspective
ChatGPT, as an example of generative artificial intelligence, possesses high-level conversational and problem-solving capabilities supported by powerful computational models and big data. However, the powerful performance of ChatGPT might enhance learner dependency. Although it has not yet been confirmed, many teachers and scholars are also concerned about this issue. Therefore, it is necessary to investigate this topic further. This study’s objective is to explore the association between inert thinking, positive experiences with ChatGPT, avoidance learning motivation, and ChatGPT dependence, based on the Interaction of Person-Affect-Cognition-Execution (I-PACE) model. Employing a cross-sectional design, we conducted an online survey with 870 Taiwanese university students, who had an average age of 22.81 years. The study found that inert thinking was positively associated with both positive experiences with ChatGPT and ChatGPT dependence. Furthermore, a significant association was found between inert thinking and avoidance learning motivation. Positive experience with ChatGPT was also positively related to avoidance learning motivation and ChatGPT dependence. Due to the scarcity of empirical research on generative artificial intelligence, the issues that people worry about when discussing AI were confirmed in this study. Moreover, avoidance learning motivation was positively correlated with ChatGPT dependence. Based on these findings, this study calls for educators to help students overcome inert thinking and avoidance learning motivation to prevent dependency on emerging technologies.
Journal Article
Structural Relationship Between L2 Learning Motivation and Resilience and Their Impact on Motivated Behavior and L2 Proficiency
This exploratory study investigates the structural relationship between second language (L2) learning motivation, resilience, motivated behavior, and L2 proficiency among English as a Foreign Language (EFL) students in South Korea. The research questions are as follows: (1) What are the constructs of resilience and L2 learning motivation among L2 learners? (2) What is the structural relationship between L2 learning motivation, resilience, motivated behavior, and L2 proficiency? A five-point Likert-type questionnaire was administered to 152 college-level EFL learners. The findings of factor analysis demonstrated that resilience factors were divided into self-composure, sociability, life satisfaction, communicative efficacy, and realistic optimism. Four factors emerged regarding L2 learning motivation: recognition from others, Ideal L2 self, instrumental motivation, and Ought-to L2 self. Confirmatory factor analysis revealed that these factors are independent constructs with conceptual validity. The final structural equation model showed that resilience influenced L2 proficiency through L2 learning motivation and motivated behavior.
Journal Article
An Investigation of Learners’ Perceived Progress During Online Education: Do Self-Efficacy Belief, Language Learning Motivation, and Metacognitive Strategies Matter?
Despite the large quantity of research projects about online learning, studies on students’ language learning motivation, self-efficacy belief, and metacognitive strategy use in the online learning setting are limited. The present paper aims to fill this gap through assessing learners’ metacognitive strategies, language learning motivation, self-efficacy belief, and their perceived progress in English learning. Responses to surveys were administered two times. The collected data were subject to longitudinal mediation analysis. The participants were a total of 627 university students in China. Results showed a positive and significant relationship among the four variables. The findings highlighted four significant longitudinal mediation patterns. Overall, self-efficacy belief predicted the use of metacognitive strategies, which in turn predicted their language learning motivation and perceived online English learning progress. The findings supported the mediating role of language learning motivation and metacognitive strategies. The findings showed the potential to enhance online English learning by facilitating learners’ self-efficacy belief, language learning motivation, and metacognitive strategies.
Journal Article