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What sustain Chinese adult second language (L2) learners’ engagement in online classes? A sequential mix-methods study on the roles of L2 motivation and enjoyment
What sustain Chinese adult second language (L2) learners’ engagement in online classes? A sequential mix-methods study on the roles of L2 motivation and enjoyment
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What sustain Chinese adult second language (L2) learners’ engagement in online classes? A sequential mix-methods study on the roles of L2 motivation and enjoyment
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What sustain Chinese adult second language (L2) learners’ engagement in online classes? A sequential mix-methods study on the roles of L2 motivation and enjoyment
What sustain Chinese adult second language (L2) learners’ engagement in online classes? A sequential mix-methods study on the roles of L2 motivation and enjoyment

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What sustain Chinese adult second language (L2) learners’ engagement in online classes? A sequential mix-methods study on the roles of L2 motivation and enjoyment
What sustain Chinese adult second language (L2) learners’ engagement in online classes? A sequential mix-methods study on the roles of L2 motivation and enjoyment
Journal Article

What sustain Chinese adult second language (L2) learners’ engagement in online classes? A sequential mix-methods study on the roles of L2 motivation and enjoyment

2025
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Overview
Recent research has integrated positive psychology with the Second Language Motivational Self System (L2MMS) to explore how enjoyment, L2 self-guides (including ideal L2 self and ought-to L2 self), and engagement interact among school-aged second-language (L2) learners. However, there is a significant gap in understanding these dynamics among adult learners, particularly those who primarily learn a second language online—a group that has been largely overlooked. To address this gap, our study examined the underlying mechanisms connecting these constructs. We employed a sequential mixed-methods approach with 367 adult L2 learners enrolled in online language courses at three universities in China. Quantitative data were analyzed using structural equation modeling (SEM) with Amos 24, revealing several key findings. Enjoyment was found to directly and positively predict engagement. However, contrary to existing literature, ideal L2 self did not directly predict either enjoyment or engagement. In contrast, ought-to L2 self directly and positively predicted both enjoyment and engagement, and it indirectly influenced engagement through enjoyment. Qualitative data, gathered through semi-structured interviews with five participants and analyzed using MAXQDA 2022, provided deeper insights into these statistical trends. This study concludes by discussing its implications and suggesting directions for future research.