The proliferation of mobile technology has created more possibilities for language education than before, making it widely available, accessible and adaptable (Yang et al., 2019; Wang et al., 2021). Nevertheless, to date, research on mobile-assisted language learning is mostly about teacher-led language learning, instead of focusing on student-initiated use of mobile technology to learn outside class (An et al., 2020; Nami, 2020). In higher education, moreover, there is not much time for students to learn and master foreign languages as part of the language curriculum, and in some countries students are not given sufficient in-class time to practice their foreign languages for the purpose of ensuring their language acquisition (Nami, 2020). In order to address this issue, many researchers (e.g., Lai et al., 2022) suggest students adopt out-of-class and self-directed language learning facilitated by mobile technology to gain exposure to authentic foreign language, thus further enhancing their language competence. This out-of-class and self-directed language learning using mobile technology (SDLLMT) means that learners take responsibility and control of their own language learning process, including deciding what and how the knowledge is learned, with the assistance of mobile technology (Merriam & Bierema, 2013). More specifically, students utilize mobile apps such as Twitter, Google Translate, YouTube, Facebook to create their own language learning environment.

Research on SDLLMT have offered insights into students’ learning experience, and acceptance and adoption of using mobile technology in self-directed language learning process. However, more research is needed on learners’ persistence in this process. Persistence, also understood as continuance intention, is quite important to keep students engaged in the process of SDLLMT, as there is no teacher involved. Despite the fact that many students are adopting self-directed learning using mobile technology in their language learning, initial use of this kind of learning cannot guarantee students to successfully acquire new languages (Yang et al., 2019). Students need to persist in this learning process to master a language as a language is learned in years, not in a day or two (Fryer, 2019). However, many researchers mentioned that students may easily give up in the midst of language learning even if with teacher and institutional effort (Cheng & Lee, 2018), let alone in self-directed learning. Due to a lack of useful materials or a decrease of motivation, learners could probably give up the SDLLMT, without undertaking any responsibility or punishment. Therefore, how to retain these learners and facilitate their persistent usage is a vital issue that many educators and mobile learning providers need to explore and address (Yang et al., 2019).  

Regarding the factors influencing learners’ persistence, teacher support plays a critical role even if in self-directed learning because teachers were found to influence learners’ self-directed use of technology in language learning through metacognitive guidance and encouragement (Lai, 2015). Mobile learning readiness refers to learners’ mobile-related knowledge, skills, attitudes, and competencies for using mobile technology to meet self-directed learning goals. With mobile readiness, learners are able to actively and continually formulate and implement their learning plans (Lin & Hsieh, 2001). Additionally, the impact of engagement and satisfaction on persistence was widely validated in online learning. Based on the above-mentioned literature, the current study aims to investigate how learners’ persistence is predicted by mobile learning readiness, teacher support, engagement and satisfaction in the process of self-directed language learning using mobile technology. More specifically, we address the following questions:   

RQ 1: How are teacher support and learners’ mobile learning readiness related to their engagement in SDLLMT?

RQ 2: How do teacher support and learners’ mobile learning readiness impact learners’ satisfaction and persistence in SDLLMT?

RQ 3: Is there any mediation effect in SDLLMT?

1. Participants and procedure In this study, a total of 446 self-directed English language learners forming a volunteer multidisciplinary sample from Chinese universities were recruited. We selected the eligible students by the first item at the beginning of the questionnaire (“Have you ever learned the English language by yourself on your own choice?”). We chose convenience sampling method with an online questionnaire to obtain data used in this study. In order to recruit participants as many as possible, we distributed a hyperlink of the online questionnaire from Qualtrics via social media platforms. And, the purpose of the anonymous link is to encourage participants to express their preferences honestly and openly. Participants were informed the objectives of the questionnaire and how these data would be used and were asked to give their consent at the end of the questionnaire. 2. Instruments The questionnaire consisted of (1) social-demographic questions such as gender, educational level, discipline, and the English exams that they have passed, (2) mobile learning readiness, teacher support in self-directed language learning using mobile technology, learners’ engagement, persistence, and satisfaction in self-directed language learning using mobile technology that were measured on a five-point Likert scale where “1” represented “strongly disagree” and “5” indicated “totally agree”. Teacher support was measured by adapting the scales of Hoi and Mu (2021). Mobile learning readiness was evaluated by adapting the scale of Lin et al. (2016). Engagement was measured by an adaptation of the scale of Deng et al. (2020). Persistence and satisfaction were assessed by the scales from Lin and Wang (2012). In order to fit our research context, we added a contextual description “When self-studying English language…” to define the context-specific feature. 3. Data analysis Structural equation modeling (SEM) with Mplus 8.3 was employed to test these relationships in this study. A three-stage process was followed. The measurement model was first estimated using confirmatory factor analysis (CFA), to assess how well the observed items measure the latent constructs. Then, the structural model was performed to estimate the relationships among latent constructs. Finally, we performed a mediation analysis using a bias-corrected bootstrapping of 5000 samples.

This study examined the factors affecting learners’ persistence in self-directed language learning using mobile technology through analyzing how it is predicted by mobile learning readiness, teacher support, learners’ engagement and satisfaction in the process. In terms of RQ 1, teacher support was positively and significantly related to learners’ mobile learning readiness(β = .613, p < .001), but negatively to learners’ engagement (β = -.197, p < .01). Mobile learning readiness (β = .979, p < .001) had a direct impact on engagement in the process of self-directed language learning using mobile technology. Regarding RQ 2, mobile learning readiness (β = .929, p < .001) had a direct influence on satisfaction in self-directed language learning with mobile technology. Mobile learning readiness (β = .453, p < .01) and engagement (β = .403, p < .01) had direct influence on persistence in self-directed language learning with mobile technology. No significant relationship was found between satisfaction and persistence in self-directed language learning with mobile technology. Concerning RQ3, additionally, two mediation relations were found in this study. First, the effect of teacher support on engagement was significantly mediated by mobile learning readiness (β = .634, p < .001, 95%CI [-0.389; -0.085]), which indicated the indirect effect of mobile learning readiness on the relationship between teacher support and engagement was significant. Second, although teacher support could not significantly and directly explain satisfaction, it had a significant influence on satisfaction via mobile learning readiness (β = .618, p < .001, 95%CI [0.399; 0.939]), which revealed that the indirect effect of mobile learning readiness between teacher support and satisfaction was significant. The results showed that teachers’ roles and learners’ mobile learning readiness were essential to make self-directed learners more engaged, satisfied and persistent, further achieving effective and successful autonomous and lifelong learning.

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