mixed-methods
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- PaperOpenAlex — TESOL research21 Jul 2026
The Impact of AI Empowerment on the Development of College Students' English Speaking Skills: A Mixed-Methods Analysis Based on Speech Competence Assessment and Self-Directed Learning Efficiency
Jian Li, Luo ZhiYao
A mixed-methods study with 20 Chinese college students found that using the Doubao AI platform for autonomous speaking practice significantly improved fluency, pronunciation, and lexical resources. AI empowerment enhanced self-directed learning efficiency through synergy between self-determination theory and self-regulated learning, though a 'situational gap' between virtual practice and real interaction persisted.
Original abstract
In the context of the digital transformation of global education, artificial intelligence (AI) has become a transformative force in the field of second language acquisition. This study explores the impact of AI empowerment on the development of Chinese college students’ oral English competence and the efficiency of self-directed learning (SDL). The core of the research is to explore the synergy between the self-determination theory (SDT) and the self-regulated learning (SRL) model to clarify the interaction between technology empowerment and psychological mechanisms. Under this framework, SDL is the main learning mode, while SDT provides motivational basis (answering students “why” initiates SDL), and SRL provides strategic guarantee (answering students “how” manages learning process). This study adopts a mixed research method and conducts a six-week quasi-experiment with 20 International Business English majors students from Guangdong University of Foreign Studies. During the winter vacation, participants used the Doubao AI platform for autonomous speaking practice and received instant, data-driven feedback. The data were comprehensively analyzed through pre-test and post-test (measuring fluency, lexical resources, accuracy and pronunciation), validated questionnaires and qualitative interviews. The results show that AI empowerment significantly improves objective speaking ability, especially in terms of fluency, pronunciation and lexical resource. More importantly, the study found that there is a deep synergistic effect between the learner’s motivation process and the strategy process: the satisfaction of the two basic psychological needs of autonomy and ability (SDT) provides the internal motivation for students to enter the three cycle stages of SRL; the effective strategy execution guided by the role of AI “virtual supervisor” further strengthens the learners’ sense of achievement, thus jointly improving the overall SDL efficiency. Despite these advances, the study also found a “situational gap” between virtual practice and real social interaction. This study highlights the importance of an AI-supported teaching model that combines technical efficiency with human-led strategic guidance to optimize the self-directed development of oral English in the digital age.
- PaperTESOL Quarterly10 Jul 2026
The Grit–Goal Relationship in Language Learning as a Dynamic Process: A Goal Self‐Concordance Perspective
Hitoshi Mikami
A mixed-method study applies the goal self-concordance framework to examine how language learning goals aligned with one's sense of self are pursued, adjusted, or abandoned, and how L2 grit relates to these processes. Findings indicate that L2 grit levels do not always correspond to attainment of self-concordant goals, and that considering multiple goal layers is crucial for understanding the grit-goal relationship.
Original abstract
Second language (L2) grit, a noncognitive ability that underlies the pursuit of language goals, has attracted growing scholarly attention. However, the grit–goal relationship—including why certain goals are set and how grittiness relates to their pursuit—remains underexplored, as L2 grit has largely been examined in relation to researcher‐selected proxies of goals. In this context, this mixed method study applied the goal self‐concordance framework (Henry, 2021) to analyze how goals aligned with one's sense of self and future direction are pursued, adjusted, and sometimes abandoned and how L2 grit relates to these processes. Questionnaire and interview data were collected from fourth‐year English majors in Japan ( N = 8) to examine how L2 grit related to their goal development histories. The findings suggest that (a) L2 grit levels do not always correspond to the attainment of self‐concordant goals and (b) attention to multiple layers of goals may be important for understanding which aspects of L2 grit are associated with the pursuit of self‐concordant goals.
- PaperTESOL Quarterly9 Jul 2026
Integration in Qualitatively Oriented Mixed Methods Data Analysis: A Worked Example of a TESOL Graduate's Professional Experiences
Yi Sun, Xuesong (Andy) Gao
This paper demonstrates integration strategies in qualitatively oriented mixed methods data analysis using a worked example of a TESOL graduate's professional experiences. It combines quantitative indicators and qualitative structural analysis of visual network data with reflexive thematic analysis and qualitative comparative analysis of textual interview data to develop holistic inferences. The study makes explicit the complex knowledge construction process in TESOL through layered analytical integration.
Original abstract
This paper focuses on integration in the data analysis of a qualitatively oriented mixed methods study, illustrating this process through a worked example of interactions with social actors shaping a TESOL graduate's professional experiences. The paper outlines our analysis of visual and textual data from interviews that employed an embedded social network data elicitation tool. We developed and integrated layered descriptions and interpretations using the following strategies: (1) quantitative indicators and qualitative structural analysis to detail network features of visual data and facilitate further analysis and (2) reflexive thematic analysis procedures and qualitative comparative analysis strategies to explore patterns within the textual data. These analyses were brought together to develop a holistic understanding of network features and associated practices, as well as their influence on the TESOL graduate's professional experiences. Finally, this paper highlights how the application of multiple data analysis strategies can support the development and integration of layered inferences, thereby making explicit the complex process of knowledge construction in TESOL within a qualitatively oriented mixed methods approach.