This study conceptualizes oral input-seeking behavior as a construct in second language (L2) acquisition and develops and validates an instrument to measure it. Based on themes that emerged from semi-structured interviews, two questionnaire scales were developed to represent Proactive Input Exposure, which refers to learners’ proactive efforts to seek oral L2 input opportunities, and Proactive Input Monitoring, which involves analyzing available oral L2 input for language learning. Data were collected from 206 Spanish learners in Florida, 294 EFL learners in Iran, and 148 English learners in Japan. Factor-analytic results across the three datasets supported our hypothesized two-factor structure. Evidence for discriminant and convergent validity was established through confirmatory factor analysis, average variance extracted, and reliability analyses. Hierarchical multiple regression analyses showed that, after controlling for the length of L2 study, Proactive Input Exposure and Proactive Input Monitoring explained significant amounts of variance in learners’ self-assessed L2 proficiency in Iran and in objective listening comprehension in Japan. These findings provide preliminary evidence for the validity of oral input-seeking behavior as a domain-specific dimension of proactive language learning.
The use of generative artificial intelligence (AI) as a tool for qualitative analysis is becoming increasingly popular, and its application to (reflexive) thematic analysis is a topic of open controversy. Rather than asking whether large language models (LLMs) can reproduce language teacher identity (LTI), this article examines what LLMs actually do when applied to a sensitive, context-embedded construct. Using the Generative AI-Augmented Thematic Analysis (GAATA) framework, three LLMs (ChatGPT, Claude, and Gemini) performed thematic analyses of interviews with eight pre-service English teachers in a German Master of Education program. Instead of regarding the agreement between models as validation, the analysis compared their outputs to a situated human counter-reading of the same transcripts. The models converged on five dimensions of teacher identity, only one of which was specific to being a language teacher. The remaining four dimensions reiterated well-established and literature-congruent patterns. The counter-reading revealed that this convergent account retained the generic arc of each interview while eliminating what was most language-specific, contradictory, and locally situated. The main finding is that the models converge on what is typical and lose what is distinctive because they are trained on overlapping data. Therefore, agreement between them reflects shared assumptions, not analytic robustness. The study argues that the trustworthiness of human analysis supported by LLM-augmented interpretation of sensitive constructs should be the basis for judgment, not agreement between models. Furthermore, model output is most useful as a foil that makes situated interpretation visible.
Qualitative comparative analysis (QCA) offers a distinctive approach to explaining causal complexity in language education, yet its value depends on whether set-theoretic logic is sustained throughout the research process. This methodological review examines the application of QCA in language education research. Systematic searches of the Web of Science Core Collection and Scopus identified 20 eligible studies, which were evaluated using a ten-dimensional coding framework covering procedural transparency, functional positioning, configurational reasoning, and case orientation. The findings reveal substantial variation in the justification of condition selection and calibration, the reporting of analytical thresholds and robustness checks, and the distinction between necessity and sufficiency. QCA served as either the primary analytical method or a supplementary method, but its formal position did not by itself determine whether it made a distinctive inferential contribution. Although most studies reported configurational solutions, some continued to interpret conditions through variable-centred notions such as independent influence or relative importance. Moreover, the cases covered by the identified pathways were often insufficiently integrated into the substantive explanation. The review concludes that rigorous QCA research requires inferential coherence among configurational research questions, theoretically grounded set construction, transparent analytical decisions, set-theoretic interpretation, and sustained engagement with cases.
This article investigates how second language (L2) learners engage with tasks and whether such engagement facilitates L2 acquisition. Drawing on educational psychology and second language acquisition theory, engagement is conceptualised as a multidimensional construct encompassing behavioural, cognitive, social, and affective components. The review examines how task-based language teaching (TBLT) researchers have defined and measured engagement, critically evaluating methodological approaches ranging from discourse-analytic measures to physiological tools such as heart rate variability and EEG analysis. Ellis synthesizes representative studies investigating how task design features (e.g., learner-generated versus teacher-generated content; task complexity) and implementation variables (e.g., pairing arrangements; goal-tracking) influence learner engagement. A significant lacuna identified is that most research examines how tasks impact engagement without demonstrating whether engagement mediates learning. The article compares the use of the complexity-accuracy-lexis-fluency (CALF) framework and the engagement framework, noting that while engagement incorporates valuable educational and affective perspectives, it suffers from conceptual fuzziness and operationalization challenges. The article concludes by advocating for longitudinal, process-product research that links task engagement to implicit L2 learning outcomes, and for studies examining engagement in input-based and written tasks beyond oral interaction.
Given the context-sensitive nature of learner engagement, how empirical findings on engagement are interpreted in relation to context is an important methodological issue in second/foreign language (L2) research. Adopting a broad ecological view of context, this methodological synthesis reviewed how previous classroom-based L2 learner engagement research interpreted engagement in relation to contextual factors. Seventy-nine studies were systematically identified and reviewed. It was found that many studies discussed only a few contextual factors when interpreting results and paid more attention to some types of contextual factors (learner, task, and classroom factors) than to others (teacher factors and factors beyond the classroom). These results highlight the importance of situating engagement within its study contexts by discussing and examining its relationship to various multilayered contextual factors.
