Methodological Diversity in Language Assessment Research: The Role of Mixed Methods in Classroom-Based Language Assessment Studies
Bibliographic record
Abstract
Although an epistemic change often is labeled as a shift, some researchers representing the social sciences consider this paradigm shift as a paradigm expansion (e.g., Pollack, 2007) because epistemological and methodological diversity allows researchers to address a wider range of questions. The third paradigm, mixed methods (MM) research, is claimed to provide a more holistic picture of a research problem by combining two different data sources—quantitative and qualitative—in a single study (Creswell & Plano Clark, 2007; Greene, 2007; Teddlie & Tashakkori, 2009). This article discusses how mixed methods research approaches have been used to enrich the results and to enhance the rigor of classroom-based language assessment investigations, drawing on both the language testing and assessment (hereafter, language assessment) and classroom assessment literature in second language education. The article opens with a brief overview of the methodological evolution in language assessment research. Then, focusing on MM research studies that investigated various facets of classroom-based language assessment (CBLA) practices, the main part of this article outlines a proposal of MM methodology as an appropriate methodology for research on CBLA, particularly in a context where a new form of assessment is implemented. The article closes with a discussion of challenges that MM researchers might face, and a proposal of MM research methodology as an appropriate research approach for CBLA scholars, especially in settings where both the validation of assessment and the explanation of the phenomenon are required.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.652 | 0.667 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.013 | 0.041 |
| Scholarly communication | 0.036 | 0.031 |
| Open science | 0.010 | 0.029 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".