Considerations in Designing and Validating the Diagnostic Inventory for Self-Regulated Language Learning (DISLL): Status of the Process
Bibliographic record
Abstract
This article describes a new questionnaire, the “Diagnostic Inventory for Self-Regulated Language Learning” (DISLL), for assessing self-regulated learning strategies of students of English as an additional language. We discuss self-regulated learning models, evaluate existing questionnaires for assessing language learners’ self-regulated strategy use, and present a rationale for the DISLL. We explain Zimmerman’s (2000) three-phase model, adapted with simplified phase names: planning, doing, and reflecting. Every DISLL phase starts with a brief scenario to help learners judge how often they employ each strategy in that phase. Already completed are a review of the DISLL 1.0 by 18 international researchers, the step-by-step creation of the DISLL 1.1, and the profiling of vocabulary in the DISLL 1.1 to ensure the simplicity necessary for intermediate-level learners. We present an argument-based validation framework, to be supported by piloting the DISLL 1.2 in Iran, Saudi Arabia, Spain, and Poland and even broader piloting of the DISLL 1.3. Statistical analyses will involve quantitative methods, e.g., exploratory and confirmatory factor analysis, reliability analysis, and confirmatory composite analysis. At least one site will also use qualitative think-aloud protocols. The DISLL will be released into the public domain for free use after version 1.3.
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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 | 0.712 | 0.658 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".