Test Validation and Complex, Dynamic Systems: The Case of the Pedagogical Content Knowledge for Supporting English Learners Test (PeCKSELT)
Bibliographic record
Abstract
Designing tests and rubrics, using test scores, and validating tests are all human-led activities that do not occur in isolation. Rather, these activities (and those performing them) constantly interact with internal and external elements, causing them to grow and change in sometimes nonlinear ways. These are the same characteristics of complex, dynamic systems as conceptualized in Complexity Theory. While Complexity Theory has been used in various disciplines, such as education, urban studies, and applied linguistics, it has yet to be fully integrated into the test validation literature. In this study, I address this gap, first by presenting a novel framework that infuses Interpretation Use Arguments (a traditional approach to validation) with aspects of Complexity Theory. I then apply this framework to uncover validity evidence for the Pedagogical Content Knowledge for Supporting English Learners Test (PeCKSELT), a measurement of Teacher Candidates’ understanding of how to support English Learners (ELs) in their K–12 classrooms. Within this complex validation system, I sought evidence to support two key claims (i.e. warrants): 1) PeCKSELT test performance elicits the relevant PCK required for teachers to successfully support their EL students in K–12 Ontario Classrooms; and 2) PeCKSELT scores reflect the target abilities and skills associated with PeCKSEL. Evidence to support these claims comes from the findings of two analyses I conducted. One of these was a thematic analysis of data that emerged from phenomenological interviews of PeCKSELT test developers. The other is from Latent Profile Analysis of the PeCKSELT scores of 307 Teacher Candidates who took the test in the Fall of 2018. Throughout this study, I also examine overarching theoretical concerns regarding the possibilities and benefits of applying Complexity Theory to test validation procedures. Moreover, as test takers, developers, test validation and the construct being measured are all complex, dynamic systems, I also explored the ways in which testing can still generate information that is stable enough to be useful.
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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 | 0.078 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".