Test Validation and Complex, Dynamic Systems: The Case of the Pedagogical Content Knowledge for Supporting English Learners Test (PeCKSELT)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.470 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it