Implications of the multiple-use of large-scale assessments for the process of validation: A case study of the multiple-use of a Grade 9 mathematics assessment
Bibliographic record
Abstract
While most large-scale assessments are designed for one intended use, in many instances, a single administration of an assessment is used for one or more uses in addition to the intended use. I suggest the term multiple-use to refer to this practice. The purpose of this dissertation is to consider the implications of the multiple-use of large-scale assessments for the process of validation. I begin by discussing multiple-use from a theoretical perspective, identifying the challenges this practice creates for the process of validation. I maintain that multiple-use increases the stakes associated with an assessment and argue that where multiple-use is known to occur, the separate validation of each use may not be adequate since interactions between multiple-uses may take place. To build on this theoretical discussion, I report on an in-depth case study of one instance of multiple-use. Using questionnaire data, document analysis, school-level case studies and interviews with test development personnel, I describe the multiple-use of the Education Quality and Accountability Office (EQAO) Grade 9 Assessment of Mathematics which is administered in Ontario, Canada. I identify the pattern of uses for this assessment and focus on two multiple-uses: the use of the assessment for accountability and the use of the assessment by teachers as part of students' grades. Evidence of interactions between the two uses is provided and the limitations of applying Kane's (2006) argument-based model of validation given these interactions are discussed. I demonstrate how the concepts of boundary objects and boundary encounters, situated within sociocultural theory, contribute to the process of validation for this assessment and may be beneficial for the validation of other instances of multiple-use. A number of ways of rethinking the process of validation to better address the multiple-use of large-scale assessments are suggested and areas for further research are identified.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".