Developing the IRIS: Toward Situated and Valid Assessment Measures in Collaborative Professional Development and School Reform in Literacy
Bibliographic record
Abstract
This article illustrates the development and use of a situated assessment tool in the context of a collaborative (university–school district) literacy reform effort in British Columbia, Canada. The three-year project was focused on improving literacy, including reading comprehension strategy use, among students in grades 4 through 8. It began in the 2002–2003 school year, with approximately 100 teachers and 2,500 students participating. The authors describe development of the Informal Reading Inventory of Strategies (IRIS) to assess students' use of comprehension strategies, including making connections, engaging with the text, active meaning construction, monitoring understanding, analysis and synthesis, and critical reading. They then explain the implementation of the IRIS to support and enhance project goals, including informing teachers' instructional decision making. Based on what they call “on-the-ground, collaborative theorizing,” the authors argue that measures of projects such as the IRIS need to be valid not only in terms of content but also in terms of their consequences and uses in particular settings. Such approaches have the potential to respond to the growing demand for assessment approaches that are sensitive to contexts in which they are used and that support teachers and school administrators as they set their own goals for accountability and improvement in literacy.
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 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 | 0.071 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".