Bibliographic record
Abstract
As we discussed in Chapter 1, teachers routinely deal with large-scale testing which is external to their classrooms, often with more at stake (or higher stakes). They routinely engage in small-scale testing, which is internal to their classrooms and measures achievement at the end of a unit or course with less at stake or (with lower stakes). Such testing, often referred to as assessment of learning, tends to be a special event, a signpost or marker in the flow of activity within a course. On the other hand, assessment for and as learning is part of ongoing classroom assessment practices. In Chapter 2, we examined how defining learning goals and outcomes, and designing our learning activities and assessment tasks in relation to those goals and outcomes, can both support our students’ learning and inform and focus our teaching. Before discussing the processes and procedures of classroom assessment planning and practices, we will highlight some of the key differences between large-scale testing and classroom assessment practices. We will then walk you through classroom test development in Chapter 4.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; both teacher heads agree on what is shown here.
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".