Using the Four Stages of Learning to Assess, Set Goals, and Instruct
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
Teaching requires attention to individual student needs by providing both adequate challenge and sufficient support to help students successfully gain academic skills (Shurr et al., 2019). The learning stages framework divides typical learning into four distinct stages: acquisition, fluency, maintenance, and generalization (Collins, 2012; Haring & Eaton, 1978). Thinking in terms of the learning progression can help teachers assess student performance and determine how they can best be supported to progress. This article will lead readers through the process of using the four stages of learning as a framework for assessment (i.e., understanding where students are currently performing), goal setting (i.e., setting the instructional aim), and instruction (i.e., planning for and delivering instruction aligned to individual student needs) within the context of mathematics for students with a variety of disabilities and support needs.
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.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it