Formative assessment: A systematic and artistic process of instruction for supporting school and lifelong learning
Bibliographic record
Abstract
Formative assessment is a potentially powerful instructional process because the practice of sharing assessment information that supports learning is embedded into the instructional process by design. If the potential of formative assessment is to be realized, it must transform from a collection of abstract theories and research methodologies and become a creative and systematic classroom practice. Policy-makers and school administrators must support this transition from theory into practice, particularly in the early stages of professional adaptation, and design assessment systems that teachers may internalize and enact efficiently. The article explores the hypothesis that many public school teachers are ‘trapped’ within environments which deter them from enacting open and inventive social learning strategies in their own classrooms, which when implemented have great potential to support autonomous learning, realize achievement, and create economically productive lifelong learners. This article therefore reviews the literature on formative assessment in practical settings, and investigates the extent to which teachers perform the basic functions of gathering and using evidence to further learning and development in pursuit of the lifelong learning competencies that are essential in the ‘new economy.’
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 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".