From Assessment for Learning to Assessment for Expansion: Proposing a New Paradigm of Assessment as a Sociocultural Practice
Bibliographic record
Abstract
Although the importance of formative assessment has been recognized worldwide, the theoretical foundation is insufficiently captured within a broader sociocultural context that promotes teachers and students building an assessment culture. This study proposes a theoretical framework that supports the claim that formative assessment aims to accelerate an agentic process of transforming and improving the teaching–learning activity systems rather than helping teachers mold students with traditional values and cultural discourses. The characteristics of formative assessment were organized for each of the learning metaphors: acquisition, participation, and expansion. In this paper, assessment for expansion is defined as a form of formative assessment to facilitate expansive learning toward a process of making teaching–learning better, of which the functional core is sociocultural feedback with reference to situational criteria. Next, the theoretical discussions demonstrate that assessment for expansion emerges from making a third space and forming a culturally fitted tool for realistic and sustainable practical judgements. These conditions, which work within a continuum of problematic, ends-in-view, and expanded contexts, recognize the impact of assessments in associating a single student’s voice with a school- and community-wide problem. In conclusion, the possibilities and challenges of assessment for expansion are discussed from theoretical and practical perspectives.
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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 | 0.027 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.057 |
| Scholarly communication | 0.018 | 0.027 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.009 |
| 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".