How hermeneutics can guide grading in integrated <scp>STEAM</scp> education: An evidence‐informed perspective
Bibliographic record
Abstract
Abstract Addressing calls to develop assessment theories for integrated teaching and learning, we propose an evidence‐informed perspective on grading in Science, Technology, Engineering, Arts and Mathematics (STEAM) education. We leveraged a qualitative collective case study design to generate rich profiles of three exemplary STEAM teachers' grading approaches and practices. Data sources included semi‐structured interviews and artefacts of teachers' instruction and assessment practice. We analysed qualitative data from interviews and artefacts using a general inductive approach. The teachers in our study pushed back against ‘objective’ views of grading, whereby grades are composites of summative assessments, in favour of informed and contextualised grading, which aims to document and support a negotiated understanding of each student's learning journey. Teachers' grading practices aligned with a hermeneutic approach to classroom assessment validity: the teachers (a) collected and interpreted a wide range of evidence of student (re)learning; (b) centred students' perspectives and evidences; and (c) employed their professional judgement to determine students' grades. Teachers characterised grading as a process of accounting for all available evidence, blurring the boundaries between formative and summative assessment. Documenting the learning process, rather than focusing on products, can support deeply integrated learning. Importantly, the teachers supported students in documenting their own learning and negotiating their grades with reference to self‐generated evidence. This practice stands to reduce power imbalances between students and teachers and foster students' self‐regulated learning. Our findings inform a framework which STEAM educators can use to guide grading in integrated classrooms, an enduring challenge for integrated learning.
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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 | 0.243 | 0.265 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.005 | 0.057 |
| Scholarly communication | 0.022 | 0.022 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".