How can students-as-partners work inform assessment?
Bibliographic record
Abstract
This fourth iteration of Voices from the Field highlights some of the many meanings and practices of assessment as faculty/academic staff, professional staff, and students define it and as they situate it in relation to students-as-partners work.The goal of this section of the journal is to offer a venue for a wide range of contributors to address important questions around and aspects of students-as-partners work without going through the intensive submission, peerreview, and revision processes.For this iteration of Voices, we invited responses to the question: "In what ways can students-as-partners work inform assessment?" Recognizing that assessment means different things in different contexts, we invited contributors to specify what definition they are working with.As we expected, people's definitions, arguments, and examples were highly diverse.Contributors' definitions reflect differences of geographical location, level (course, program, institutional), and focus or priority.Regarding the last of those, definitions include reference to teachers offering opportunity to students to demonstrate knowledge and/or skills; dynamic, mutual conversations between educational shareholders; an opportunity to honor knowledge, experience, and engagement; a process of engaging students' language, agency, selfauthorship, and self-directed learning goals in dialogue with course learning goals; instructors' and administrators' evaluations of teaching; the assessment of learning and development
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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 | 0.128 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.014 | 0.029 |
| Scholarly communication | 0.052 | 0.057 |
| Open science | 0.005 | 0.040 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".