Student assessment in exemplary work‐based education programs
Bibliographic record
Abstract
Purpose The purpose of this paper is to describe the assessment practices reported by Canadian educators and workplace supervisors involved in exemplary work‐based education (WBE) programs for high‐school students. Design/methodology/approach Six focus groups were conducted, four with teachers and coordinators and two with workplace supervisors from exemplary WBE programs, to identify the features of these exemplary programs that prepare adolescents to participate in WBE, that prepare workplace supervisors to mentor WBE students, and that characterize the day‐to‐day interactions in the workplace through which adolescents learn. Surprisingly, in the absence of any questions directly focused on assessment, participants spoke at length and with passion about the purpose and nature of assessment in their outstanding WBE programs. Findings Analyses of these interviews revealed six themes that describe the range of assessment practices associated with these three features of exemplary programs: identification of student interests and abilities; student self‐assessment; communication of expectations and responsibilities; contextualized assessment; collaboration between school and workplace; and connections between assessment and instruction. Originality/value The findings highlight practical assessment procedures, for teachers and workplace supervisors, which facilitate the meaningful participation and learning of students in WBE programs and of workers in the workplace.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".