CONCEPTIONS OF ASSESSMENT IN HIGHER EDUCATION
Bibliographic record
Abstract
Under Bologna processes, it is important that educators understand and use assessment for formative purposes as well as traditional summative evaluation purposes. This small-scale exploratory study tested the Teachers Conceptions of Assessment (TCoA) inventory in the Portuguese higher education context. A convenience sample of Portuguese academic faculty (n=185) from five public universities and across multiple scientific areas were surveyed. Confirmatory factor analysis rejected the original model and preferred a four-factor model (i.e., improvement, assessment quality, institutional quality, and, reject assessment use) using just 15 of 27 items. Findings from this study indicate that, in line with Bologna intentions, Portuguese faculty seem to be taking a positive and constructive view of assessment as a tool for improved outcomes and have confidence in their evaluative practices. Faculty agreed that assessment was a high-quality process for improved outcomes and rejected its irrelevance. At the same time, they had a much weaker but positive view that assessment evaluated institutional quality. The study indicates that the TCoA inventory needs to be supplemented with different items and factors to capture the quality of Portuguese faculty conceptions of assessment. Keywords: Higher Education; Faculty; Teachers Conceptions of assessment (TCoA)
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".