Assessing and enhancing learning outcomes in an architectural context: meaning equivalence methodology versus traditional formats of testing
Bibliographic record
Abstract
Current methods used for the assessment of learning are plagued with problems and often do not reveal the depth of comprehension of learned material (Kintsch, 1998). The development of the Meaning Equivalence (ME) methodology (Shafrir, 1999; Sigel, 1993, 1999), based on research highlighting the importance of multiple representations to learning, has been driven by the current lack of valid and reliable means for assessing deep conceptual understanding. This study described an evaluative implementation of the ME methodology, within the context of Architecture, as a tool for assessing and enhancing students' learning outcomes relative to traditional assessment formats such as multiple-choice and essay. Findings substantiate the efficacy of the ME measure for objectively assessing and enhancing deep comprehension and transfer of skills relative to traditional formats. Further, this study identified patterns, which suggest that the processing and interpretation of meaning may be dependent upon the modality used to represent conceptual content, for instance, text or image-based. The study included 160 university students in an undergraduate program in the department of Architecture at an Ontario university. Students were given traditional formatted and ME formatted tests after each of five historical periods covered during the course. Students' performance was compared on all methods of assessment to evaluate their relative potential for assessing a learner's deep comprehension as opposed to superficial 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 | 0.018 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".