Heuristic and Formative Evaluation: A Case Study Illustration of a New Technique
Bibliographic record
Abstract
The purpose of the present work was to carry out an evaluation of an interactive, instructional Website that teaches the basic tenets of human performance technology. The evaluation methodology was based upon a unique combination of heuristic and formative evaluation techniques. It involved measuring attitudinal reactions to the Website, learning gains from performance scores on practice exercises, and content, navigation, and design areas needing modification or revision. Evaluation data were gathered from five students pursuing graduate degrees in education at an urban university. Paper-based attitude surveys, think-aloud protocols, and heuristic response forms were utilized to collect data. Student evaluators found the content in the Website to be useful and interesting; however, in some instances the practice items were confusing. The site was found to be easy to navigate and, overall, evaluators enjoyed using it. The evaluation methodology was shown to be effective in assessing design, content, and attitudinal issues, although in the future think-aloud protocols may be optional because they do not provide sufficient data to warrant the time spent on their use. Data also revealed that measuring learning gains was critical to the accurate evaluation and educational effectiveness of instructional Websites.
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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 | 0.135 | 0.181 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".