Heuristic and Formative Evaluation: A Case Study Illustration of a New Technique
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it