Educational software usability: Artifact or Design?
Bibliographic record
Abstract
Online educational technologies and e-learning tools are providing new opportunities for students to learn worldwide, and they continue to play an important role in anatomical sciences education. Yet, as we shift to teaching online, particularly within the anatomical sciences, it has become apparent that e-learning tool success is based on more than just user satisfaction and preliminary learning outcomes-rather it is a multidimensional construct that should be addressed from an integrated perspective. The efficiency, effectiveness and satisfaction with which a user can navigate an e-learning tool is known as usability, and represents a construct which we propose can be used to quantitatively evaluate e-learning tool success. To assess the usability of an e-learning tool, usability testing should be employed during the design and development phases (i.e., prior to its release to users) as well as during its delivery (i.e., following its release to users). However, both the commercial educational software industry and individual academic developers in the anatomical sciences have overlooked the added value of additional usability testing. Reducing learner frustration and anxiety during e-learning tool use is essential in ensuring e-learning tool success, and will require a commitment on the part of the developers to engage in usability testing during all stages of an e-learning tool's life cycle. Anat Sci Educ 10: 190-199. © 2016 American Association of Anatomists.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".