Sociotechnical Human Factors Involved in Remote Online Usability Testing of Two eHealth Interventions
Bibliographic record
Abstract
BACKGROUND: Research in the fields of human performance technology and human computer interaction are challenging the traditional macro focus of usability testing arguing for methods that help test moderators assess "use in context" (ie, cognitive skills, usability understood over time) and in authentic "real world" settings. Human factors in these complex test scenarios may impact on the quality of usability results being derived yet there is a lack of research detailing moderator experiences in these test environments. Most comparative research has focused on the impact of the physical environment on results, and rarely on how the sociotechnical elements of the test environment affect moderator and test user performance. Improving our understanding of moderator roles and experiences with conducting "real world" usability testing can lead to improved techniques and strategies OBJECTIVE: To understand moderator experiences of using Web-conferencing software to conduct remote usability testing of 2 eHealth interventions. METHODS: An exploratory case study approach was used to study 4 moderators' experiences using Blackboard Collaborate for remote testing sessions of 2 different eHealth interventions. Data collection involved audio-recording iterative cycles of test sessions, collecting summary notes taken by moderators, and conducting 2 90-minute focus groups via teleconference. A direct content analysis with an inductive coding approach was used to explore personal accounts, assess the credibility of data interpretation, and generate consensus on the thematic structure of the results. RESULTS: Following the convergence of data from the various sources, 3 major themes were identified: (1) moderators experienced and adapted to unpredictable changes in cognitive load during testing; (2) moderators experienced challenges in creating and sustaining social presence and untangling dialogue; and (3) moderators experienced diverse technical demands, but were able to collaboratively troubleshoot with test users. CONCLUSIONS: Results highlight important human-computer interactions and human factor qualities that impact usability testing processes. Moderators need an advanced skill and knowledge set to address the social interaction aspects of Web-based usability testing and technical aspects of conferencing software during test sessions. Findings from moderator-focused studies can inform the design of remote testing platforms and real-time usability evaluation processes that place less cognitive burden on moderators and test users.
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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 | 0.043 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".