Evolving interaction: a qualitative investigation of user mental models for smart thermostat users
Bibliographic record
Abstract
Smart thermostats differ significantly from traditional devices and are quickly becoming commonplace in homes. Literature demonstrates that thermostat interfaces greatly influence user interaction and related energy outcomes. Moreover, how users imagine their device to work appears to have a greater impact on usage than how the system functions. Previous work investigated manual and programmable thermostats in this context, employing user mental models (UMMs) to analyse user understanding. Since then, thermostats have developed significantly. This paper presents a novel investigation of smart thermostat UMMs. It employs contemporary methods to construct ten UMM diagrams, and three detailed case studies, contextualized with previous findings. All participants demonstrated feedback theory. Case studies highlight common misconceptions. Overall, smart thermostat UMMs appear to enable effective usage; however, some users are overwhelmed by the complexity, limiting engagement and use of features (e.g. programming).
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".