Dealing with the Human-centered Approach within HCI Projects
Bibliographic record
Abstract
Our interactions with objects or/and systems through digital screens are constantly increasing. Industry and information technology have more and more ambition toward offering new functions and interactions through these computerized systems. At the same time as the complexity of these systems is escalating, the complexity in designing them also grows. While user-centered approaches and usability in the area of human-computer interfaces (HCI) have been thoroughly researched for more than a decade now, we still encounter regularly unsatisfying interfaces. It is generally recognized that the design of HCI within multidisciplinary teams brings better answers to users. However as design practitioners, we see the inadequacy when it comes to working with other disciplines, at the conceptual level, and in creating shared understanding and new knowledge regarding user-centeredness. The paper explains what factors contribute to user-centered design and how we can see the inadequacy within multidisciplinary teams. Aiming to create the conditions for knowledge sharing and emergence of innovative and sustainable solutions, we propose a model called environment for reflective collaboration that encourages interdisciplinary attitude and allows for achieving joint reflective practice. Both seem necessary for dealing with the complexity of HCI. In this model, design is used as a method to understand people. Applying this design process in the early stages of a project provides the needed structure for collaboration. We explain the model as used in a real project, and we explain how a project-grounded approach helped the team bridge theory and practice.
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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 | 0.129 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.016 | 0.046 |
| Scholarly communication | 0.027 | 0.022 |
| Open science | 0.005 | 0.028 |
| Research integrity | 0.010 | 0.011 |
| 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".