BCSS 2014, Behavior Change Support Systems:Proceedings of the Second International Workshop on Behavior Change Support Systems co-located with the 9th International Conference on Persuasive Technology (PERSUASIVE 2014), Padua, Italy, May 22, 2014
Bibliographic record
Abstract
Behavior change support systems (BCSS) research is an evolving area.While the systems have been demonstrated to work to the effect, there is still a lot of work to be done to better understand the influence mechanisms of behavior change, and work out their influence on the systems architecture.The papers of the second BCSS workshop aim at filling this gap.They test existing influence strategies and suggest new ones, develop evaluation methods of influence strategies, and introduce systems architectures that support novel influence strategies.Second'International'Workshop'on'Behavior'Change'Support'Systems'(BCSS'2014)' 3' 2.1Evaluation of BCSS In their paper, de Jong and associates (2014) evaluate constructs developed for measuring perceived persuasiveness in technology.They find that, in general, the different measures line up with the data obtained with Perceived Persuasiveness Questionnaire (PPQ).However, the relationship between perceived persuasiveness (cf.Oinas-Kukkonen 2010b) and actual use rates of the persuasive technology, obtained by analyzing log-data, appears to be much more problematic.In sum, the authors conclude that their analysis demonstrate that the PSD model (Oinas-Kukkonen and Harjumaa 2009) generates consistent results, when measured using different methods.Caon and co-authors (2014) describe at conceptual level the Virtual Individual Model that will be integrated to the PEGASO system through an ontology-based virtualization.The aim of the project is to develop a system that is sensitive to characteristics of the individual and the interaction context and capable of using this information to dynamically select opportune tailored interventions.The PEGASO model is integrated to the system through an ontology-based virtualization.Rao (2014) reports about her work on developing evaluation tools to assist the design of persuasive game systems.The paper argues for applying persuasive design principles to games design when behavior change is the fundamental end of the game.The paper suggests that it is important to include gamification in a discussion about persuasion through games, because persuasive strategies play a central part in gamification design.Rao suggests that the Persuasive Systems Design (PSD) model (Oinas-Kukkonen and Harjumaa 2009) can be used in game design to identify specific characteristics of game systems that affect categories of persuasive structures such as credibility and personal involvement. 2.2Influence Strategies of BCSS Unal and colleagues (2014) examine users' compliance to persuasive messages in mobile application recommendation domain and explore how persuadability of users affects their compliance.The authors motivate their research by noting that the rapid growth in mobile application market means a significant challenge to find interesting and relevant applications for users.They find that subtle methods of persuasion are more effective than obvious persuasive messages at creating compliance.Also, persuadability is an important determinant on individual's compliance to recommendations.Orji (2014) explores gender effects on the strategies for persuasiveness of BCSSs.They identify that there is a need to adapt persuasive approaches to various user characteristics and go on to test if gender is among the characteristics that should be taken into account when designing individualized persuasive strategies.The author concludes that gender-dependent approaches would generally be more appropriate for designing BCSSs that will effectively promote health behavior changes than the one-size fits all approach.Gkika and Lekakos (2014) test whether certain persuasive strategies, especially in the form of recommendation explanations, can affect user's adoption of recommendations.The authors argue that explanation is an important aspect of 6' Second'International'Workshop'on'Behavior'Change'Support'Systems'(BCSS'2014)' 11.Oinas-Kukkonen Harri (2013) A foundation for the study of behavior change support systems.Personal and ubiquitous computing, Vol.17, No.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.101 | 0.057 |
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".