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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,101 | 0,057 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».