Development of a Social Robot as a Mediator for Intergenerational Gameplay & Development of a Canvas for the Conceptualisation of HRI Game Design
Notice bibliographique
Résumé
Intergenerational interaction between grandparents and grandchildren benefits both \ngenerations. The use of a social robot in mediating this interaction is a relatively \nunexplored area of research. Often Human-Robot Interaction (HRI) research uses the robot \nas a point of focus; this thesis puts the focus on the interaction between the generations, \nusing a multi-stage study with a robot mediating the interaction in dyads of grandparents \nand grandchildren. \nThe research questions guiding this thesis are: 1) How might a robot-mediated game \nbe used to foster intergenerational gameplay? 2) What template can be created to conceptually describe HRI game systems? \nTo answer the first question, the study design includes three stages: 1. Human mediator Stage (exploratory); 2. The Wizard-of-Oz (WoZ) Stage (where a researcher remotely \ncontrols the robot); 3. Fully/semi-autonomous Stage. A Tangram puzzle game was used \nto create an enjoyable, collaborative experience. Stage 1 of the study was conducted with \nfour dyads of grandparents (52-74 years of age) and their grandchildren (7-9 years of age). \nThe purpose of Stage 1 was to determine the following: 1. How do dyads of grandparent-grandchild perceive their collaboration in the Tangram game? 2. What role do the dyads \nenvision for a social robot in the game? Results showed the dyads perceived high collaboration in the Tangram game, and saw the role of the robot as helping them by providing \nclues in the gameplay. The research team felt the game, in conjunction with the proposed \nsetup, worked well for supporting collaboration and decided to use the same game with a \nsimilar setup for the next two stages. Although the design and development of the next \nstage were ready, the COVID-19 pandemic led to the suspension of in-person research. \nThe second part of this thesis research focused on creating the Human-Robot Interaction Game Canvas (HRIGC), a novel way to conceptually model HRI game systems. A literature search of systematic ways to capture information, to assist in the design of the multi-stage study, yielded no appropriate tool, and prompted the creation of the HRIGC. \nThe goal of the HRIGC is to help researchers think about, identify, and explore various \naspects of designing an HRI game-based system. During the development process, the \nHRIGC was put through three case studies and two test runs: 1) Test run 1 with three \nresearchers in HRI game design; 2) Test run 2 with four Human-Computer Interaction \n(HCI) researchers of different backgrounds. The case studies and test runs showed HRIGC \nto be a promising tool in articulating the key aspects of HRI game design in an intuitive \nmanner. Formal validation of the canvas is necessary to confirm this tool.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 tête enseignante, 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 ».