Human-Robot Scaffolding
An Architecture to Foster Problem-solving Skills
Datos Bibliográficos
| ID | 22190861 |
|---|---|
| Autores | John Páez (District University of Bogotá), Enrique González (0000-0001-8208-0953, Pontificia Universidad Javeriana) |
| Año | 2022 |
| Volumen | 11 |
| Número | 3 |
| Páginas | 1-17 |
| Fecha de publicación | 2022-09-30 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | ACM Transactions on Human-Robot Interaction (JOURNAL) |
| Identificadores de la revista | ISSN: 2573-9522 • E-ISSN: 2573-9522 |
| Editorial | Association for Computing Machinery (ACM) (PUBLISHER) |
| DOI | 10.1145/3526109 |
| OpenAlex | W4220949815 |
| Idioma | EN |
| Citas recibidas | 2 |
| Referencias citadas | 58 |
In order to give assertive support, robots need to understand the cognitive and emotional characteristics of learners while in the learning process. Also, if the task involves handling capacity, robots must have similar skills. Three concepts were explored to control the cognitive and emotional robot’s behavior: the psychological flow theory, the scaffolding pedagogic strategy, and the multiagents-software paradigm. Based on these concepts, the Human-Robot Scaffolding architecture was designed. It is divided into five blocks. First, the sensory block recognizes body gestures, speech, and task state. Second, the beliefs block estimates the skills and emotional state of learners. Third, the desires block validates the goals the robot can reach; the goals are grouped in skills development, emotional control, cognitive control, challenge control, life signals, and immediate support. Fourth, the intentions block, based on the goals competition strategy, selects the goal that the robot will perform. Finally, the action-planner block regulates the robot’s movements according to the robot’s emotions. The validation procedure was done with 53 learners ranging between 10 and 13 years old who study in public and private schools. Based on the research achievements, the robot fosters learning the Mean-Ends Analysis strategy and the solution of a problem. A video fragment that summarizes the research process is available in https://youtu.be/qbohCjBIwYc
Cognition · Cognitive architecture · Gesture · Human–computer interaction · Robot · AI in Service Interactions · Child and Animal Learning Development · Computer Science · Engineering · Psychology · Social Robot Interaction and HRI · Artificial Intelligence
Intention, Plans, and Practical Reason
Applying the Rasch Model
Who Sees Human?
Emotional Intelligence
Prediction of Human Behavior in Human--Robot Interaction Using Psychological Scales for Anxiety and Negative Attitudes Toward Robots
Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology
The Role of Tutoring in Problem Solving
Humanoid robots in higher education
Acceptance of Healthcare Robots for the Older Population
Using Embodied Multimodal Fusion to Perform Supportive and Instructive Robot Roles in Human-Robot Interaction
Assessing Acceptance of Assistive Social Agent Technology by Older Adults
Development and validation of brief measures of positive and negative affect
Construction of a New Scale
The Model of Interpersonal Teacher Behaviour
Social robot tutoring for child second language learning
Teaching and learning with children
The Benefits of Interactions with Physically Present Robots over Video-Displayed Agents
Interactive Robots as Social Partners and Peer Tutors for Children
The Social and Technological Dimensions of Scaffolding and Related Theoretical Concepts for Learning, Education, and Human Activity
| Obras citantes distintas | 2 |
|---|---|
| Citas por año | 1 |
| Intervalo de citas | 2024 - 2026 (3) |
| Velocidad de citación | current |
| Altamente citado | No |
| Tipos de cita | Neutras: 1 |