Educational Robotics Research Articles

The Application of CoppeliaSim EDU in Robot Course Teaching

CoppeliaSim EDU, as a powerful tool for robot teaching, is changing the traditional teaching mode. This software provides a comprehensive robot simulation environment where users can explore different scenarios, models, and programming interfaces to gain a deeper understanding of robot technology. Its advantage lies in reducing the cost and potential security risks of physical robots, making robot education more widespread and secure. In actual teaching, teachers can use CoppeliaSim EDU for course design, guide students to carry out robot programming and simulation experiments, and improve teaching effectiveness. Through case analysis, it can be found that using CoppeliaSim EDU can better stimulate students' interest in learning, improve their practical ability and innovative thinking. In the future, with the continuous development of robotics technology, the application prospects of CoppeliaSim EDU will be even broader, bringing more possibilities for robot education.

A Laboratory-Based Multidisciplinary Approach for Effective Education and Training in Industrial Collaborative Robotics

The rapid evolution of robotics across various sectors, including healthcare, manufacturing, and domestic applications, has underscored a significant workforce skills gap. The shortage of qualified professionals in the labor market has had adverse effects on production capacities. Therefore, the significance of education and training for cultivating a skilled workforce cannot be overstated. This research work presents the development of a pedagogical approach centered on laboratory infrastructure designed specifically with multidisciplinary technologies and strategic human–machine interaction protocols to enhance learning in industrial robotics courses. Progressive competencies in laboratory protocols are developed, focusing on programming and simulating real-world industrial robotics tasks, to bridge the gap between theoretical education and practical industrial applications for higher education students. The proposed infrastructure includes a user-configurable maze comprising different colored elements, defining starting points, endpoints, obstacles, and varying track sections. These elements foster a dynamic and unpredictable learning environment. The infrastructure is fabricated using Computer Numerical Control (CNC) machining and 3D printing techniques. A collaborative robot, the Universal Robots UR3e, is used to navigate the maze and solve the track with advanced computer vision and human–machine communication. The amalgamation of practical experience and collaborative robotics furnishes students with hands-on experience, equipping them with the requisite skills for effective programming and manipulation of robotic devices. Empowering human–machine interaction and human–robot collaboration assists in addressing the industry’s demand for skilled labor in operating collaborative robotic manipulators.

Education robot object detection with a brain-inspired approach integrating Faster R-CNN, YOLOv3, and semi-supervised learning.

The development of education robots has brought tremendous potential and opportunities to the field of education. These intelligent machines can interact with students in classrooms and learning environments, providing personalized educational support. To enable education robots to fulfill their roles, they require accurate object detection capabilities to perceive and understand the surrounding environment of students, identify targets, and interact with them. Object detection in complex environments remains challenging, as classrooms or learning scenarios involve various objects, backgrounds, and lighting conditions. Improving the accuracy and efficiency of object detection is crucial for the development of education robots. This paper introduces the progress of an education robot's object detection based on a brain-inspired heuristic method, which integrates Faster R-CNN, YOLOv3, and semi-supervised learning. By combining the strengths of these three techniques, we can improve the accuracy and efficiency of object detection in education robot systems. In this work, we integrate two popular object detection algorithms: Faster R-CNN and YOLOv3. We conduct a series of experiments on the task of education robot object detection. The experimental results demonstrate that our proposed optimization algorithm significantly outperforms individual algorithms in terms of accuracy and real-time performance. Moreover, through semi-supervised learning, we achieve better performance with fewer labeled samples. This will provide education robots with more accurate perception capabilities, enabling better interaction with students and delivering personalized educational experiences. It will drive the development of the field of education robots, offering innovative and personalized solutions for education.

Assessing teacher readiness for educational robotics integration in primary and preschool education

ABSTRACT Educational Robotics combines elements of many sciences, covering all fields of STEM education. Its successful implementation depends largely on the teachers who will be asked to implement it in a school classroom. This study aimed to investigate the factors that influence the declaration of readiness of primary and preschool education teachers regarding the challenge of the use of educational robotics. The research involved 307 (n = 307) teachers, 191 (62.2%) of them were preschool teachers and 115 (37.8%) primary school teachers. The data was collected using a closed questionnaire called AKAER, with a Cronbach's alpha reliability coefficient of α = .924. While the benefits of using educational robotics are significant, its integration into the educational process requires more than a simple hardware setup. It primarily necessitates adequately trained educators. The general conclusion of the research is that teachers declare difficulty in programming issues, while their training can significantly improve the declaration of their degree of readiness.

MINDBOT: DESIGN AND IMPLEMENTATION OF A MIND-CONTROLLED EDUCATIONAL ROBOT TOY FOR DISABLED CHILDREN

The mindBot robot is a new educational robot toy that can be controlled by brain signals and voice commands. It was evaluated with children with disabilities as well as healthy children as the potential users. The most significant challenge was the size of the used Emotiv Insight electroencephalogram headset when adjusting it on the children’s’ heads. Despite all the challenges, the mindBot robot is a promising technology that could be fun and educational for disabled children. The 11 participants took 36 minutes to finish all tasks on average. This includes the time they spent setting up the robot for the first time, putting on the headset, learning how to use the robot, and using the main educational features. The System Usability Scale usability score for the robot is 71.13, which is considered to be the score of good. The future stages of improving the mindBot includes adding more mobility capabilities and adding the feature of educational assessment.

Robotics in the Context of Primary and Preschool Education: A Scoping Review

This paper presents an overview of Educational Robotics (ER) in primary and pre-school education. As ER seems to be gaining popularity for its effectiveness as a learning tool, more research needs to be done in this area. Recent results from ER pilot projects advocate for the integration of ER in K-12 education curricula. On the other hand, teachers may face various difficulties in carrying out such activities due to lack of experience or knowledge in this field. Previous research has shown that ER is still an open field for exploration. Even though an increasing number of experiences are available for the use of robotic tools in early education, there is not enough empirical evidence on the features they need to present for young learners to perceive them as attractive and easy to use. In addition, the high cost of some tools may prevent educational institutions from using them systematically. To detect possible gaps in the current research, in the context of this work, 21 articles that have been published between 2011-2021 and represent ER applications and frameworks were collected and reviewed. The results of this study demonstrate that ER can be a valuable tool for supporting primary and pre-school students. However, the review supports that more research is needed on the technical features that a robotic tool must have to be successfully introduced to students of this age. Moreover, future work is needed to develop low-cost educational robotic tools so they can become more accessible to educational institutions.

Exploration of the prospective utilization of educational robotics by preschool and primary education teachers

Educational robotics integrates aspects from various scientific disciplines, encompassing the entire spectrum of science, technology, engineering, and mathematics (STEM) education. Its effective application is heavily reliant on educators tasked with implementing it within a school setting. This study aimed to investigate the potential adoption of educational robotics among preschool and primary education teachers. The study involved 191 preschool teachers (62.2%) and 115 primary school teachers (37.8%). Data was gathered using a structured questionnaire, AKAER, demonstrating strong internal consistency with a Cronbach’s alpha reliability coefficient of α=.892. Educators, irrespective of their specialization, gender, or scientific background, acknowledge the significance of educational robotics and express eagerness to incorporate it. A substantial percentage of educators expressed discomfort in using educational robotics and related if they had trained or not. Nonetheless, more than 70.0% of the surveyed educators expressed interest in receiving training on educational robotics to proficiently integrate it into their teaching methodologies. To ensure that the new generation of students can reap the benefits of modern teaching tools like educational robotics, closely tied to STEM education and the cultivation of 21<sup>st</sup> century skills, we must not only supply schools with the required materials but prioritize the provision of adequately trained and informed educators.

Experimenting With Soft Robotics in Education: A Systematic Literature Review From 2006 to 2022

Experimenting With Soft Robotics in Education: A Systematic Literature Review From 2006 to 2022

Aspects of interdisciplinarity in dissertations and theses on Educational Robotics with students in public Basic Education schools

Resumo: Este artigo apresenta um estudo teórico-bibliográfico que teve como objetivo investigar aspectos da interdisciplinaridade presentes em dissertações e teses que se voltam para a Robótica Educacional (RE) no desenvolvimento de ações com alunos da Educação Básica regular pública. Discute a interdisciplinaridade em seus aspectos epistemológicos no âmbito da Educação Matemática e de suas interfaces com outras áreas de conhecimento. Anuncia o estudo realizado que, por meio de uma análise interpretativo-fenomenológica de excertos das pesquisas selecionadas, buscou compreender como a interdisciplinaridade nelas se apresenta. Nos resultados, destacam-se características e potencialidades interdisciplinares da RE; abordagens de ensino atreladas; e caminhos para o trabalho pedagógico. Evidenciam-se possibilidades da RE na promoção de práticas que visem transcender o conhecimento compartimentado, em especial no campo da Educação Matemática, destacando a abertura para a integração das disciplinas nos espaços públicos de ensino, em direção a uma aprendizagem sistêmica na qual os saberes dialoguem.

Educational Robotics: The Implementation of Karel in 3D

Educational Robotics: The Implementation of Karel in 3D

On special issue “New Trends in Robot Education”

On special issue “New Trends in Robot Education”

Evaluation of the effectiveness of robot-assisted teaching in the training room in preschool education

Abstract In recent years, the national policy has strongly promoted the integration of artificial intelligence technology and the field of education. In response to the national policy’s call, this paper proposes a robot-assisted teaching model. Taking the coordinate transformation model, velocity motion model, odometry motion model, and visual and auditory recognition model as design principles of the educational robot, the design of the educational robot is materialized by borrowing relevant hardware and programming software. The robot-assisted preschool education teaching model was created to explain the role of educational robots in preschool education. The research subjects were selected, and the teaching effect of robot-assisted preschool education was analyzed by combining theoretical knowledge of statistics. After the experimental intervention, in terms of learning quality, language expression ability, and cooperative literacy, there was a significant difference between the early childhood students in Group B and Group C, with P<0.05. In addition, the mean value of all the satisfaction indices remained above 3.3. Combining all the analyzed results confirms the facilitating role of robot-assisted teaching in preschool education.

Development of an Educational Robot for Exploring the Internet of Things

Development of an Educational Robot for Exploring the Internet of Things

Training efficacy of robotic duct-to-mucosa pancreaticojejunostomy simulation using silicone models for surgical fellows.

In the era of minimally invasive surgery (MIS), robotic pancreatoduodenectomy (PD) is actively performed, and clinical fellows need to thoroughly prepare for MIS-PD during the training process. Although pancreaticojejunostomy (PJ) is a difficult anastomosis that requires repeated practice, there are obstacles preventing its practice that concerns patient safety and limited time in the actual operating room. This study evaluated the efficacy of simulation-based training of robotic duct-to-mucosa PJ using pancreatic and intestinal silicone models using a scoring system. Three pancreatobiliary clinical fellows who had never performed a real robotic PJ participated in this study. Each trainee, who was well acquainted with master's video created by a senor surgeon, performed the robotic PJ procedures 9 times, and 3 independent pancreatobiliary surgeons assessed the videos and analyzed the scores using a blind method. The mean robotic PJ times for the 3 trainees were 42.8 and 29.1 minutes for the first and 9th videos, respectively. The mean score was 13.8 (range, 6-17) for the first video and 17.7 (range, 15-19) for the 9th video. When comparing earlier and later attempts, the PJ time decreased significantly (2,201.67 seconds vs. 2,045.50 seconds, P = 0.007), whereas test scores increased significantly (total score 14.22 vs. 16.89, P = 0.011). This robotic education system will help pancreatobiliary trainees overcome the learning curves efficiently and quickly without raising ethical concerns associated with animal models or direct practice with human subjects. This will be of practical assistance to trainees preparing for MIS-PD.

Development of an Anthropomorphic Dental Simulator Based on the Robo-C Robot

An analysis of robots (simulators) in education is provided. Promising directions for their development are highlighted, such as realism, interactivity, adaptation and personalization. The features of using simulators in dentistry are considered. The main disadvantages of existing simulators in dentistry have been identified, namely the lack of a communicative component and imitation of patient behavior. The anthropomorphic dental simulator is based on the Robo-C robot, which is a unique combination of advanced technologies and human facial expressions, which allows it to communicate with people, reproduce movements of different parts of the body and express emotions. As dental components, the following components were created and implemented into the Robo-C control system: a Smart jaw, including cameras and a temperature sensor, and a Smart tooth, including a pressure sensor. The Robo-C control system has been upgraded taking into account the Smart jaw and Smart tooth, which made it possible to connect the dental treatment process with the robot’s servos through its linguistic base. The process of analyzing data obtained from Smart jaw cameras using a neural network is described. A two-stage classification scheme for dental defects has been proposed and its effectiveness has been proven. The linguistic base contains a set of rules with the help of which devices (microphone, speakers, servos, Smart jaw, Smart tooth) interact with each other. An example of compiling a linguistic database rule is given. The linguistic base, Smart-jaw and Smart-tooth are configured for one of four cases: caries treatment, tooth preparation for a crown, tooth extraction, endodontic treatment. Treatment quality control is carried out using a comprehensive assessment of communication interaction with the robot and analysis of Smart-jaw and Smart-tooth data. An example of work in one of the cases is given. The anthropomorphic dental simulator presented in the article allows the use of new technologies in the training of dentists, as well as the simulation of various dental procedures, which will significantly improve the practical preparation of students for working with patients.

Assessment of Data Representation in Scratch Via the SOLO Taxonomy

The work addresses the significance of robotics in education, emphasizing its role in enhancing STEM skills through programming and sensory feedback. Scratch, a multimedia programming environment, is highlighted as a tool for robotic projects. Within Scratch, this work discusses data representation, distinguishing between visible and transparent data. The current work focuses on the visible data. Variables in Scratch are made tangible, helping users understand their function. Developers define their own data, such as values, variables, lists, and call parameters, while Scratch also provides system data. This system data can be numeric, alphanumeric, or logical, and its representation in code varies. To evaluate how data are used in Scratch by novice programmers, this work also proposes an evaluation framework using the Structure of the Observed Learning Outcome (SOLO) taxonomy. This evaluation framework can be used by the teacher as a tool either to evaluate with measurable criteria the students’ code (on issues related to the way the data indicates their presence in Scratch) or to develop their personal teaching paths, thus creating mental scaffolds that assist students to master new knowledge.

Teacher Digital Competence Analysis in Block Programming Applied to Educational Robotics

This research aims to analyze digital teaching competence in the field of block programming applied to educational robotics among active primary education teachers. The research methodology employed is defined as descriptive and exploratory. We gathered quantitative data through a structured questionnaire, reflecting the level of digital competence of the participating teachers. The results obtained from this study reveal significant deficiencies in the development of teaching digital competence, especially in the areas of robotics and educational programming. Importantly, the teachers in the sample exhibit a high level of knowledge in active pedagogies. In conclusion, we highlight the need to design training and professional development programs that enhance the digital competence of active primary education teachers, with an emphasis on training schemes geared towards robotics and educational programming.

Reform of Robotics Engineering Practice Teaching in Applied Undergraduate Colleges and Universities Based on OBE Education

The modern society has put forward higher level requirements for the development of robotics technology, which also puts forward new challenges to the teaching practice of robotics engineering majors in applied undergraduate colleges and universities. In this paper, guided by the concept of OBE education, we discuss the reform strategies for the practical teaching of robotics engineering majors in applied undergraduate colleges and universities from the three aspects of project-driven learning, focusing on diversified assessment, and being practice-oriented. Through the implementation of these reform measures, students' practical ability, teamwork ability and problem-solving ability can be better cultivated, and their application level in the field of robotics engineering can be enhanced.

Acceptance of educational robotics: Evolution and validation of the unified theory of acceptance and use of technology via structural equation modeling

Fully understanding factors that are related to teachers’ behavioural intentions to use and acceptance of Educational Robotics (ER) in their classes, particularly among students with disabilities, is a big challenge. In particular, social psychology models may be used more consistently to inform scholars about the paths and the strength of interrelated factors influencing learning support teachers’ acceptance of ER. In this study, the Almere model, an evolution and adaptation of the Unified Theory of Acceptance and Use of Technology (UTAUT) as used in Conti and colleagues was validated. The model is directed to measure acceptance of ER in a sample of 319 learning support teachers via structural equation modeling. Results showed a model explaining a good percentage of variance. In the learning support teachers’ intentions to use ER with students with disabilities, positive and direct effects were exerted by teachers’ positive attitudes toward robotics, and by their perception of the enjoyment and usefulness of robotics. Furthermore, results showed that perception of enjoyment in using ER was strongly and positively associated with perceived sociability and this, in turn, was positively associated with levels of trust. Finally, perceived sociability was positively associated with social presence perceptions.

The impact of educational robots on students' computational thinking: A meta-analysis of K-12

The impact of educational robots on students' computational thinking: A meta-analysis of K-12