Educational Robotics: Platforms, Competitions and Expected Learning Outcomes

Evripidou, Salomi; Georgiou, Kyriakoula; Doitsidis, Lefteris; Amanatiadis, Angelos; Zinonos, Zinon; Chatzichristofis, Savvas A.

doi:10.1109/access.2020.3042555

Cited by 66 publications

(30 citation statements)

References 143 publications

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“…Several educational robots have been proposed, not limited to DNN robotics applications. S. Evripidou et al have investigated and analyzed existing platforms of educational robotics and their learning outcomes [ 12 ]. R. Burbaite et al proposed a framework that allows teachers to educate students on computer science algorithms and programming using multiple robots [ 13 ].…”

Section: Requirements Of Basic Educational Kitmentioning

confidence: 99%

Development of a Basic Educational Kit for Robotic System with Deep Neural Networks

Kanamura

Suzuki

Suga

et al. 2021

Sensors

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In many robotics studies, deep neural networks (DNNs) are being actively studied due to their good performance. However, existing robotic techniques and DNNs have not been systematically integrated, and packages for beginners are yet to be developed. In this study, we proposed a basic educational kit for robotic system development with DNNs. Our goal was to educate beginners in both robotics and machine learning, especially the use of DNNs. Initially, we required the kit to (1) be easy to understand, (2) employ experience-based learning, and (3) be applicable in many areas. To clarify the learning objectives and important parts of the basic educational kit, we analyzed the research and development (R&D) of DNNs and divided the process into three steps of data collection (DC), machine learning (ML), and task execution (TE). These steps were configured under a hierarchical system flow with the ability to be executed individually at the development stage. To evaluate the practicality of the proposed system flow, we implemented it for a physical robotic grasping system using robotics middleware. We also demonstrated that the proposed system can be effectively applied to other hardware, sensor inputs, and robot tasks.

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Section: Requirements Of Basic Educational Kitmentioning

confidence: 99%

Development of a Basic Educational Kit for Robotic System with Deep Neural Networks

Kanamura

Suzuki

Suga

et al. 2021

Sensors

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“…As stated in the literature, there are numerous processes for which ER can be utilized in the educational procedures (i) as a tool to learn or teach; (ii) as a generic subject with its own curriculum; and lastly (iii) as a tool which could aid students in enhancing their skills. Taking the last statement into consideration, the study in [ 15 ] defines a list of skills that can be developed and improved by using educational robotics. This list includes but is not limited to adaptive and critical thinking skills, collaboration and communication skills, problem solving skills, creativity skills, and finally management skills.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The tasks/activities may include but are not limited to speaking, mathematics, memory or mime games, etc. The aim of the proposed work is not to deeply examine the tasks/activities offered by educational robots [ 15 , 26 ] or which skills are developed when students undertake specific tasks/activities, but rather to propose a decentralized solution for educational robots that can automatically create or update a student record at the completion of a task/activity. In this context, at stage 3, when a certain task/activity concludes, the educational robot creates a new or updates the existing record of the student using the student’s Ethereum address as well as the values of certain skills, such as: adaptive and critical thinking skills, collaboration and communication skills, problem solving skills, creativity skills, and finally management skills.…”

Section: Skillschain Architecturementioning

confidence: 99%

skillsChain: A Decentralized Application That Uses Educational Robotics and Blockchain to Disrupt the Educational Process

Christodoulou

Andreou

Zinonos

2021

Sensors

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Our epoch is continuously disrupted by the rapid technological advances in various scientific domains that aim to drive forward the Fourth Industrial Revolution. This disruption resulted in the introduction of fields that present advanced ways to train students as well as ways to secure the exchange of data and guarantee the integrity of those data. In this paper, a decentralized application (dApp), namely skillsChain, is introduced that utilizes Blockchain in educational robotics to securely track the development of students’ skills so as to be transferable beyond the confines of the academic world. This work outlines a state-of-the-art architecture in which educational robotics can directly execute transactions on a public ledger when certain requirements are met without the need of educators. In addition, it allows students to safely exchange their skills’ records with third parties. The proposed application was designed and deployed on a public distributed ledger and the final results present its efficacy.

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“…Robotics competitions that bring students together to solve engineering challenges and engage in robot battles have grown in popularity over the last 20 years [1]. With the increase in available educational robotics technologies (such as LEGO robotics, VEX Robotics, micro:bit, and other tools) robotics competitions have become ubiquitous in most school communities around the world [2].…”

Section: Introductionmentioning

confidence: 99%

“…These competitions have been shown to have positive learning outcomes for those who participate [3,4]. Specifically, research into robotics competitions has shown that they positively impact the development of: (1) problem solving skills, (2) self efficacy, (3) computational thinking, (4) creativity, (4) collaboration, and (5) motivation [1]. Additionally, online and in person robotics challenges encourage students to think for themselves and come up with their own ideas in the absence of the step-by-step instructions often provided in classroom settings.…”

Section: Introductionmentioning

confidence: 99%