Robotics in the international educational space: Integration and the experience

Kerimbayev, Nurassyl; Beisov, Nurbol; Kovtun, natoly; Nurym, Nurdaulet; Akramova, Aliya

doi:10.1007/s10639-020-10257-6

Cited by 13 publications

(8 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Collaborative robotic learning, which takes place with computer support, contributes not only to learning, a robotic app is a motivating activity for students that fosters collaboration between them, has a positive effect on learning and the emotional state of students, when a synergistic effect can be observed between participants in the educational process (Tang et al, 2020 ; Kerimbayev et al, 2020 ; Ribeiro & Lopes, 2020 ; Yang et al, 2020 ; Ospennikova et al, 2015 ; Lubold et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Educational Robotics: Development of computational thinking in collaborative online learning

et al. 2023

Self Cite

View full text Add to dashboard Cite

In the previous study the work experience on organization of teaching Robotics to secondary school students at school lessons and in study groups was introduced. This study which was conducted within 2019 and 2021 covered the period of distant learning caused by COVID-19 pandemic and even post-pandemic period, when a part of school students continued learning online. The study deals with the problem of developing school students’ computational thinking in online learning. We consider computational thinking as a set of cognitive skills of solving educational and cognitive problems. The research questions raised were aimed at solving the problem of the influence of Educational Robotics on developing computational thinking. During the research we have found out that due to the adaptability of robots, Educational Robotics, the development of individual learning programs, and the arrangement of collaborative online learning are instruments and a solution to the problem of developing computational thinking. The main components of computational thinking, which were studied within those 3 years, are the following: algorithmic thinking, ability to program, and efficiency in team work. The influence of the learning strategy we chose enabled us to determine the level of computational thinking and its dependence on learning Robotics. We used statistical criteria in order to summarize the results of our research. The statistics provided suggests progress in the indicator tracked. Based on the experimental data received we approximated reliability (R 2 ) and relevant exponential equation (trend lines). The research we carried out also has led to the general conclusion that Educational Robotics helps to create synergistic learning environment for stimulating students’ motivation, collaboration, self-efficacy and creativity.

show abstract

Section: Methodsmentioning

confidence: 99%

Educational Robotics: Development of computational thinking in collaborative online learning

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…[ [12] Pondering the transformation of teacher' professional roles Kessler [44] Analytic essay of AI in the language teaching Petit et al [ [68] Exploring online assessments system applied for the measurement in medical education 4 Complexity [81] Investigating the existing digital structural violence and the approaches to tackling it Rowe [82] Exploring the effect on education reform brought by intangible economy which is shaped by globalized datasets such as OECD PISA and artificial intelligence Ally [83] Identifying the shaping forces for future education and competencies required by future digital teachers Song and Wang [84] Analyzing analysis of worldwide educational artificial intelligence research development in recent twenty years Ulum [ [106] Constructing guidelines to apply data mining techniques to predict student success Renz and Hilbig [107] Analyzing the drivers and barriers that currently affect data-based teaching and learning paths from the perspective of EdTech companies Gulson and Witzenberger [108] Investigating how automated education governance assemblage includes new forms of expertise and authority and constitutes EduTech as an important policy space Kerimbayev et al [109] Review the research aimed at studying robot-man interaction, taking Russia and Kazakhstan as an example of the international cooperation in the sphere of robotics Fu et al [ 6 Complexity research questions of the sample papers were classified into three dimensions: (a) development, focusing on the knowledge presentation model; (b) extraction, centering on how to obtain knowledge from data mining; and (c) application, emphasizing the human-computer interaction through information derivation. Secondly, with regard to technology adoption, the focus was on the types of technology that the study adopted, which were further categorized into software (e.g., algorithms and programs) and hardware (e.g., sensors and devices such as virtual reality).…”

Section: 2mentioning

confidence: 99%

A Review of Artificial Intelligence (AI) in Education from 2010 to 2020

et al. 2021

View full text Add to dashboard Cite

This study provided a content analysis of studies aiming to disclose how artificial intelligence (AI) has been applied to the education sector and explore the potential research trends and challenges of AI in education. A total of 100 papers including 63 empirical papers (74 studies) and 37 analytic papers were selected from the education and educational research category of Social Sciences Citation Index database from 2010 to 2020. The content analysis showed that the research questions could be classified into development layer (classification, matching, recommendation, and deep learning), application layer (feedback, reasoning, and adaptive learning), and integration layer (affection computing, role-playing, immersive learning, and gamification). Moreover, four research trends, including Internet of Things, swarm intelligence, deep learning, and neuroscience, as well as an assessment of AI in education, were suggested for further investigation. However, we also proposed the challenges in education may be caused by AI with regard to inappropriate use of AI techniques, changing roles of teachers and students, as well as social and ethical issues. The results provide insights into an overview of the AI used for education domain, which helps to strengthen the theoretical foundation of AI in education and provides a promising channel for educators and AI engineers to carry out further collaborative research.

show abstract

“…This form of teaching intervention lasts about 1-5 weeks or above (Chen et al, 2018;Hsiao et al, 2022;Noh & Lee, 2019). Third, ER, as the basic course of curriculum computer education, usually last one semester or one academic F I G U R E 6 Funnel plot (Hedges' g) of generative ability year (>16 weeks) (Hutchins et al, 2019;Kerimbayev et al, 2020;Witherspoon et al, 2017). However, long-term technical learning would reduce students' interest and learning motivation, and the learning effect may not be as good as short-term intervention (Chauhan, 2017).…”

Section: Intervention Durationmentioning

confidence: 99%

Effective instruction conditions for educational robotics to develop programming ability of K‐12 students: A meta‐analysis

Sun

Zhou

2022

Computer Assisted Learning

View full text Add to dashboard Cite

Background As one of the mainstream forms of programming education, educational robotics (ER) have been a crucial way to develop K‐12 students' programming ability. Objectives The purpose of this study is to clarify the content of programming ability, to verify the effectiveness of ER as a teaching method to improve students' programming abilities and the conditions for more effective instructional design. Methods The method of meta‐analysis was adopted to conduct this study. 4180 K‐12 students from 36 studies with a total of 85 effect sizes were captured. Based on the contents of included literature, an ER programming ability model was put forward, which was composed of essential ability and generative ability. The meta‐analysis of essential ability (N = 22) and generative ability (N = 64) was carried out respectively. Results and conclusion The results showed that ER activity has a positive effect on the essential ability (Hedges' g = 0.539 CI [0.327, 0.752], p < 0.001) and generative ability (Hedges' g = 0.535 CI [0.426, 0.643], p < 0.001). Additionally, the results of moderator analysis showed that utilizing ER to solve mathematics problems is able to more effectively develop students' essential ability, while the STEM interdisciplinary ER instruction had the largest effect on generative ability. Simultaneously, the individual programming form is conducive to students' essential ability, while the generative ability is significantly affected by the cooperative programming form. No matter for the evaluation of essential ability or generative ability, ER had the greatest effect on kindergarten children' programming ability. Moreover, the intervention duration ranging from 1 to 5 weeks, the samples size less than 50 and the choice of measurement scale can effectively improve students' programming ability. Implications This research enriched the theoretical basis of programming ability, and provided reference and guidance for K‐12 programming teaching practitioners and researchers in ER teaching design and practise.

show abstract

Robotics in the international educational space: Integration and the experience

Cited by 13 publications

References 19 publications

Educational Robotics: Development of computational thinking in collaborative online learning

Educational Robotics: Development of computational thinking in collaborative online learning

A Review of Artificial Intelligence (AI) in Education from 2010 to 2020

Effective instruction conditions for educational robotics to develop programming ability of K‐12 students: A meta‐analysis

Contact Info

Product

Resources

About