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Cited by 70Teaching and Learning ProgrammingEducational Robotics and EngineeringGender and Technology in Education

Computational Thinking and Robotics: A Teaching Experience in Compulsory Secondary Education with Students with High Degree of Apathy and Demotivation

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Díaz-Lauzurica, Belkis, Moreno-Salinas, David·2019·Sustainability
DOI 10.3390/su11185109
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Abstract

In present and future society, all individuals must be able to face the problems, risks, advantages and opportunities that will arrive with new paradigms in the labour market, social relations and technology. To reach this goal, a quality and inclusive education together with a proper and complete formation in technology (communications, robotics, programming, computational thinking (CT), etc.) must be imparted at all educational levels. Moreover, all individuals should have the same opportunities to develop their skills and knowledge, as stated in Goal 4 of the Sustainable Development Goals, Sustainable Education. Following this trend, in the present work, a practical experience about how to teach CT using robotics is developed, showing the results and evaluation of the lessons on robotics taught to students in their 4th year of compulsory secondary education, and where the students showed a high degree of apathy and demotivation. The teaching unit was based on an action research approach that includes a careful selection of pedagogical techniques and instruments to attract and keep the attention and interest of the students. In addition to the robotics lessons, a previous computational thinking training with Blockly Games was carried out, which contributed to noticeably increase the students motivation and to introduce them to the programming of robots. Moreover, gamification was used to motivate and evaluate the individual knowledge, and the students were required to present the work performed through a final project. The individual needs of the students were fulfilled with a daily monitoring. The results show that the pedagogical techniques, instruments and evaluation were adequate to increase the motivation of the students and to obtain a significant learning, showing how the teaching of CT may attract students that have lost interest and motivation, while providing them with abilities that will be essential for the learning throughout life.

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Research works printed but not linked: 39

  1. Kazimieras, J.; Katiliute, E. Complex evaluation of sustainability in engineering education: Case & analysis. J. Clean. Prod. 2016, 120, 13-20. [CrossRef] · DOI
  2. McLaughlan, R. Instructional Strategies to Educate in Sustainability in Technology Assessment. Int. J. Eng. Educ. 2007, 23, 201-208.
  3. Tabet, N.; Gedawy, H.; Alshikhabobakr, H.; Razak, S. From Alice to Python. Introducing Text-based Programming in Middle Schools. In Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education ITiCSE 2016, Arequipa, Peru, 9-13 July 2016; pp. 124-129.
  4. Dwyer, H.; Hill, C.; Hansen, A.; Iveland, A.; Franklin, D.; Harlow, D. Fourth Grade Students Reading Block-Based Programs: Predictions, Visual Cues, and Affordances. In Proceedings of the Eleventh Annual International Conference on International Computing Education Research ICER 2015, Omaha, NE, USA, 9-13 August 2015; pp. 111-119.
  5. Papadakis, S.; Kalogiannakis, M.; Orfanakis, V.; Zaranis, N. The Appropriateness of Scratch and App Inventor as Educational Environments for Teaching Introductory Programming in Primary and Secondary Education. Int. J. Web Based Learn. Teach. Technol. (IJWLTT) 2017, 12, 58-77. [CrossRef] · DOI
  6. Papadakis, S.; Orfanakis, V. The combined use of Lego Mindstorms NXT and App Inventor for teaching novice programmers. In Proceedings of the International Conference EduRobotics 2016, Athens, Greece, 25 November 2016; pp. 193-204.
  7. Papadakis, S.; Orfanakis, V. Comparing novice programming environments for use in secondary education: App Inventor for Android vs. Alice. Int. J. Technol. Enhanc. Learn. 2018, 10, 44-72. [CrossRef] · DOI
  8. Mills, J.E.; Treagust, D.F. Engineering education-Is problem-based or project-based learning the answer? Australas. J. Eng. Educ. 2003, 3, 2-16.
  9. Thomas, J.W. A Review of Research on Project-Based Learning; The Autodesk: San Rafael, CA, USA, 2000.
  10. Roman-Gonzalez, M.; Pérez-González, J.C.; Moreno-León, J.; Robles, G. Extending the nomological network of computational thinking with non-cognitive factors. Comput. Hum. Behav. 2017, 80, 441-459. [CrossRef] · DOI
  11. Wing, J. M. Computational thinking. Commun. ACM 2006, 49, 33-35. [CrossRef] · DOI · Paper page
  12. Wing, J.M. Computational thinking and thinking about computing. Philos. Trans. R. Soc. A 2008, 366, 3717-3725. [CrossRef] · DOI · Paper page
  13. Barr, V.; Stephenson, C. Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community? Inroads 2011, 2, 48-54. [CrossRef] · DOI · Paper page
  14. Lee, I.; Martin, F.; Denner, J.; Coulter, B.; Allan, W.; Erickson, J.; Malyn-Smith, J.; Werner, L. Computational thinking for youth in practice. Inroads 2011, 2, 32-37. [CrossRef] · DOI · Paper page
  15. Grover, S.; Pea, R. Computational thinking in K-12 a review of the state of the field. Educ. Res. 2013, 42, 38-43. [CrossRef] · DOI
  16. Ioannou, I.; Charoula A. A Framework and an Instructional Design Model for the Development of Students' Computational and Algorithmic Thinking. In Proceedings of the 10th Mediterranean Conference on Information Systems MCIS 2016, Paphos, Cyprus, 4-6 September 2016; p. 19.
  17. Bocconi, S.; Chioccariello, A.; Dettori, G.; Ferrari, A.; Engelhardt, K. Developing Computational Thinking in Compulsory Education-Implications for Policy and Practice; Joint Research Centre: Seville, Spain, 2016, doi:10.2791/792158. · DOI
  18. Cabrera, J.M. Programación informática y robótica en la enseñanza básica. Avances en Supervisión Educativa 2015, 24. [CrossRef] · DOI
  19. Moreno-Leon, J; Roman-Gonzalez, M.; Robles, G. Can we Measure Computational Thinking with Tools? Present and Future of Dr. Scratch. In Proceedings of the Seminar Series on Advanced Techniques and Tools for Software Evolution SATToSE 2017, Madrid, Spain, 7-9 June 2017.
  20. Shoop, R.; Flot, J.; Higashi, R.; Witherspoon, E.; McKenna J. Using Model Eliciting Activities to Engage Students in Computational Thinking Practices in Robotics Classrooms. In Proceedings of the High Impact Technology Exchange Conference HI-TECH 2016, Pittsburgh, PA, USA, 25-28 July 2016.
  21. Toh, D.; Ravintharan; Lim, M; Wee, L.K.; Ong, M. Robotics for Learning. arXiv 2015, arxiv:1502.01089. · arXiv
  22. Kanemune, S.; Shirai, S.; Seeichi, T. Informatics and programming education at primary and secondary in Japan. Olymp. Inform. 2017, 11, 143-150. [CrossRef] · DOI
  23. Liu, X.; Gong, X.; Wang, F.; Sun, R.; Gao, Y.; Zhang, Y.; Zhou, J.; Deng, X. A New Framework of Science and Technology Innovation Education for K-12 in Qingdao. In Proceedings of the 2017 American Society for Engineering Education International Forum ASEE 2017, Columbus, OH, USA, 28 June 2017.
  24. Bong-Hyun, K. Development of young children coding drone using block game. Indian J. Sci. Technol. 2016, 9, 1-5. [CrossRef] · DOI
  25. Mikropoulos, T. A.; Bellou, I. Educational robotics as mindtools. Themes Sci. Technol. Educ. 2013, 6, 5-14.
  26. Vidal Abarca, E., Garcia, R., Pérez, F., Eds.; Alianza Editorial: Madrid, Spain, 2010; pp. 45-70, ISBN 978-84-206-8908-1.
  27. Polman, J.L. The zone of proximal identity development in apprenticeship learning. Rev. Educ. 2010, 353, 129-155.
  28. Csikszentmihalyi, M. Flow and the Foundations of Positive Psychology; Springer: Berlin, Germany, 2014; ISBN 978-94-017-9088-8.
  29. Tello-Leal, E.; Guerrero-Melendez, T.; Saldívar-Alonso, V. A review of Lego Mindstorms robotics platform for educational and research applications. Sist. Telemát. 2013, 11, 9-27. [CrossRef] · DOI
  30. Sabandar, N.; Noldy, R.; Suryana, E. Kahoot!: Bring the Fun Into the Classroom!. Indones. J. Inform. Educ. 2018, 2, 127-134. [CrossRef] · DOI
  31. Weitzman, E.; Miles, M. Computer Programs for Qualitative Data Analysis; SAGE Publications: London, UK, 1995; ISBN 0-8039-5537-5.
  32. Lewins, A.; Silver, C. Using Software in Qualitative Research: A Step-by-Step Guide; SAGE Publications: London, UK, 2014; ISBN 9781446249734.
  33. Mertens, D. Research and Evaluation in Education and Psychology: Integrating Diversity with Quantitative, Qualitative, and Mixed Methods; SAGE Publications: London, UK, 2010; ISBN 978-1-4129-7190-4.
  34. Savin-Baden, M.; Major, C.H. Qualitative Research: The Essential Guide to Theory and Practice; Routledge: Abingdon, UK, 2013; ISBN 978-0-415-67478-2.
  35. Grover, S.; Bienkowski, M.; Niekrasz, J.; Hauswirth, M. Assessing Problem-Solving Process At Scale. In Proceedings of the Third ACM Conference on Learning At Scale 2016, Edinburgh, UK, 25-26 April 2016.
  36. Nilsson, L.; Eklund, M.; Nyberg, P.; Thulesius, H. Driving to learn in a powered wheelchair: The process of learning joystick use in people with profound cognitive disabilities. Am. J. Occup. Ther. 2011, 65, 652-660. [CrossRef] [PubMed] · DOI
  37. Somekh, B. Action Research: A Methodology for Change and Development; McGraw-Hill Education: Berkshire, UK, 2006; ISBN 0-3352-1659-5.
  38. Brabury-Huang, H. What is good action research? Why resurgent interest? Act. Res. 2010, 8, 93-109.
  39. CrossRef · DOI · Paper page

Cited by

Sorted by citations

Authors
Díaz-Lauzurica, B., & Moreno-Salinas, D.
Affiliations
Universidad Nacional de Educación a Distancia (ES)
Corresponding author
Moreno-Salinas, David
ORCID
Venue
Sustainability
Publisher
MDPI AG
Published
2019-09-18
Volume
11
Issue
18
Pages
5109
ISSN
2071-1050 (ISSN-L)
DOI
10.3390/su11185109
OpenAlex
W2973608166
Cited by
70
References
2
Citations by year
2026: 7 · 2025: 8 · 2024: 22 · 2023: 13 · 2022: 6 · 2021: 6 · 2020: 8
FWCI
5.15
Type
journal article
Language
en
Open access
Licence
Locations
  • Sustainability (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • UNED repository (submittedVersion, other-oa)
Funders
Ministerio de Economía y Competitividad (DPI2017-84259-C2-2-R)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
  • Physical Sciences › Computer Science › Artificial Intelligence › Educational Robotics and Engineering
  • Social Sciences › Gender Studies › Gender and Technology in Education
Keywords
computational thinking, robotics education, student apathy, demotivation, secondary education, Blockly, gamification, student motivation, inclusive education, Sustainable Development Goal 4, programming education, action research

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