A Robotic Gamification Model for Climate Change Literacy for Green Innovation and Entrepreneurship in Sub-Saharan Africa

  • Stephen Ochieng Oguta Information Technology Department, Durban University of Technology, Durban, South Africa
  • Sunday Ojo Information Technology Department, Durban University of Technology, Durban, South Africa
  • Benard Maake Computing Sciences Department Kisii University, Nairobi, Kenya

Abstract

Purpose – The study aims to introduce the Gamified Climate Change Literacy for Green Innovation and Entrepreneurship Training Model, integrating the Social Robot Nao to enhance climate change education in Sub-Saharan Africa. The objective is to empower learners with knowledge about carbon emissions and to foster engagement in green innovations.

Method – The model integrates principles from Self-determination theory, Behavioral reinforcement theory, and the Mechanics, Dynamics, and Aesthetics gamification framework. Development and validation were conducted using Design Science Methodology and probability theory. The implementation involves desktop training via Moodle and interactive sessions with the Nao robot. The evaluation is based on the Technology Acceptance Model.

Results – The proposed model incorporates random badge awards to enhance engagement and sustain motivation, addressing the shortcomings of traditional reward systems that rely on extrinsic motivation. The integration of the Nao robot adds an interactive element, further increasing learner engagement and interest.

Conclusion – The study successfully develops a theoretical framework, mathematical modeling, and architectural design to sustain learner interest in climate change education. By combining gamification with interactive technology, the model redefines educational strategies in this domain.

Recommendations – Future implementations should consider scalability and the integration of additional interactive technologies to further enhance engagement. Continuous feedback from learners should be incorporated to refine and improve the model.

Research Implications – The study provides a robust framework for utilizing gamification and robotics in educational settings, particularly in regions with limited resources. It opens avenues for further research into the long-term impacts of such models on learner engagement and knowledge retention in climate change education.

Author Biographies

Stephen Ochieng Oguta, Information Technology Department, Durban University of Technology, Durban, South Africa

Stephen Oguta is a PhD in Information Technology student at Durban University of Technology. He has a master's degree in Telecommunications Engineering from Jomo Kenyatta University of Technology in Kenya. His research interests are Gamification, Artificial intelligence, Big data, Computer security, Cloud computing, and neural networks among others.

Sunday Ojo, Information Technology Department, Durban University of Technology, Durban, South Africa

Prof. Sunday Ojo is a Computer science professor at Durban University of Technology South Africa. He has PhD in Database Systems from the University of Glasglow in Germany. His research interests are in Computational Linguistics, Machine Learning, Data Science, Gamification, Climate change, Cloud computing, and Neural Networks among others.

Benard Maake, Computing Sciences Department Kisii University, Nairobi, Kenya

Dr. Benard Maake is a Senior Lecturer in the computing sciences department at Kisii University Kenya. He has PhD in computer systems engineering from Tshwane University of Technology in South Africa. His research interests are Recommender Systems, Big Data, Machine Learning, Cloud computing, Natural Language Processing, and Artificial intelligence.

Published
2024-06-13
How to Cite
OGUTA, Stephen Ochieng; OJO, Sunday; MAAKE, Benard. A Robotic Gamification Model for Climate Change Literacy for Green Innovation and Entrepreneurship in Sub-Saharan Africa. International Journal of Computing Sciences Research, [S.l.], v. 8, p. 2905-2932, june 2024. ISSN 2546-115X. Available at: <//stepacademic.net/ijcsr/article/view/480>. Date accessed: 24 july 2024.
Section
Articles