Mary Patrick Uko (Ph.D), Dr Mfon Edet Thompson & Dr Glory Udo Evans
The Use of AI to Support Computational Thinking in Mathematical Tasks Using GeoGebra in Measurement and Evaluation Course
This study explored the potential of artificial intelligence (AI), specifically ChatGPT, to improve computational thinking (CT) skills in mathematics education using GeoGebra. It examined how AI can integrate CT into core statistical concepts in a measurement and evaluation course in a college of education in Akwa Ibom State, Nigeria. Using an exploratory mixed-methods research design, to gain insight of the subject matter since it is not well defined, 100 second-year undergraduate students from the School of Science, with an emphasis on those with a background in computer science and access to the necessary technological resources, from a population of 2,674, were purposively sampled. To ensure the proper representation of the sample, participants were drawn from diverse academic backgrounds like mathematics, physics, biology giving credence to prior experience with GeoGebra, and familiarity with AI tools to contextualize the findings. To enhance the comprehension of the functionalities of this design, the researchers employed an educational design research (EDR) approach. This methodology allowed for a detailed exploration of participant experiences and the integration of quantitative and qualitative data. Quantitative data on CT performance and qualitative insights on AI interactions were analysed using a descriptive qualitative method and chi-square. Results indicate that ChatGPT facilitated the creation of GeoGebra commands, promoted algorithmic thinking, debugging, and independent problem solving. However, challenges such as command errors and inconsistent responses from AI have been observed. The study concludes that AI, combined with GeoGebra, promises differentiated and personalized lessons, highlighting its role in improving CT in mathematics education. Future research should explore its broader applications and address technical challenges for better integration.
Mary Patrick Uko (Ph.D), Dr Mfon Edet Thompson & Dr Glory Udo Evans
The Use of AI to Support Computational Thinking in Mathematical Tasks Using GeoGebra in Measurement and Evaluation Course
This study explored the potential of artificial intelligence (AI), specifically ChatGPT, to improve computational thinking (CT) skills in mathematics education using GeoGebra. It examined how AI can integrate CT into core statistical concepts in a measurement and evaluation course in a college of education in Akwa Ibom State, Nigeria. Using an exploratory mixed-methods research design, to gain insight of the subject matter since it is not well defined, 100 second-year undergraduate students from the School of Science, with an emphasis on those with a background in computer science and access to the necessary technological resources, from a population of 2,674, were purposively sampled. To ensure the proper representation of the sample, participants were drawn from diverse academic backgrounds like mathematics, physics, biology giving credence to prior experience with GeoGebra, and familiarity with AI tools to contextualize the findings. To enhance the comprehension of the functionalities of this design, the researchers employed an educational design research (EDR) approach. This methodology allowed for a detailed exploration of participant experiences and the integration of quantitative and qualitative data. Quantitative data on CT performance and qualitative insights on AI interactions were analysed using a descriptive qualitative method and chi-square. Results indicate that ChatGPT facilitated the creation of GeoGebra commands, promoted algorithmic thinking, debugging, and independent problem solving. However, challenges such as command errors and inconsistent responses from AI have been observed. The study concludes that AI, combined with GeoGebra, promises differentiated and personalized lessons, highlighting its role in improving CT in mathematics education. Future research should explore its broader applications and address technical challenges for better integration.