Assessing the Impact of a STEM Learning Project Model on Artificial Intelligence Education in Higher Learning Institutions

Rahmiati Rahmiati, Nizwardi Jalinus, Hansi Effendi, Rahmat Fadillah and Rizki Ema Wulansari

Data and Metadata, 2024, vol. 3, .623

Abstract: This study investigates the effectiveness of the STEM Learning Project model in enhancing student outcomes in Artificial Intelligence (AI) courses at higher education institutions. The research aimed to assess the model’s impact on students’ cognitive, affective, and psychomotor skills, with a focus on fostering active participation, problem-solving, and interdisciplinary knowledge integration. Employing a mixed-methods approach, the study utilized both qualitative and quantitative data collection methods. The experimental group engaged in the STEM Learning Project, while the control group followed a traditional AI curriculum. Changes in student knowledge and engagement were measured using pre- and post-test surveys, complemented by qualitative insights obtained from interviews and focus group discussions. The results demonstrated progress in both groups, though the experimental group achieved a greater increase in post-test scores (29,87) compared to the control group (29,21). Statistical analyses confirmed that the data satisfied normality and homogeneity assumptions, allowing for parametric testing. An independent sample t-test revealed a significant difference in post-test scores between the two groups, highlighting the effectiveness of the STEM Learning Project model in enhancing students' AI-related skills. This approach notably improved students' cognitive abilities and interdisciplinary knowledge in AI education, establishing it as a promising strategy for preparing students to address the demands of the AI industry. Future research could explore the model's long-term impact on career readiness and its applicability to other technology-driven educational settings.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:3:y:2024:i::p:.623:id:1056294dm2024623

DOI: 10.56294/dm2024.623

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