Computers and Education: Artificial Intelligence (Jan 2023)
Developing middle school students’ understanding of machine learning in an African school
Abstract
Researchers' efforts to build a knowledge base of how middle school students learn about machine learning (ML) is limited, particularly, considering the African context. Hence, we conducted an experimental classroom study (N = 32) within the context of extracurricular activities in a Nigerian middle school to discern how students engaged with ML activities. Furthermore, we explored whether participation in our intervention program elicit changes in students' ML comprehension, and perceptions. Using multiple qualitative data collection techniques including interviews, pre-post open-ended surveys and written assessments, we uncover evidence that indicated evolution of students’ ML understanding, ethical awareness, and societal implication of ML. In addition, our findings showed that a middle school student can learn and understand ML, even when one had no prior knowledge or interest in science related careers. The findings have implication for pedagogical design of AI instruction in middle school context. We discuss the implication of our results for researchers and relevant stakeholders, highlight the limitations and chart future work paths.