Examining High School Students’ Self-Efficacy in Machine Learning Practices

Abstract

Artificial Intelligence (AI) has increasingly become a ubiquitous face in our daily lives. Following this trend, many organizations and educational researchers started fostering AI education at the K-12 level. Yet, there is less knowledge about the impact of curriculum interventions on students' self-efficacy. In order to understand K-12 students' AI learning and interests, it is critical to examine their self-efficacy. This paper examines high school students’ self-efficacy in machine learning practices before and after participating in a technology-enhanced AI curriculum intervention for three weeks. We analyzed students’ pre- and post-questionnaire responses to investigate the impact of the AI curriculum intervention on students’ self-efficacy. Our analysis revealed that students’ self-efficacy toward text classification tasks significantly increased after they completed the AI curriculum activities. Additionally, we found that students’ characteristics in terms of their interests and engagement in the activities played a critical role in their self-efficacy.