Promoting Engagement in Computing Research for Non-CS Majors

Recent advancements in machine learning (ML) have paved the way for new research paths across various disciplines. Nevertheless, students in the global south often have limited exposure to these breakthroughs and may not actively pursue opportunities in these emerging fields. This paper presents a case study of a training workshop that aimed to involve students from diverse undergraduate programs to actively engage in research opportunities utilizing ML skills in other application domains. To do so, we present empirical quantitative findings based on the participation of 24 undergraduate students from 10 distinct programs in workshops designed to introduce them to ML concepts and applications, as well as the research process and methodologies. We hypothesized that this kind of workshop would lead to a higher intention to do research, along with positive improvements in their assessment of self-efficacy, focusing on perceived behavioral control (PBC), behavioral beliefs (BB), and subjective norms (SN). The workshop led to an improvement in students' PBC in self-efficacy beliefs over their perceived ability, especially in the development of research and ML skills. These findings provide valuable insights and set a clear course for future initiatives, with the aim of further encouraging students' involvement and confidence in computing research initiatives.