Affiliation:
1. Center for Institutional Research and Data Analytics National Yang Ming Chiao Tung University Hsinchu Taiwan
2. Department of Computer Science and Information Engineering National Central University Taoyuan City Taiwan, ROC
Abstract
AbstractBackgroundNumerous higher education institutions worldwide have adopted English‐language‐medium computer science courses and integrated online problem‐solving competitions to bridge gaps in theory and practice (Alhamami Education and Information Technologies, 2021; 26: 6549–6562).ObjectivesThis study aimed to investigate the factors influencing the use of online competitions in machine learning courses and their impact on student learning. We also analyse disparities in learning outcomes and instructional language effects (Chinese vs. English).MethodsAmong 123 participants at northern Taiwan university, 74 chose Chinese instruction (CMI), and 49 opted for English instruction (EMI). The course spanned 18 weeks: team formation in week one, data analysis, machine learning, and deep learning from week 2 to 8, draft proposals and oral presentations by week 9, instructor guidance in weeks 9–17, followed by off‐campus competitions. In week 18, students presented projects for evaluation by judges.ResultsThe results showed improved scores in competition proposal writing and oral presentations, especially for CMI students, who excelled in these areas and in terms of creativity. CMI students emphasized domain knowledge, implementation completeness, and technical depth in proposals. The EMI students focused on implementation completeness and artificial intelligence model accuracy, along with creativity.ConclusionCMI students achieved superior outcomes in machine learning courses, particularly in terms of competition proposals, oral presentations, and increased creativity. Instructional language choice significantly influenced learning trajectories, leading to distinct knowledge development focuses for CMI and EMI.
Funder
National Science and Technology Council