Research on Algorithm Thinking in Higher Education Pedagogies Based on Knowledge Engineering

This paper seeks to enhance the efficiency with which engineering undergraduates learn programming by incorporating advanced artificial intelligence applications into engineering education. The development of a knowledge graph dataset tailored for engineering instruction was proposed, accompanied by a recommendation system designed to improve educational outcomes. At its current stage, the system is beginning to take shape, and experimental results indicate it effectively boosts programming learning efficiency among undergraduate students. By collecting and analyzing data from students at two universities, educators can gain deeper insights into individual learning patterns and identify knowledge areas where students may face difficulties. This research investigates the sustainability of the recommendation system within engineering education and explores hybrid instructional techniques that leverage a knowledge-based recommender system. This study actively seeks potential solutions that warrant further development and widespread adoption.