澳大新語 • 2023 UMAGAZINE 28 24 封面專題 • COVER STORY 斷模型，通過數據分析理解成人在廣泛運用科技的學習 環境中解難的能力，也有學生開發了同時供教師、學生 和學校管理層使用的教學質素評估系統。」 人工智能增強人類智能 掌握數據技能不僅能使畢業生從事數據分析、教育設 計、學習分析、教育數據科學等工作，也能提升教師 的教學成效。一些畢業生也在攻讀博士課程，拓展數 據科學在教育的應用。陳教授說：「即使傳統的教學 角色，今日亦可能需要一定的教學數據分析能力，才 能協助每位學生走最合適的學習道路。」 Prof Chen emphasises that data cleaning, statistical analysis, data visualisation, and data-driven decision-making are essential skills for those engaged in teaching and learning analytics. These skills form the backbone of the Analytics in Teaching and Learning specialisation under UM’s Master of Science in Data Science programme, a collaborative offering between the university’s Institute of Collaborative Innovation and FED. Prof Chen, a faculty member in the programme, says that some graduates have applied their skills to analyse the problem-solving abilities of adults in technology-rich environments, based on cognitive diagnosis models. Others have created a comprehensive teaching quality evaluation platform for a variety of stakeholders, including teachers, students, and school administrators. Enhancing Human Intelligence With AI Mastering data skills not only prepares graduates for careers in data analysis, educational design, learning analytics, and educational data science but also enables teachers to enhance their teaching effectiveness. Furthermore, some of the graduates are pursuing doctoral degrees to further explore the applications of data science in education. ‘Even those in traditional teaching roles may find themselves in need of a certain level of proficiency in data analysis, which will improve their ability to tailor learning paths for their students,’ says Prof Chen. 陳孚教授 Prof Chen Fu In an ideal world, personalised education would be accessible to everyone, and data science is gradually making this dream a reality. According to Chen Fu, assistant professor in the Faculty of Education (FED) at the University of Macau (UM), technologies such as educational data mining and learning analytics are revolutionising teaching and learning. Predicting and Enhancing Student Performance Big data tools enable educators and schools to analyse, predict, and enhance student academic performance. ‘For example, machine learning models trained on historical data can identify incoming students who are likely to struggle in their studies,’ says Prof Chen. Moreover, data science-driven innovations are contributing to learning path recommendations, automated grading, game-based learning, and the use of virtual learning assistants powered by large language models, adds Prof Chen. As an expert in educational data mining, Prof Chen has studied the digital reading literacy of over 70,000 students across 14 countries and regions. Analysing data about their learning process and outcomes, he has proposed strategies for improvement. Through deep-learning models, he is also developing a system to assess and interpret students’ cognitive states during learning, such as skill levels, emotions, and engagement levels, as well as learning outcomes such as correct or incorrect responses.
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