معرفی
Daniel Zingaro is a Professor, Teaching Stream and Associate Chair (CSC) in the Department of Computer Science at the University of Toronto Mississauga. He holds a PhD from the Ontario Institute for Studies in Education (OISE) at the University of Toronto, focusing on Computer Science Education. His research emphasizes measuring and improving student learning in CS, particularly through concept inventories and Peer Instruction methodologies.
Education:
- PhD in Computer Science Education, OISE, University of Toronto
Research Interests: Zingaro's work centers on pedagogy in introductory CS courses, including the design of effective teaching strategies, the impact of Peer Instruction, and the development of assessment tools like the Basic Data Structures Inventory (BDSI). He also explores the role of generative AI in programming education and competitive programming pedagogy.
Publications: His work spans textbooks (e.g., Algorithmic Thinking, Learn to Code by Solving Problems) and peer-reviewed articles on topics like student performance prediction and educational technology. Recent trends focus on integrating AI tools like GitHub Copilot into teaching and assessing foundational CS concepts.
Awards:
- Best Paper Award (Journal of Online Learning and Teaching, 2012)
Teaching & Advising: Teaches courses ranging from introductory programming to advanced topics like Operating Systems and Algorithms. He actively develops curriculum materials and advocates for student-centered learning approaches. Current initiatives include exploring LLMs' impact on CS1 pedagogy and expanding accessible educational resources in multiple languages.
Labs/Teams: Leads projects on AI-assisted programming education and collaborates with institutions globally to refine concept inventories and Peer Instruction techniques.
