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Creators/Authors contains: "Zhang, Yuanlin"

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  1. ; ; (, International Society of the Learning Sciences)
    Abstract: There is a consensus to introduce data science to secondary schools. However, data science is interdisciplinary in nature and not easy to teach in K−12 settings. We proposed a new approach to integrate mathematics, statistics, and programming—the foundations of data science—for high school students based on set theory and logic. We developed an 8-week data science foundation course and implemented it in a public high school. We conducted semistructured group interviews to collect students’ feedback on the course and the new approach. Students thought the approach could well connect the topics and helped them learn the interdisciplinary content. 
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    Free, publicly-accessible full text available June 10, 2025
  2. ; ; ; ; (, SIGCSE 2022: The 53rd ACM Technical Symposium on Computer Science Education)
    Accepted Manuscript Full Text Available
  3. ; ; ; ; ; (, Thirteenth AAAI Symposium on Educational Advances in Artificial Intelligence)
    Accepted Manuscript Full Text Available
  4. ; ; (, Proceedings of the 2022 ACM Conference on International Computing Education Research)
    Full Text Available 
  5. ; ; ; ; ; (, Electronic Proceedings in Theoretical Computer Science)
    Full Text Available 
  6. ; ; ; ; ; (, Journal of Computers in Mathematics and Science Teaching)
    Accepted Manuscript Full Text Available
  7. ; ; ; ; (, An Integrated Approach to Data Science Foundations in Computing, Mathematics and Statistics)
    Full Text Available 
  8. ; ; ; ; ; (, Proceedings of the 14th International Conference on Computer Supported Education - Volume 1, CSEDU 2022)
    Full Text Available 
  9. ; ; (, Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education)
    Novice programming learners encounter programming errors on a regular basis. Resolving programming errors, which is also known as debugging, is not easy yet important to programming learning. Students with poor debugging ability hardly perform well on programming courses. A debugging learning trajectory which identifies learning goals, learning pathways, and instructional activities will benefit debugging learning activities development. This study aims to develop a debugging learning trajectory for text-based programming learners. This is accomplished through (1) analyzing programming errors in a logic programming learning environment and (2) examining existing literature on debugging analysis. 
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    Full Text Available