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Marcelo Fernando Rauber; Christiane Gresse von Wangenheim; Pedro Alberto Barbetta; Adriano Ferreti Borgatto; Ramon Mayor Martins; Jean Carlo Rossa Hauck – ACM Transactions on Computing Education, 2025
The current insertion of Machine Learning (ML) in our everyday life demonstrates the importance of introducing the teaching of a basic understanding of ML already in school. Accompanying this trend arises the need to assess the students' learning of ML, yet so far only a few assessment models have been proposed, most of them rather simple, based…
Descriptors: Artificial Intelligence, Middle School Students, High School Students, Computer Science Education
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Ryan, Zachary D.; DeLiema, David – Instructional Science: An International Journal of the Learning Sciences, 2023
This paper articulates an approach to incorporating instructor feedback in design-based research. Throughout the process of designing and implementing curriculum to support middle school students' debugging practices in a summer computer science workshop, our research and practice team utilized instructor-generated conjecture maps as boundary…
Descriptors: Teaching Methods, Feedback (Response), Teacher Attitudes, Computer Science Education
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Wanxue Zhang; Lingling Meng; Bilan Liang – Interactive Learning Environments, 2023
With the continuous development of education, personalized learning has attracted great attention. How to evaluate students' learning effects has become increasingly important. In information technology courses, the traditional academic evaluation focuses on the student's learning outcomes, such as "scores" or "right/wrong,"…
Descriptors: Information Technology, Computer Science Education, High School Students, Scoring
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Gresse Von Wangenheim, Christiane; Da Cruz Alves, Nathalia; Rauber, Marcelo F.; Hauck, Jean C. R.; Yeter, Ibrahim H. – Informatics in Education, 2022
Although Machine Learning (ML) is used already in our daily lives, few are familiar with the technology. This poses new challenges for students to understand ML, its potential, and limitations as well as to empower them to become creators of intelligent solutions. To effectively guide the learning of ML, this article proposes a scoring rubric for…
Descriptors: Performance Based Assessment, Artificial Intelligence, Learning Processes, Scoring Rubrics
Barnes, Tiffany, Ed.; Desmarais, Michel, Ed.; Romero, Cristobal, Ed.; Ventura, Sebastian, Ed. – International Working Group on Educational Data Mining, 2009
The Second International Conference on Educational Data Mining (EDM2009) was held at the University of Cordoba, Spain, on July 1-3, 2009. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented…
Descriptors: Data Analysis, Educational Research, Conferences (Gatherings), Foreign Countries