A Proposal for Performance-based Assessment of the Learning of Machine Learning Concepts and Practices in K-12

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 o...

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Bibliographic Details
Published in:Informatics in education 2022, Vol.21 (3), p.479-500
Main Authors: Gresse von Wangenheim, Christiane, Da Cruz Alves, Nathalia, Rauber, Marcelo Fernando, Hauck, Jean Carlo Rossa, YETER, Ibrahim H
Format: Article
Language:English
Subjects:
Artificial Intelligence
Artificial neural networks
assessment
Classification
Computer Science Education
Concept Formation
Education
Educational evaluation
Educational Objectives
Elementary Secondary Education
Feedback
High School Students
ICT Information and Communications Technologies
Image classification
Intelligent Tutoring Systems
Interrater Reliability
K-12
Kindergarten
Learning Processes
Machine learning
Middle School Students
Object recognition
Performance Based Assessment
Research Reports
Rubric
Scoring Rubrics
Student Evaluation
Students
Teaching Methods
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Summary: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 the performance-based assessment of the learning of concepts and practices regarding image classification with artificial neural networks in K-12. The assessment is based on the examination of student-created artifacts as a part of open-ended applications on the use stage of the Use-Modify-Create cycle. An initial evaluation of the scoring rubric through an expert panel demonstrates its internal consistency as well as its correctness and relevance. Providing a first step for the assessment of concepts on image recognition, the results may support the progress of learning ML by providing feedback to students and teachers.
ISSN:1648-5831
2335-8971
DOI:10.15388/infedu.2022.18