Learning Assessment of Machine Learning in K-12: A Systematic Mapping

• Gustavo de Castro Salvador UFSC
• Christiane Gresse von Wangenheim UFSC http://orcid.org/0000-0002-6566-1606
• Marcelo Fernando Rauber UFSC / IFC
• Abisague Belem Garcia UFSC
• Adriano F. Borgatto UFSC

DOI: https://doi.org/10.5753/wei.2021.15919

Abstract

Machine learning (ML) is being increasingly integrated into our life. Thus, to demystify ML several instructional units are emerging teaching ML already in K-12. Yet, especially the assessment of the learning of ML concepts remains an open issue. Thus, in order to provide an overview on the current state of the art regarding the assessment of the learning of ML in K-12, we performed a systematic mapping. We identified 15 instructional units on ML, which also present the assessment of the students’ learning, mostly in a simple manner by few test questions/quizzes with few proposing performance-based assessments. However, an evident lack of systematic definition and validation of these assessments indicates the need for further research.

Keywords: Learning assessment, Machine Learning, K-12

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