Assessing the Learning of Machine Learning in K-12: A Ten-Year Systematic Mapping

• Published in: Informatics in education Vol. 22; no. 2; pp. 295 - 328
• Main Authors: Rauber, Marcelo Fernando, Gresse von Wangenheim, Christiane
• Format: Journal Article
• Language: English
• Published: Vilnius Vilniaus Universiteto Leidykla 2023; Vilnius University Press; Institute of Mathematics and Informatics; Vilnius University Institute of Mathematics and Informatics, Lithuanian Academy of Sciences; Vilnius University
• Subjects: Active learning; Algorithms; Artificial Intelligence; assessment; Assessments; Automation; Basic Skills; Cognition & reasoning; Cognitive ability; Cognitive Psychology; Computer science; computing education; Core curriculum; Education; Educational Assessment; Educational Experience; Educational Methods; Educational objectives; Educational Strategies; Educational Technology; Elementary Secondary Education; Evaluation Methods; Feedback (Response); Guidelines; ICT Information and Communications Technologies; Influence of Technology; Informatics; Information Science; Instructional Design; Interviews; K-12; Learning Experience; Learning Processes; Learning Strategies; Machine learning; Mapping; Networks; Neural networks; Performance Based Assessment; Problem solving; State of the Art Reviews; Student Evaluation; Students; Teaching; Teaching Methods; Technology Education; Test Construction; Test Items; Thinking Skills; assessment; computing education; machine learning; K12
• ISSN: 1648-5831; 2335-8971
• DOI: 10.15388/infedu.2023.11

Although Machine Learning (ML) has already become part of our daily lives, few are familiar with this technology. Thus, in order to help students to understand ML, its potential, and limitations and to empower them to become creators of intelligent solutions, diverse courses for teaching ML in K-12 have emerged. Yet, a question less considered is how to assess the learning of ML. Therefore, we performed a systematic mapping identifying 27 instructional units, which also present a quantitative assessment of the students’ learning. The simplest assessments range from quizzes to performance-based assessments assessing the learning of basic ML concepts, approaches, and in some cases ethical issues and the impact of ML on lower cognitive levels. Feedback is mostly limited to the indication of the correctness of the answers and only a few assessments are automated. These results indicate a need for more rigorous and comprehensive research in this area.