Abstract: We present results of a preliminary study that applies cognitive load theory (CLT) to investigate how students with different amounts of prior experience learn algorithms. We test the following assertions from the CLT framework: The high CL of algorithm learning comes from intrinsic CL – meaning the complexity of the information being processed. There is also high germane CL – that induced by the instructional intervention – in tasks designed to assess the learned knowledge. Lowering either of these two CLs results in measurable learning gains. Lowering the complexity of incremental steps is the key determinant of success. We investigated the extent to which students’ previous knowledge and experience influence the process of learning algorithms. This also involved testing whether an algorithm visualization tool (Map-based Educational Tools for Algorithm Learning, METAL) improves the understanding of graph algorithms. Our study adapted an existing survey instrument developed by Klepsch, et al., to algorithmic thinking tasks and used it as a tool to measure CL components. We explored and measured three types of CL for breadth-first and depth-first graph traversal algorithms, and among three groups of participants, non-Computer Science students, beginning CS students, and more advanced CS students. Results include: (i) Among different types of CL, germane load was the most substantial type for all groups. Students with more background in CS showed lower levels of all types of CL. (ii) The three groups showed similar relative effects of intrinsic, germane, and extraneous CL. We discuss future research and limitations of the study.
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Recommended Citation: Fathi, R., Teresco, J., Regan, K., (2023). Measuring Learners’ Cognitive Load When Engaged with an Algorithm Visualization Tool. Journal of Information Systems Applied Research16(3) pp 58-67. http://JISAR.org/2023-3/ ISSN : 1946 - 1836. A preliminary version appears in The Proceedings of EDSIGCON 2022