Reducing Cognitive Load through the Worked Example Effect within a Serious Game Environment

Reference Format:  B. Spieler, N. Pfaff, and W. Slany, 2020. Reducing Cognitive Load through the Worked Example Effect within a Serious Game Environment. Proceedings of the 6th International Conference of the Immersive Learning Research Network (iLRN). June 21–25, 2020, Online.

Bernadette Spieler1, Naomi Pfaff2, and Wolfgang Slany2
1 University of Hildesheim, Institute of Mathematics and Applied Informatics,
2 University of Graz, Institue of Software Technology, Austria

Abstract—Novices often struggle to represent problems mentally; the unfamiliar process can exhaust their cognitive resources, creating frustration that deters them from learning. By improving novices’ mental representation of problems, worked
examples improve both problem-solving skills and transfer performance. Programming requires both skills. In programming, it is not sufficient to simply understand how Stackoverflow examples work; programmers have to be able to adapt the principles and apply them to their own programs. This paper shows evidence in support of the theory that worked examples are the most efficient mode of instruction for novices. In the present study, 42 students were asked to solve the tutorial The MagicWord, a game especially for girls created with the Catrobat programming environment. While the experimental group was presented with a series of worked examples of code, the control groups were instructed through theoretical text examples. The final task was a transfer question. While the average score was not significantly better in the worked example condition, the fact that participants in this experimental group finished significantly faster than the control group suggests that their overall performance was better than that of their counterparts.

Index Terms—programming, worked examples, cognitive load, games for education, problem-solving, gender

Conference: Immersive Learning Research Network. Proceedings of 6th International Conference, iLRN 2020, Online, June 21-25, 2020. Immersive Learning Research Network. ISBN 978-1-7348995-0-4