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

Authors

  • Bernadette Spieler Institute of Mathematics and Applied Informatics; University of Hildesheim Hildesheim, Germany
  • Naomi Pfaff Institute of Software Technology; Graz University of Technology Graz, Austria
  • Wolfgang Slany Institute of Software Technology Graz; University of Technology Graz, Austria

DOI:

https://doi.org/10.56198/

Keywords:

Programming, Worked examples, Cognitive load, Games for education, Problem-solving, Gender

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 Magic Word, 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.

Published

12-11-2025

Conference Proceedings Volume

Section

Conference Proceeedings

How to Cite

Reducing Cognitive Load through the Worked Example Effect within a Serious Game Environment. (2025). Immersive Learning Research - Academic, 1(1), 1-8. https://doi.org/10.56198/

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