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Abstract
The objective of this study was to investigate the impact of knowledge representations on problem-oriented learning in online learning environments. The study compared the impact of knowledge map representation with traditional hierarchical representation with regard to learning memory and problem-solving performance. Twenty-nine students participated in an experiment in which they studied online materials with the goal of solving two programming problems (simple and complex). It was found that participants who used the hierarchical representation read in the depth-first sequence, whereas participants who used the knowledge map representation read in a sequence reflecting the system running mechanism implied by the graphical representation. In addition, participants who used the knowledge map representation had better memory of the learning content, especially about relations between knowledge nodes. When solving the complex problem, participants who used the knowledge map representation made a deeper analysis of the problem and had better problem-solving performance. These results were not significant in the simple problem-solving task.
Additional information
Notes on contributors
Qin Gao
Qin Gao received her PhD from Tsinghua University, Beijing, China. She is currently an associate professor in the Department of Industrial Engineering at Tsinghua University. Her main research includes user-centered design, cognitive ergonomics, human–computer interaction, and decision making.
Dunxing Wang
Dunxing Wang is a PhD candidate in the Department of Industrial Engineering at Tsinghua University, Beijing, China. He received his bachelor’s degree from Northeastern University in 2012. He has been working with Professor Qin Gao since 2012.
Fan Gao
Fan Gao is an interaction designer of qyer.com. He received his bachelor’s degree from the Department of Industrial Engineering of Tsinghua University in 2006, and his master’s degree in 2009. He worked with Professor Qin Gao as a master’s student from 2006 to 2009.