The Effect of a Visualization-Based Learning Environment on Student Expertise Development: A Quasi-Experimental Study

• In Event: Innovative Learning Design in an Information and Communications Technology-Rich Environment: The Ongoing Story of Shanghai

Abstract

Objectives: This study investigates the effectiveness of a visualization-based learning environment to help students construct well-organized domain knowledge and develop problem-solving expertise.
Theoretical framework: Research on expertise development has indicated that problem-solving and knowledge-construction processes should be highly integrated and reciprocally reinforced during learning (Ericsson, 2009; Kinchin et al., 2008). Visualization-based cognitive tools (Jonassen, 2011; Lajoie, 2009) can be used to externalize these two complex cognitive processes, build connections between them, and facilitate learning through problem solving.
Methods: This study adopted a quasi-experimental approach and compared the effects of two problem-solving learning environments on students’ problem-solving and knowledge construction performance. One environment (treatment condition) included a visualization-based cognitive tool, called dual-mapping tool (DMT), and the other environment (control condition) did not include DMT. The dual mapping tool includes concept mapping to represent the knowledge-construction process and argument mapping to represent the problem-solving process, whereas the connections between these two maps indicate the interactions between these two processes. The study is conducted in a medical problem-solving domain; fifty medical students from a teaching hospital in Shanghai are evenly assigned into two groups for learning through solving three authentic clinical problems prepared in the designed e-learning systems.
Data sources: Collected data include pretest and posttest of domain-specific competency as well as students’ online learning products. Two domain experts help assess online learning products based on the predefined rubrics. Both quantitative and qualitative methods are adopted in this study. Descriptive and inferential statistics are used to analyze the pretest and posttest results and scores of online learning products. Content analysis is used to analyze the online learning data.
Preliminary findings: The hypothesis of this study is that learning through a visualization-based problem-solving learning environment can achieve better expertise performance compared with through non-visualization-supported problem-solving learning environment. The study is on-going. Thirty participants have completed the online learning. The preliminary findings suggest that the experimental group outperform the control group in terms of online learning performance (i.e., clinical problem solving solution and medical knowledge structure). More samples are required; content analysis of students’ online learning process may answer how the aforementioned two processes interact during student learning.

Authors

• Bian Wu, East China Normal University

• Huiying Cai, Jiangnan University