AN ASSESSMENT OF NOVICE AND EXPERT USERS’ DECISION-MAKING STRATEGIES DURING VISUAL TRADE SPACE EXPLORATION

Open Access
- Author:
- Wolf, David Richard
- Graduate Program:
- Mechanical Engineering
- Degree:
- Master of Engineering
- Document Type:
- Master Thesis
- Date of Defense:
- None
- Committee Members:
- Timothy William Simpson, Thesis Advisor/Co-Advisor
Timothy William Simpson, Thesis Advisor/Co-Advisor - Keywords:
- Multi-dimensional data visualization
trade space exploration
human-computer interaction
design optimization - Abstract:
- Thanks to advances in computing power and speed, designers can now generate a wealth of data on demand to support engineering design and decision-making. Unfortunately, while the ability to generate and store new data continues to grow, methods and tools to support data exploration have evolved at a much slower pace. Moreover, current methods and tools are often ill-equipped at accommodating evolving knowledge sources and expert-driven data exploration that is being enabled by computational thinking. This thesis contributes to ongoing research that seeks to transform decades-old decision-making paradigms to more effectively convert data into knowledge ultimately leading to better decisions. Specifically, this thesis addresses decision-making within the area of trade space exploration by conducting human-computer interaction experiments using multi-dimensional data visualization software created at The Pennsylvania State University. In this thesis, the goals are to: (1) evaluate the current performance of novice decision-makers, (2) develop novice user training protocols by evaluating expert decision-maker problem solving methodology, (3) evaluate the ability of these training protocols to support efficient and effective trade space exploration for novice decision-makers, and (4) provide a foundation for additional training protocols for problems with varying tradeoffs and complexity. The results suggest that, without proper training, novices are ineffective at using multi-dimensional data visualization and visual steering tools to solve a design problem. The training protocols developed in this analysis were effectively able to teach the novices valuable design decision-making strategies. This was demonstrated, through controlled experiments, to provide substantial improvement in their average performance when using trade space exploration to solve a complex engineering design problem. The training protocols also successfully encourage the novices to utilize visualization and visual steering tools that were previously misused or ignored.