Transfer of Learning Between Solid Modelers: An Investigation of Icon Recognition

Selecting the right solid modeling software is a complex, multi-criteria decision making problem. There are many issues a decision-maker needs to take into account, such as ease of learning, educational materials built into the software, learning curve issues, performance of the software for different solid modeling functions, operations and utilities, and cost. Beyond selecting the right software, the decision-maker should also be concerned about (1) conceptual learning of the solid modeling topics while “the right software” is being used, and (2) transfer of conceptual learning between solid modelers. This is because a sound conceptual learning might increase the probability of learning another solid modeling software in less time. Accordingly this paper investigates the impact of icon recognition as an aid to transfer conceptual learning between solid modelers. The investigation includes a review of the literature on icon design and usage as it relates to solid modeling, in addition to an experiment in which the icon recognition correctness and duration for over 20 operation icons were compared across two modelers. The results shed light into the impact of icon designs on the transfer of learning between solid modelers using the correct recognition counts as the transfer measure. 1.0 Introduction Initially solid modeling applications concentrated on replacing engineering drawings with unambiguous computer models to support automated engineering tasks. Nowadays, solid modeling is seen as an integral tool for product development and engineering because of its functionality as a computer-aid in design and documentation 1 . Therefore, due to its importance in engineering design practice, integrating a solid modeling software (solid modeler) to design teaching is necessary 2 . In industry, the trend in adopting solid modeling software is also apparent. A recent review of design software users’ survey showed that only 31% of the design practitioners are using 2D CAD systems. The rest are either only using 3D CAD (5%), implementing a hybrid usage of 2D/3D CAD systems (38%), or mainly using 2D CAD, but evaluating 3D CAD (26%) 3 . However, beyond only including a solid modeler, instilling a conceptual understanding of solid modeling basics in students is paramount. This is because it is expected that what students learn in the classroom may not be the solid modeler their future employers use. Therefore, the transfer of learning from one solid modeling software to another has become a concern. This is also highlighted by the fact that there is an increased mobility of professionals among companies, and there are now many cost-effective modeling software packages available 4 . Due to these, P ge 10353.1 Proceedings of the 2005 American Society for Engineering Education Annual Conference & Exposition Copyright ©2005, American Society for Engineering Education studying the transfer of learning is important. The information regarding how conceptual learning in solid modeling transfers from one software to another might provide opportunities for better training designs with potential gains in effectiveness and efficiency. The remaining sections of the paper includes a review of the related literature, design of an experiment conducted to discern the transfer of learning through icon recognition, results and conclusion along with recommendations for future research. 2.0 Literature Review For usability testing, focusing on user-based studies has been advocated in place of expert-based ones because of their increased effectiveness in discerning how everyday subjects (such as students) will respond. As such, Hans Van Der Meij 5 emphasizes how information found in textfocused and expert-judgment-focused methods provide no indication as to how an actual reader will respond. For example, in a usability testing experts predicted less than half of the problems users experienced when reading a VCR manual 4 . Although this testing focused on written text rather than a computer interface, it is expected that a similar situation might be true for the usability of a graphical user interface (GUI) and its icons. Expert-focused studies exist, however. For example, Huang et al. 6 collected a set of 50 icon design criteria from various sources and had two experienced professional graphics designers analyze the guidelines to construct a 19-item questionnaire for their study of “factors affecting the design of computer icons”. They then had 43 computer GUI designers complete the questionnaire. Their study proposes that design experience may affect how people judge icons, and therefore it may be undesirable to collect icon design criteria information from subjects with no icon design experience. However, what their study fails to address is the inherent importance of the preferences of these inexperienced subjects. After all, it is for these inexperienced subjects that the icons are ultimately designed, not the computer GUI designers. Wouldn’t it then be more desirable to in fact design the icons based on the preferences of the subjects that will actually be using them, rather than that of the GUI designers? This paper intends to illustrate how a user based study sheds light into the impact of icon designs on the transfer of learning between solid modelers. In general, the implementation of GUI as a means of communicating with the computer takes advantage of the human capability to recognize and process graphical images quickly. Accordingly, most solid modelers use it today. However, the growth of interfaces is a matter of concern for software developers, and might be a barrier in solid modeling education and in engineering practice 7 . This is because it is believed that the layout of GUI elements influences the way users can interpret these elements 8 . While the user’s correct mental model of the interface can help with their productivity, a false image of the interface might mislead them and limit their ability to work with the software effectively 9 . For example, a recent experimental study showed that, if an unknown icon A in software 1 looked like a well-known icon B in software 2, the users supposed that the icon A represented the same function as the icon B, even if both pieces of software were quite different 10 . Therefore, it is clear that differences in user mental models of a GUI are expected, and hence icon recognition can be one of the important factors in the transfer of solid modeling learning. P ge 10353.2 Proceedings of the 2005 American Society for Engineering Education Annual Conference & Exposition Copyright ©2005, American Society for Engineering Education Accordingly, various databases were searched for previous work on icon design and recognition, and its impact on the transfer of learning. Although there is research on visual icon design and usefulness within a GUI, there are no direct studies on the effect of icon designs and transfer of learning issues within the solid modeling domain. However, the research on icon design and icon usefulness can be transferred into the solid modeling area 11 . An extensive search in Compendex resulted in direct hits on keywords in connection with icons that included: design software, 3-D design, 2-D design, and solid modeling. Articles on topics such as interface evaluation based on human eye movement characteristics, usefulness of icons on the computer interface, and visual icon design proved to be related. For example, significant research has been done on the analysis of eye movement characteristics and the impact they have on display design. Using eye movement-based analysis can improve performance evaluations of GUI’s 12 . It is common knowledge that the specific grouping of information can lead to enhanced search efficiency. The spatial grouping and mere presence of function icons can greatly increase scanning speed as well as reduce the number of fixations 13 . GUIs with icons that are well organized according to functionality, as opposed to random grouping, resulted in shorter scanning paths and less fixations, ultimately giving way to higher search efficiency 12 . These findings show a direct correlation between user performance and function icon groupings. Along with the spatial grouping of icons, the quality of iconic representation can also have an impact on user performance on a GUI. Icons that are visually representative of their respective function are known to be identified considerably faster than icons that are visually arbitrary of their function 14 . User knowledge or experience also plays an important role in determining the ability to correctly identify iconic representations 15 . These findings are important factors to take into consideration when designing icons and are also potentially important when evaluating user performance and transfer of learning. However, earlier studies of transfer of learning between solid modelers did not focus on these issues. For example, Wiebe’s 4 experiment related to transfer of learning between Pro/ENGINEER and SolidWorks, and AutoCAD and SolidWorks focused on how successfully users can take higherlevel (semantic) task strategies developed using one solid modeler and apply them to a new one. This was considered to be an initial look at the transfer of learning issues between modelers because previous search indicated that many modelers include commands/tools that support the same higher level modeling strategies for modeling simple parts 16 . However, it was also stated that “...all of the packages have different interface elements, which create different syntaxes for achieving these higher level goals.” 4 . Accordingly, this study continues the work on understanding issues related to the transfer of learning by focusing on the GUI issues, specifically on the icon design and recognition, and transfer of icon recognition between modelers. 3.0 Experimental Design The experiment, which was conducted to shed light regarding t