The Transparency Paradox: Computational Simulations as Learning Tools for Engineering Graduate Education

Computational simulations have become a critical part of computational science, which is being described as the third leg in this century’s methodologies of science. Computational simulations have also become a critical element of learning experiences as they can provide engineering students with the ability to do things that they could not do in the real world. This study explores engineering graduate students’ perceptions related to aspects associated to the transparency of the simulation tools. The results of this study show that most of the students interviewed found using computational simulations as useful for their learning. However, a transparency paradox was identified. A proposed solution to the transparency paradox may be the implementation of scaffolds together with three different levels of transparency. Introduction Computational simulations have become a critical part of computational science, which is being described as the third leg in this century’s methodologies of science (Sabelli, 2006). Computational simulations have also become a critical element of learning experiences (Magana, Brophy, & Bodner, 2008). Furthermore, computer simulation tools can provide engineering students with the ability to do things that they could not do in the real world (Winn, 2002). This study builds upon the body of knowledge of the use of computer and computational simulation tools for learning. In particular, this study focuses on issues of transparency that may impact the uses of computational simulations as learning tools. Background Cannon-Bowers and Bowers (2007, p. 318) defined simulations as “a working representation of reality; used in training to represent devices and processes and may be low or high in terms of physical or functional fidelity (p. 318). ” Alessi (2002, p. 177) proposed that simulations are “any program which incorporates an interactive model (one which can be repeatedly changed and rerun) and where the learning objective is for students to understand that model, whether through discovery, experimentation, demonstration, or other methods (p. 177). ” Based on these definitions, we define computational simulations as working representation(s) of reality; used in training, research, and education to represent physical phenomena, devices, and/or processes through mathematical models and numerical solution techniques using computers. These computational simulations are used as tools to analyze and solve scientific and engineering problems. Alessi (2002) distinguished between “building” simulations and “using” simulations. This distinction was well-described by Clariana and Strobel (2007) in which building simulations refers to computer software modeling tools designed to build and run dynamic models, while using simulations are computer software tools designed under others’ own understanding of a model that allow users to manipulate its processes or variables and observe the output results (R. B. Clariana, 1989; Reigeluth & Schwartz, 1989). Alessi (2002) made clear that not all educational simulations can be divided cleanly into using or building simulations; instead, they form a continuum. This study explores engineering graduate students’ perceptions and experiences of computational simulations in the context of five different homework assignments involving either using or building simulations. In particular, this study focuses on students’ perceptions related to aspects associated to the transparency of the simulation tools. Tanimoto (2004) defined transparency as a property of some systems where the inner workings and the design of the system are visible to users (p.1). This study reports a transparency paradox identified through students’ experiences and perceptions and proposes potential pedagogical approaches to overcome it. The guiding research question for this study is: What are engineering students’ perceptions and experiences using computational simulations as learning tools? Method Cross-case analysis design was employed as the method of inquiry. According to Eisenhardt (1989), the case study is a research strategy focused on understanding the dynamics within single settings. In this case, the setting focused on engineering students’ experiences using computational simulations. Cross-case analysis then followed withincase analysis. These analyses resulted in common themes and patterns among students. Data was collected through interviews with students from three different engineering courses offered in the Fall of 2008 (see Table 1). Classroom observation was also conducted throughout the entire semester. The interviews focused on students’ perceptions of using simulation tools in a particular homework assignment. For example we asked them: What was your experience of using the simulation tool as part of your homework assignment? What do you think was the purpose of the homework activity using the simulation tool? What the professor wanted you to learn? Which portion of the assignment did you find the most challenging? How did you overcome those challenges? When you were solving the homework assignment, how confident were you with the required knowledge to solve it? After completing the activity, what was new for you that you did not know before? Why are you taking this course? The interviews lasted approximately one hour and were conducted at different points during the semester. In particular, for Course 1, we interviewed six students after the first homework assignment and another set of five students towards the end of the semester. For Course 2, five students were interviewed after the mid-term exam and four more towards the end of the semester. For Course 3, five students were interviewed towards the end of the semester. Each student was interviewed one single time and even though they may have done two homework assignments, the interview was focused on one particular assignment. Therefore, these students described their experiences of using computational simulations as learning tools in the context of five different homework assignments where they primarily used, built or used and built simulation tools. In total, we interviewed 25 students who were randomly selected. Classroom observation was also conducted throughout the entire semester for the three different courses. The purpose for these observations was to identify how instructors incorporated the simulation tools as part of their learning activities. These for building simulations students also used it after having built them. However in most of the cases the primary goal was focused on learning the computational techniques to build those simulations. observations were useful not only to identify instructors’ pedagogical practices with simulation tools, but also they allowed us to establish rapport with students and gain a general understanding of the subject matter. Table 1. Participants Per Course and Per Homework Assignment Courses Course 1 Course 2 Course 3 Homework Assignments HW_A Using HW_B Using HW_C Building HW_D Building HW_E Building-Using Students Libo_A John_A Isison_A Arash_A Valerie_A Joe_A Howard_B Jack_B Jonathan_B Allison_B Maria_B Jake_C Drew_C Jim_C Uday_C Kyle_C Lawrence_D Rajiv_D Melanie_D Robert_D Dimitris_E Tom_E Connor_E Maurice_E Steve_E The analysis started with open coding followed by axial coding. While in open coding we conducted the first level of abstraction in conceptualizing major ideas, in axial coding we reassembled the data so explanations and relationships of the data were done. Major advantages and disadvantages of using computational simulations as learning tools were identified. Themes of students’ experiences with simulation tools were grouped together. Next, individual cases were compared to general patterns across cases. When a pattern from one data source was corroborated by the evidence from another, the finding became stronger and better grounded in the data (Merriam, 1988). nanoHUB Simulation Tools The Network for Computational Nanotechnology developed an infrastructure network called nanoHUB to help transform nanoscience to nanotechnology through online simulation and training. The nanoHUB website provides research-quality simulations that experts in nanoscience commonly use to build knowledge in their field (see example Figure 1). NanoHUB leverages an advanced cyber-infrastructure and middleware tools to provide seamless access to these simulations. Key characteristics of the nanoHUB simulation tools that make them good resources for incorporation into classroom environments are: (a) they were produced by researchers in the NCN focus areas, (b) they are easily accessed online from a web browser powered by a highly sophisticated architecture that taps into national grid resources, and (c) they provide a consistent interactive graphical user interface known as Rappture, which makes esoteric computational models approachable to non-experts. Rappture is a toolkit that allows the incorporation of a friendly graphical user interface with the simulation tools in the nanoHUB (McLennan, 2005). Figure 1. Sample nanoHUB Simulation Tools Results Overview Homework Assignments By means of classroom observations we were able to identify two major focuses or themes for incorporating simulation tools into their instructional activities; while some instructors focused more on conceptual understanding and engineering skills, others emphasized the computational techniques. Therefore, two general methods for incorporating simulation tools into higher education courses were identified; simulations used to predict system performance relative to a design task (Course 1), simulations built as part of a modeling task to predict model performance relative to observed phenomena (Course 2), and a combination of these two (Course 3). The main instructional approach instructors followed was to first introduce in class the physical principles defining the behavior of a device, material, or phenom