All game, course, and data collection/analysis content for Rowan's Sophomore Engineering project "The Rubber Project". This project was initially developed by myself (Dr. Cayla Ritz) and a team of faculty members at the university (Dr. Cheryl Bodnar, Dr. Melissa Montalbo-Lomboy) to encourage students to engage meaningfully with a social problem in the context of a design challenge.
This project is run as part of Rowan Univeristy's sophomore engineering clinic 1 (SEC1) curriculum. SEC1 is an interdisciplinary engineering core course focused on teaching engineering students fundemental principles of engineering, such as research design, parametric design, engineering design cycle, engineering ethics, techincal writing, and teamwork. Paired with the techincal engineering lab portion, SEC1 focuses heavily on student's communication ability, requiring students to hand in a series of reports and literature reviews to better understand the research and dissemination process.
There are two modules to this project: 1) materials testing and 2) design-build-test of an autonomous robotic vehicle. In the first module, students complete a series of materials testing on various elastomers and plastics to determine the material properties. The tested materials included student-made silicone rubber, shore 95A thermoplastic polyurethane (TPU), and polylactic acid (PLA). After completing the materials testing, students were asked to determine the yield strength, Young's modulus, and stress/strain behavior of the material. The results of this test help inform students on some of their design choices for the second module. The results of this experimental activity were then reported in a scientific IMRaD (Introduction, Methods, Results and Discussion) style report written by student teams (3-4 students).
The second module was the design-build-test part of this project. Students were told they were competing for a contract with a company called "Meals on Mini Wheels". The company was interested in finding a group of talented engineering students to design a autonomous food delivery robot for Rowan's campus; the winning team would get the contract. The product would deliver meals to hungry students on campus wherever they were at any time of day, helping to combat the social issue of food insecurity among the college student population. Student teams were provided with the chassis shell and were tasked with designing 1) all terrain tires that could replace a formal suspension system, and 2) the circuitry and logic for autonomous drop off. To complete the first deliverable, students completed a parametric design cycle for the vehicle’s tires. Students were encouraged to think about how the treads of the tires could contribute to the vehicles’ overall ride dynamics. Groups could choose to make their tires out of any of the tested materials from the previous module of the project (silicone, TPU, or PLA). The designs were then tested for performance using an accelerometer to measure roll (rotation about x), pitch (rotation about y), and ride (acceleration along z).
Concurrently, student groups were working on the autonomous driving circuit for the vehicle. Student teams were able to “purchase” up to 3 sensors for their car. Available to them were sonar sensors and infrared (IR) sensors. Sonar sensors detect the distance that another object is from the sensor by sending out a sonic signal and reading the total time that it takes for that signal to be reflected off the object and back to the sensor. IR sensors detect changes in light in a greyscale, meaning that this sensor returns a range of values which translate into colors ranging from pure black to pure white.
Alternate reality games (ARGs) allow each player to have their own unique experience with the game because their choices and behaviors change their gameplay. Students engaging with this ARG were working on the same project, but ultimately their choices and experience is what progressed the gameplay narrative. One important aspect of the project was budget tracking. Ultimately, the company would select the design that had the best cost-to-performance ratio. This competitive component is where the “gamified” aspect of the project comes in. On the final day of class, students competed in a series of obstacle courses to score points based on their design. The first test was a terrain course where teams had to complete the course with their tire design without veering off and without losing their payload. Teams were able to earn up to ten points for completing this course and meeting all the criteria.
The second test was the autonomous driving component of the vehicle’s design. Student cars needed to follow a black line and land in the center of a target, also known as the “delivery zone”. The vehicles were scored based on how close to the center of the target they were able to park their vehicle. In this course, vehicles needed to be able to travel completely independently, meaning that once the vehicle was placed on the ground, the team members could no longer touch the car. As previously mentioned, students could use sensors to measure different parameters, such as the distance away from obstacles like walls or the color of the floor below the vehicle. These sensors cost money (in-game currency, not real money) and contributed to the overall cost of the vehicle, impacting their cost-to-performance ratio.
Students competed for the best cost-to-performance ratio by completing the aforementioned set of obstacle courses. Students had two opportunities to try the course and the highest of the two earned scores was taken into consideration for their total score. Students were encouraged to make tweaks between attempts but were not allowed to make major adjustments to the vehicle. The final scores of both obstacle courses were added together and the total cost of the student team’s vehicle was then divided by this value to obtain the final cost-to-performance ratio.
There were certain events in the game that the students could choose to participate in which could influence student team’s spending during the ARG. As a part of the game, there were three fictional stakeholders which prompted students to make judgments which would impact their project. These judgments were designed to look like emails from each of the stakeholders. The students would then choose a response and write an email back to the stakeholder explaining their choice. Some of the judgments had requirements that students needed to fulfill should they have chosen a certain option during the judgments. The judgments can be found as exported Qualtrics survey questions here (LINK).
To collect data in this study, a series of surveys were distributed to students in the six participating sections of Sophomore Engineering Clinic I. In this study, data was collected through two pre-surveys, six judgments distributed throughout the semester, and one post-survey
The first portion of this study involved administering two surveys on the first day of class through Qualtrics, a web-based survey platform (Pre-surveys 1 & 2). Pre-survey 1 had the students respond to six decontextualized hypothetical situations where they were asked to make a judgment. Each situation had two options for the student to choose between. Once the students chose their response, they were then asked to qualitatively explain their rationale for choosing the selected option. Each of the options were provisionally coded to reflect one of the six criteria from the conceptual framework. This framework, though initially applied to the process safety context, was built on literature from a variety of industries.
Pre-survey 2 asked students to rank six criteria from the conceptual framework in terms of what they believed was most important to them when making judgments in public welfare contexts. The survey also measured students’ perceptions of engineers’ professional responsibility using the Engineering Professional Responsibility Assessment (EPRA) survey instrument. The EPRA “measures students’ social responsibility attitudes and operationalizes the professional social responsibility development model, which describes the development of personal and professional social responsibility in engineers” (Canney & Bielefeldt, 2016, p. 1). This survey instrument was intended to be used to assess curricular interventions which are specifically focused on issues of engineering social responsibility. I utilized the Likert scale items presented in the survey as these items have shown that they have validity evidence in the three measured realms (personal social awareness, professional development, professional connectedness) and eight measured dimensions (awareness, ability, connectedness, base skills, professional ability, analyze, professional connectedness, costs-benefits) (Canney & Bielefeldt, 2015, 2016). This survey was administered on the first day of the course and again at the end of the semester (as the “Post-survey”) to determine how these measures changed over the course of the semester.
The second portion of the study involved students making judgments related to the in-class project and served as the basis for the game. Students’ simulated behaviors were collected through an alternate reality game (ARG). The ARG presents contextualized and non-hypothetical versions of the judgments that students saw in Pre-survey 1. The alignment between the judgments presented in the pre-survey and during the ARG helped to link students’ espoused beliefs to their simulated behaviors to understand both quantitatively and qualitatively how their beliefs and behaviors did or did not align. Following the completion of data collection for each survey, the data was exported, and identifying information was replaced with anonymized numerical identifiers to protect participants.
Included in the data analysis folders for this repo is some information on how to clean the data collected from the various surveys and judgments, and how to complete median imputation to estimate group cirtieria prioritization. By updating the excel file (medImp.xlsx) with the data collected, users can then run the MedianImputationOutput.m file to automatically perform the median imputation process on the collected data. This MATLAB file will give the user both the ordinal rankings of the six studied criteria and the median imputated estimates for the frequency of which each criterion was selected.