Exploration of the Timing of Introduction of Design Heuristic Cards to Early Design Brainstorming Sessions by Interdisciplinary Student Teams

This research focuses on improving the outcome of idea generation sessions of interdisciplinary student teams working in the early design stages of a product or solution by measuring the effect of incorporating Design Heuristics Cards at different points of ideation sessions that adhere to the brainstorming guidelines. Using the design thinking methodology, an open-ended challenge was given to the participating teams for a Brainstorming exercise divided into a fifteen-minute individual segment followed by a thirty-minute team ideation segment. Three experimental treatments were designed where Design Heuristics Cards were introduced at different points of the ideation exercise: the start of the individual ideation segment, the start of the team ideation segment, or the second half of the team ideation segment. A fourth control treatment did not introduce the cards at any point but used the Brainstorming guidelines throughout. The metrics observed were Fluency, Novelty, Feasibility, and Market Fit of the ideas generated by the students. Eighty-four students participated in the experiment, with 58.3% being from majors in the College of Engineering, 28.6% from majors in the College of Business Administration, 7.1% from majors in the College of Arts and Sciences, and 6.0% from majors in the College of Agriculture. No significant difference was found among the experimental treatments; however the results are not considered final due to the explorative nature of the study. Recommendations are made on future work and possible improvements to the experiment.

A Power Analysis to Determine Appropriate Sample Size for the Study of Student Design-Team Effectiveness

Forming collaborative teams is a critical first step in team-project-based design courses as team composition directly affects not only teamwork processes and outcomes but also teamwork skills and experience. While various approaches have been used to form teams, the best methodology has not been found due to a lack of understanding of how team compositions impact team performance and teamwork learning. We need to establish a team effectiveness model for student design teams that describes relationships between team characteristics and team performance or teamwork learning. One of many challenges in such an effort is to estimate an appropriate sample size to achieve statistically significant results before starting data collection. In this paper, we demonstrate a power analysis for determining an appropriate sample size, i.e., the number of student teams, before we study the effectiveness of student design-teams. We first present a hypothesized team effectiveness model for student design teams that shows possible relationships among team factors. We then illustrate a statistical analysis procedure for studying the team effectiveness model using structural equation modeling (SEM) or path analysis. We finally demonstrate a power analysis of SEM for determining the appropriate sample size for studying the team effectiveness model.

Digitizing Dissection: A Case Study on Augmented Reality and Animation in Engineering Education

Advancing virtual education through technology is an important step for engineering education. This has been made evident by the educational difficulties associated with the 2020 Covid-19 pandemic. Maintaining educational standards while using virtual learning is something possibly solved through researching new educational technologies. A potential technology that can enhance virtual education is Augmented Reality, since it can show information that would otherwise not be easily experienced or obtained. Traditional learning tools fail to offer the ability to control objects and explore numerous perspectives the way augmented reality can. Augmented reality can be even further enhanced through the addition of animation. Animation could add the ability to see motion, increasing overall understanding as well as increasing the motivation to learn. When motion is not visualized, it must be perceived, which can increase cognitive load and cause the limitations of working memory to be met. Reaching the limits of working memory has been shown to negatively affect learning. Therefore, the purpose of this study was to identify the impact of digitizing product dissection on engineering student learning and cognitive load. Specifically, we sought to identify the impact of Augmented Reality and Animations through a full factorial experiment with 61 engineering students. The results of the study show that the virtual condition with animation exhibited increased effectiveness as a learning tool. It also showed that augmented reality is not significantly different than a virtual environment in the context of product dissection. The results of this study are used to explore future uses of augmented reality and animation in education, as well as lay the groundwork for future work to further explore these technologies.

Who, What, and When? Exploring Student Focus in the Capstone Design Experience

Product development is a key component of engineering education taught at a number of universities through their capstone design course. This course provides students with an opportunity to apply their newly obtained knowledge in engineering to design, build, and test working prototypes. This educational approach also encourages students to place additional attention on time and group management. As students walk through the design process, their focus fluctuates between group organization, product development, and course deliverables. This paper observes this variation in focus to extract key insights related to who is focusing on what and when. Data was collected in the form of individual project journals for each student and these provide a detailed look into the design activities throughout the semester allowing for a focus mapping from week to week. The focus of each student is quantified by a topic distribution of each student’s weekly journal entries, automatically extracted using Latent Dirichlet Allocation. Our results place emphasis on the topic identification accuracy and interpretation, before identifying trends found that separate high performing students and groups from those with poor performances. It was found that efficient time management focusing on the required course deliverables, and group cohesion led to the most impactful performance variations. Using this knowledge, we identify future directions supporting the pedagogy for capstone projects.

Quantification of Students’ Learning Through Reflection on Doing Based on Text Similarity

In this paper we address the following question: How can instructors leverage assessment instruments in design, build, and test courses to simultaneously improve student outcomes and assess student learning well enough to improve courses for future students? A learning statement is a structured text-based construct for students to record what they learned by reflecting on authentic immersive experiences in a semester-long engineering design course. The immersive experiences include lectures, assignments, reviews, building, testing, and a post-analysis of an electro-mechanical device to address a given customer need. Over the past three years, in the School of Aerospace and Mechanical Engineering at the University of Oklahoma, Norman, we have collected almost 30,000 learning statements from almost 400 students. In the past few years, we have analyzed this data to improve our understanding of what students have learned by reflecting on doing and thence how we might improve the delivery of the course. In an earlier paper, we described a text mining framework to facilitate the analysis of a vast number of learning statements. Our focus, in the earlier paper, was on describing the functionalities (i.e., data cleaning, data management, text analysis, and visualization results) of the framework and demonstrating one of the text quantification methods — term frequency — using the learning statements. In this paper, we focus on demonstrating another text quantification method, namely, text similarity, to facilitate instructors’ gaining new insights from students’ learning statements. In the method of text similarity, we measure the cosine distance between two text vectors and is typically used to compare the semantic similarity between documents. In this paper, we compare the similarity between what students learned (embodied in learning statements) and what instructors expected the students to learn (embodied in the course booklet), thus providing evidence-based guidance to instructors on how to improve the delivery of AME4163 – Principles of Engineering Design.

Pilot Study: Investigating EEG Based Neuro-Responses of Engineers via a Modified Alternative Uses Task to Understand Creativity

Assessing creativity is not an easy task, but that has not stopped researchers from exploring it. Because creativity is essential to engineering disciplines, knowing how to enhance creative abilities through engineering education has been a topic of interest. In this paper, the event related potential (ERP) technique is used to study the neural responses of engineers via a modified alternative uses task (AUT). Though only a pilot study testing two participants, the preliminary results of this study indicate general neuro-responsiveness to novel or unusual stimuli. These findings also suggest that a scaled-up study along these lines would enable better understanding and modeling of neuroresponses of engineers and creative thinking, as well as contribute to the growing field of ERP research in the field of engineering.

Quantifying Individuals’ Theory-Based Knowledge Using Probabilistic Causal Graphs: A Bayesian Hierarchical Approach

Extracting an individual’s knowledge structure is a challenging task as it requires formalization of many concepts and their interrelationships. While there has been significant research on how to represent knowledge to support computational design tasks, there is limited understanding of the knowledge structures of human designers. This understanding is necessary for comprehension of cognitive tasks such as decision making and reasoning, and for improving educational programs. In this paper, we focus on quantifying theory-based causal knowledge, which is a specific type of knowledge held by human designers. We develop a probabilistic graph-based model for representing individuals’ concept-specific causal knowledge for a given theory. We propose a methodology based on probabilistic directed acyclic graphs (DAGs) that uses logistic likelihood function for calculating the probability of a correct response. The approach involves a set of questions for gathering responses from 205 engineering students, and a hierarchical Bayesian approach for inferring individuals’ DAGs from the observed responses. We compare the proposed model to a baseline three-parameter logistic (3PL) model from the item response theory. The results suggest that the graph-based logistic model can estimate individual students’ knowledge graphs. Comparisons with the 3PL model indicate that knowledge assessment is more accurate when quantifying knowledge at the level of causal relations than quantifying it using a scalar ability parameter. The proposed model allows identification of parts of the curriculum that a student struggles with and parts they have already mastered which is essential for remediation.

A Methodological Framework for Making the Transition From Traditional Innovation Teaching Towards Serious Games

Serious games (SGs) are motivational and practical pedagogical tools that have been widely used in design education. SGs seem to be an efficient way to give instructions on innovation processes (IPs), offering unique and attractive environments to support situated learning. While there has been much interest in SGs of the IPs type, there is very little research about the design framework to reduce the complexity and time consumption of their design process. This paper presents the preliminary results of our ongoing study: a design framework adapted to innovation SGs. The framework integrates eight general design frameworks/models/methodologies for SGs. Besides, it introduces a new stage “analysis of traditional teaching experience,” which conducive to the early phases of the design. We use a case study to prove the value of this stage. First, it aids designers in defining the teaching objectives of innovation SGs, that is, choosing required competencies from innovation competency frameworks. More importantly, it helps identify game mechanics that may contribute to the realization of teaching objectives. This stage should support designers successfully making the transition from traditional innovation teaching towards SGs.

Investigating the Relationship Between Digital Prototyping Tool Use and Student Designers’ Perceptions of Design Processes

With the rise of the “maker” culture, the prevalence of affordable and rapid “maker” tools has increased dramatically, and the mass proliferation of 3D printers has become a staple of engineering design and engineering design education. The increased use of digital prototyping tools, such as Additive Manufacturing (AM) technologies, is fundamentally transforming the way students and educators approach engineering design courses and hands-on projects. This work investigates the effect of AM prototyping efforts on student perceptions of design processes through an in-situ study conducted across two project-based design courses. Results suggest that students’ perceptions of prototype value, time spent prototyping, and the development of designer knowledge is significantly affected due to AM use during prototyping activities.

Towards an Understanding of Semantic Memory During Idea Generation

Research in new product design still lacks an understanding of how the types of information used by designers can lead to more successful designs and what cognitive components are involved in the process of generating new ideas. Some theories have arisen that focus on memory usage that could have an impact in idea generation early on in the design process. This framework forms the basis of the current study, focused on identifying the underlying cognitive processes that are active during the design process. To accomplish this, undergraduate students were recruited from the University of Nebraska-Omaha. During the study, participants were presented a design problem, given information pieces that corresponded to the Information Archetypes Framework, and asked to generate ideas for a solution. Students were then asked to recall the information pieces from memory. Participants’ data were analyzed using Latent Semantic Analysis in order to assess the similarities between generated ideas, recall, and information pieces. Results from this were assessed for relationships using Spearman correlations and simple regression. This study was able to demonstrate memory usage within the early design process.