Exploration of demographic variations is required to develop dynamic web platforms that cater to the varying preferences of diverse users. Hence, this study evaluated instructors’ demographic variations on a web-based platform for connecting with practitioners for student development. Both objective and subjective measures were adopted to investigate age- and gender-related differences in gaze behavior, task completion time, perceived cognitive load, perceived usability, and trust. Compared to male instructors, female instructors had higher fixation counts, longer task completion times, and statistically significant longer fixation duration. Female instructors gave higher usability and trust ratings but reported a higher cognitive workload. Compared to Generation Y instructors, Generation X instructors had longer fixation duration, higher fixation count, and statistically longer task completion time. Generation X instructors reported high cognitive load, lower usability, and trust ratings. The study also reveals demographic differences in parameters that instructors focused on while connecting with practitioners via a web platform.

In this work-in-progress, we explore the use of prompt engineering and image generation to elicit student understanding of different concepts discussed in structural engineering courses. In the context of image generation, prompt engineering refers to the process of providing a text or image prompt to a machine learning model to generate an image that meets certain criteria. The quality of the prompt can significantly impact the quality of the generated image. Hence, prompt engineering is an important part of the image generation process. By providing a well-crafted prompt that accurately describes the desired image, the generative model will more likely produce an image that meets the desired criteria. In the context of this study, students created prompts that were converted into images using Text-to-image Generative Models. When adequately prompted, these images are meant to visually describe their understanding of the concepts we have discussed during traditional lectures. The participants were presented with their own image as well as others' images to elicit both agreements or concerns at two levels: their understanding of the concepts and their understanding of the importance of prompting in the foreseeable future of AI-based systems. Hitherto, we have tried to use the generation of images using natural language for answering these research questions: i) for students, to which extent the crafting prompts may elicit thinking about the structural engineering phenomena? and ii) for instructors, to which extent these represent the level of understanding of the students on these topics?

The construction industry is a new avenue for big data and data science with sensors and cyber-physical systems deployed in the field. Construction students need to develop computational thinking skills to help make sense of this data, but existing data science environments designed with textual programming languages create a significant barrier to entry. To bridge this gap, we introduce Octave, an end-user programming environment designed to help non-expert programmers analyze spatiotemporal data (e.g., as gathered by a GPS sensor) in an interactive graphical user interface. To aid exploration and understanding, Octave's design incorporates a high degree of liveness, highlighting the interconnection between data, computation, and visualization. We share the underlying design principles behind Octave and details about the system design and implementation. To evaluate Octave, we conducted a usability study with students studying construction. The results show that non-programmer construction students were able to learn Octave easily and were able to effectively use it to solve domain-specific problems from construction education. The participants appreciated Octave's liveness and felt they could easily connect it to real-life problems in their field. Our work informs the design of future accessible end-user programming environments for data analysis targeting non-experts.

This research full paper presents research around the Engineering Projects in Community Service (EPICS) program that serves two key purposes to: 1) provide a structured approach for engineering students to engage in real-world, service-based projects and 2) provide technical support and expertise that may be critical to local and global community organizations. Hence, EPICS strives to offer a platform that fosters the collaboration of engineering students and communities. EPICS helps develop undergraduate students’ professional skills extending beyond the theoretical knowledge acquired in classrooms. EPICS has been a fixture in engineering education for over 15 years, with a strong focus on curricular and pedagogical interventions to help students gain professional skills. The purpose of this paper is to explore the perspectives of over 650 students who participated in EPICS at a U.S. university during the academic years of 2019/2020 and 2020/2021. We used natural language processing (NLP) to thematically analyze students’ responses to an open-ended survey administered at the end of their semester participating in the EPICS program. Students’ responses reflect their perspectives on the design process, teamwork, real-world experiences, and the challenges they face during the design process related to other people and the program. In our findings, students’ least favorite parts of EPICS were lectures and design reviews, while their favorite parts of EPICS were teamwork and engaging with community partners. Understanding the themes emerging from the data can help us better implement community-based educational initiatives and find ways to better engage students in community service-learning projects. Our research provides implications for practice and research.

This work in progress paper describes preliminary results of a methodology used at three different universities to explore students’ perceptions of engineering through drawings. One of the primary objectives of introductory and foundational engineering courses is to help students develop a sense of identity and belonging within the field of engineering, and understand basic engineering knowledge and skills. Hence, it is crucial to understand students’ preconceptions of the engineering discipline when they start their academic program. However, many students entering the program have narrow preconceptions or limited knowledge about the field. One challenge instructors face is how to facilitate students’ thinking about their own perceptions of engineering in a meaningful way. A typical activity to help the students understand their perceptions of the engineering discipline is to ask them, "What is engineering?" However, instructors have been frustrated by the lack of depth in students’ responses. This paper explores a different methodology to understand students’ perceptions of the engineering discipline by taking an arts-informed approach; instead of writing down their perceptions or talking with a peer, students are asked to draw a response to the question "What is engineering?" Data were collected and analyzed using an arts-based open-coding approach. Initial results provide a representation of students' preconceptions about the discipline in terms of human, technical, process-based, and holistic/global aspects, which provide further evidence that arts-based methods are effective in capturing student deep perceptions of the engineering discipline.

This full paper reports on students’ experiences after working on a first-year engineering design project with a real client. The instructors partnered with a Children's Museum in the local area, and students were tasked with developing prototypes of potential exhibits. The purpose of this paper is to present results on students’ perceptions of their experience working with a real client, developing a prototype, and having to interact with project stakeholders (e.g., children). The course design was based on problem-based learning (PBL) and data were collected from 169 first-year engineering students who anonymously filled out an exit survey. Responses were coded and emerging themes are presented. Natural processing language techniques were also used to analyze the open-ended responses.

In this Work-in-Progress Research paper we introduce longitudinal video reflection as a useful data collection approach for understanding students’ real-time experiences of educational environments. We use data collected across three study abroad programs in engineering, comparing longitudinal video reflection data to written journals and post-program interviews, to understand the benefits and challenges of using each of these data collection approaches. Based on this, we make recommendations for when longitudinal video reflection could be used for data collection in future studies within engineering education.

This Work-in-Progress research paper describes preliminary work on a research agenda for emotions in engineering education. Emotions play an important role for teaching and learning in engineering education, but research on the topic is scarce. To spur research in this area, the authors participate in an international collaboration that aims to map existing research, identify questions that are under-researched, and outline important questions for future research on emotions in engineering education. In this paper, we describe preliminary work that has been done in preparation of an international symposium during which a first draft of the research agenda on emotions in engineering education will be developed. At FIE 2020, we will present both this preparatory work and the agenda itself.

This Work-In-Progress research paper presents preliminary results and next steps of a study that aims to identify institutional data and resources that instructors find helpful in facilitating learning in large foundational engineering courses. The work is motivated by resource-driven compromises made in response to increasing engineering student populations. One such compromise is teaching some courses (usually foundational courses taken by students across multiple disciplines) in large sections, despite research suggesting that large class environments may correspond with unfavorable student learning experiences. Examples of courses often taught in large class environments are mathematics, physics, and mechanics. We are currently working with a cohort of instructors of foundational engineering courses as part of an NSF Institutional Transformation project. We have collected qualitative data through semi-structured interviews to explore the following research question: What data and/or resources do STEM faculty teaching large foundational classes for undergraduate engineering identify as being useful to enhance students' experiences and outcomes a) within the classes that they teach, and b) across the multiple large foundational engineering classes taken by students? Our inquiry and analysis are guided by Lattuca and Stark's Academic Plan Model. Preliminary analysis indicated that instructors would like more opportunities to interact and collaborate with instructors from other departments. These results will inform activities for our Large Foundational Courses Summit scheduled for Summer 2018 as part of the project.

Students from low social economic status (SES) groups remain underrepresented in higher education and particularly in STEM fields. From existing literature, we know some of the barriers in promoting STEM careers among people in low SES groups include a lack of role models, understanding or misconceptions of STEM careers, and knowing about STEM career opportunities. The purpose of our research is to explore the potential influence of socializers on students as they make career choice decisions (primarily in science and engineering) from student's and educator's perspectives. We focused on students from Appalachia because they typically come from lower SES, are often first generation college (FGC) students, and are underrepresented in STEM fields, making their college and career choices particularly important to understand. We framed our research in Eccles' Expectancy Value Theory using data from an on-line informational questionnaire with educational stakeholders and in-person interviews with high school students from rural central Appalachia. Consistent with EVT, our findings reveal that students consider their parents and their educators as valuable socializers. Educators, however, value outreach activities provided by professionals to help students explore engineering career choices. This is consistent with a reported lack of confidence in ability to talk with students regarding engineering careers. By comparing the perspectives of students and educators, we begin to address the potential gaps of “who's doing what” with respect to assisting students as they navigate career choice decision making in high school.

Creative thinking is one of the professional skills desired in engineering students. Empirical evidence suggests that active learning strategies can have a positive impact in the development of professional skills required in the engineering field. Team-based learning (TBL) is a type of active learning that may be able to promote creative thinking in undergraduate engineering students. The purpose of this study is to determine if TBL has an impact on creative thinking in undergraduate engineering students. An experiment was conducted in which 106 undergraduate engineering students were randomly assigned to a TBL or traditional lecture-type learning condition, respectively. Students in the TBL environment were randomly assigned to teams of 5 participants each. The experiment was conducted over a 16-week semester. A post-test will be administered by the end of the semester in order to measure creative thinking in both groups to measure possible differences.

Hofstede's theory of national cultures has been widely used to analyze cultural differences. In this pilot study we describe our experiences applying an instrument based on Hofstede's work to determine whether his dimensions of national cultures can be mapped to academic disciplines. In this paper, we present information about Hofstede's model as well as critiques of both the model and the instrument used. We also report initial results of a pilot survey based on Hofstede's model that we administered to a sample of 687 undergraduate engineering students. Preliminary results show that the instrument is reliable and valid; in addition, we present preliminary results regarding how different engineering majors map to Hofstede's cultural dimensions.