This work-in-progress research paper describes the epistemic identity of one researcher, Dr. Roberts, working on an interdisciplinary engineering education research team. Engineering education research is an interdisciplinary field that includes researchers from a range of disciplinary backgrounds who each bring their own approaches to the generation, expression, and application of knowledge. These different epistemic identities can lead to tensions that prevent teams from having their desired impact. Our data includes a transcript from a 60-minute semi-structured interview. During the interview, we asked questions to explore specific aspects of Dr. Roberts' epistemic identity, the approaches he used to navigate differences in thinking, and instances of epistemic negotiation in the research team meetings. This data was analyzed using an open coding process defined by Charmaz (2014) followed by the construction of a memo to describe Dr. Roberts' epistemic identity. We describe the results from our initial analysis of Dr. Roberts' epistemic identity.

This work-in-progress, innovative practice paper describes the design and implementation of a computer programming project that encouraged students to use artificial intelligence (AI) tools (e.g., ChatGPT). This innovative practice was implemented at a state university in an introduction to programming course. Course assessments included weekly homework assignments, an open-ended project, one quiz, and three midterm exams. Students were explicitly told in class and in the syllabus that no AI was allowed for weekly homework assignments; however, students were encouraged to use AI for the open-ended project. This decision was made because the problems on weekly assignments were more structured and could be easily solved by AI tools. In comparison, the learning objective of the project was focused on code design with open-ended requirements making it harder for AI to provide workable code. Students who reported using AI indicated that they used it to help start the project and/or a piece of the project and to aid in debugging or finding errors. Project grades were similar to grades seen in previous years for similar projects; however, grades on midterm two, which occurred after the project, were surprisingly higher than the grades for midterm one and higher than exam grades from previous years on similar topics. At this point, we do not know if/how the project contributed to these improved exam scores. Given the success of the project in its first implementation, Dr. E plans to use the assignment in future semesters. Additionally, Dr. C plans to collect data to begin exploring how the use of AI tools in this open-ended project supported students' learning and understanding of principles in the course.

This Full Research paper uses resource network analysis to explore what resources faculty use when they make changes to their pedagogy, and how an engineering education “guild” is situated among those resources. The process of influencing pedagogical change can be understood as lying along a spectrum. On one end of the spectrum is the dissemination model, where research is simply made available and instructors are expected to seek out new tools. On the other end is the propagation model, where researchers, developers, and instructors work as one cohesive team to get innovative tools into classrooms. While each of these models and the instructor resources associated with them have been separately studied and defined, approaches on the spectrum between them remain understudied. Engineering education guilds employ an approach that falls along the dissemination-propagation spectrum; they use both dissemination and propagation techniques to influence pedagogical changes. Despite lack of formal research on the subject, engineering education “guilds” have become an increasingly popular vehicle for pedagogical change in engineering education classrooms. One such engineering education guild is the Kern Entrepreneurial Engineering Network (KEEN), which is focused on integrating entrepreneurial mindset (EM) into engineering curricula. By constructing resource networks for educators who have been exposed to KEEN, we aim to understand the role of KEEN among the myriad resources used by engineering educators when they integrate EM-related content into their classrooms. Results suggest that engineering education guilds are central to the resource networks of faculty looking to innovate their pedagogy, with the most popular resources all falling under the guild's umbrella. These resources are also strongly interconnected, especially during the integration process. However, the resources networks of those who saw successful, complete, sustained adoption reached beyond the guild's umbrella, forging connections with a variety of other materials from different sources.

In this Full Research Category paper, we will provide a detailed description of how we are using analytical memo-writing within a mixed methods grounded theory study. The goal of our work is to identify key elements from undergraduate research experiences (UREs) that can be translated into the classroom by exploring the connections between engineering students' researcher identities, perceptions of research, and epistemic thinking within the context of UREs. The qualitative phase of our study which is the focus on this paper takes a grounded theory approach as we explore our research questions using in-depth interviews. Interviews were conducted by researcher pairs, transcribed, and cleaned. Preliminary analysis, which included initial coding, was conducted by researcher pairs to increase the validity of the analysis and facilitate analytical discussions between researchers. After initial coding, the researcher pairs constructed analytical memos with three parts: (1) summary of the participant (factual representation), (2) summary of salient themes/codes/analysis, and (3) connections of themes/analysis to other participants. We are using these memos to facilitate a constant comparative approach across participants, communicate and understand complex relationships between concepts, and enhance the development of our grounded theory model. As part of our description of our analytical memos, we provide details and examples from our study for each part of the memo. We also discuss the ways in which we have found the analytical memos to be helpful in our analysis. In particular, we discuss our use of the theoretical coding (three-part coding where you identify context, mechanism, and outcome) and magnitude coding (quantify qualitative data by creating Likert-type Scales for specific concepts) of our memos, which allow us to explore relationships between concepts and produce empirical support for the connections within our grounded theory model.

This Research Work in Progress paper presents our initial findings using network analysis to explore the resources (peers, teaching assistants, faculty, textbook, class notes, online, etc.) first-year engineering students use to complete homework in their engineering course. We designed a survey with open and closed-ended items to begin to explore what resources students use and why they use specific resources when completing a course assignment. Based on the data related to what resources students use, we created a weighted edge network using the statistical software, R. Through this analysis we identified the pairs of resources that were used the most by students. The open-ended survey responses were analyzed using conventional qualitative content analysis to understand why students use specific resources. This analysis identified four themes (contingency, isolation, convenience, and comfort) that represent how students make decisions about resources to use when completing homework.

As the need for qualified science, technology, engineering, and mathematics (STEM) graduates increases, there is an accompanying need for improved undergraduate STEM education. Undergraduate Research Experiences (UREs) have been shown to enhance an undergraduate student's academic experience; however, not all students can participate in or have access to UREs due to schedule constraints during the school year or other commitments in the summer. Our current research project seeks to determine how students develop a researcher identity and transform their epistemic beliefs through UREs. Elements identified to contribute to students' researcher identities and epistemic beliefs will then be translated into strategies that can be incorporated into traditional learning environments. This paper will overview the progress made in the first part of this multi-phase, multi-institution project and preliminary results from the initial surveys.

This study utilized a quantitative survey and open-ended items to understand engineering students' epistemic beliefs and gather content and face validity evidence. The survey included 22 items from the Engineering-Related Beliefs Questionnaire. Fifty undergraduate bioengineering students completed the survey. In addition to responding to the items on an anchored scale, the students were asked to provide short written explanations of their responses in a textbox below each item. Students' open-ended responses were analyzed using qualitative content analysis to gather content validity evidence and gain a general understanding of students' engineering epistemic beliefs. This analysis revealed inconsistencies with how students interpret the items and ambiguous terms used in the items. Based on the results of this analysis suggestions are made to improve the items on the survey for future use.

Successful group work requires that students transfer relevant prior knowledge to solve problems. This paper establishes a method to assess dynamic knowledge transfer in a group setting through analysis of a group project in a biomechanics class. Transcripts of student-student and student-instructor interactions were coded for evidence of target tools (students identifying relevant problem features), source tools (students activating prior knowledge), answers (stopping points), external inputs (resources and prompts from individuals or the instructor), and workbench explanations (student explanations of connections between source tools and target tools). Knowledge transfer was identified when a source tool and a target tool were coded within a phrase. The frequencies of codes were quantified to provide an overall picture of knowledge transfer for each group member throughout the project. Analysis for one group (a sophomore and junior bioengineering student, and a freshman engineering student) revealed that the junior was the largest contributor in the group, followed by the sophomore and freshman. The group mentioned source tools most frequently, followed by external inputs and target tools. The analysis provided evidence of knowledge transfer within the group through their identification of target tools and use of prior knowledge to explain their observations.