This work-in-progress research paper outlines an exploratory study that investigates the agency of engineering students in the context of design-based learning experiences. Professional engineers engage in various decision-making activities when tasked with the design of a product, system, or service. Such decisions are either informed by constraints placed by stakeholders or are a consequence of trade-offs between multiple criteria in the design process. In both scenarios, engineers have to exercise agency in how they approach information seeking, goal-setting, solution generation, prototyping and testing. This requires them to be comfortable both with being given agency, and in utilizing it to their advantage for effective problem-solving. Therefore, it is important to expose students to learning environments where they have opportunities to make choices and evaluate those choices in the context of problem solving. There is also literature to suggest that opportunities for exercising agency in educational contexts improve learning, engagement, and develop skills of critical thinking and innovative problem-solving. Such opportunities are created through learning activities with different levels of scaffolding, open-endedness, and course integration - from the use of open-ended modeling projects within specific content areas, to entire design courses that invite students to exercise agency at all steps of their design process. Though agency has been studied before in these contexts, there is a dearth of studies that examine how such opportunities to practice agency impact students during their engagement with engineering design. This study aims to begin addressing this gap by exploring the research question: How do first-year students experience agency in an introductory design project? The study uses data from the first-year course sequence of an engineering program offered at large, Midwestern, public university. Students from all engineering disciplines enroll in this course and complete multiple design-related problems and projects in a team-based learning setting. This paper will present the initial results of an inductive analysis of 3 semi-structured interviews conducted with the students, capturing their own perceptions regarding their ability to make decisions throughout the project and examining the structures that encouraged or inhibited them in exercising agency. This research has implications for engineering instructors related to the design of learning environments that foster agency and the role that scaffolding plays in forming agentic student behaviors. Future work will expand our study of student perceptions of agency to include other mediating factors such as self-efficacy, motivation, and constraints on their learning experience.

This work-in-progress research paper focuses on the first-year engineering teams' peer evaluation processes. Teamwork has been identified as a necessary skill for professional engineers and thus an ABET learning outcome. Reliable systematic processes to assess teamwork effectiveness are crucial for improving team outcomes and identifying dysfunctional teams in a classroom setting. Evaluating team effectiveness and identifying dysfunctional teams has been traditionally done through peer evaluations. However, there is a lack of evidence on what/how students' perceptions of effective teammates and functional teams impact peer evaluations. Identifying common indicators and behaviors that students consider when evaluating their peers is a first step to exploring the reliability of peers as evaluators, understanding potential biases, and using peer feedback to investigate dysfunctional teams. This paper aims to take this first step and will explore the following question: “What evidence do students provide to describe poor team behaviors?” The study was conducted at a large, public, urban, Midwestern R1 institution. For the first two semesters of the engineering curriculum, students are required to participate actively in team-based projects as part of engineering design thinking courses. The following procedure was used to answer our research question. Students were first asked to respond to a peer evaluation instrument based on a four-factor (Interdependency, Goal setting, Potency, and Trust) team effectiveness model. Then, students were asked to grade their teammates on a cumulative score of 100 for the team and support their distribution choices with open-ended comments on team behaviors. We started with an analysis of students' comments regarding negative behaviors to support their low rating for each teammate. We selected comments that had a high misalignment between the model-based peer evaluation and corresponding distributed ratings, resulting in a sample of 93 students. Next, we conducted inductive coding on the comments to identify frequently mentioned negative behaviors and see if these behaviors were addressed in the model-based peer evaluation. Eight major themes emerged from students' comments.

This research work-in-progress paper reports on a proof of concept trial of an augmented reality learning platform that aims to unobtrusively capture elementary students' collaborative actions and affective states. With the growing push to integrate engineering education in K-12 settings, elementary teachers and students face many implementation challenges. In particular, while engineering design projects can serve as a context for developing collaborative skills, younger students might find open-ended projects where working with others who have different views than their own especially difficult. Such projects may also force young students to contend with several emotions. In this space, we propose MindLabs, an augmented reality platform for engaging in integrated science and engineering design projects. In a pilot study 216 elementary students on teams completed a lesson on forces and motions which ended with a two-day design challenge. While students interacted with the system, their collaborative actions and use of the affective state reporting tool were captured. Using learning analytics and more specifically exploratory data visualization, we present three cases that demonstrate positive and negative collaboration dynamics and affect reporting differences. We discuss how the results relate to past research on collaboration dynamics, affect and conflict and how these analytics could support teachers to identify and assist different groups of students. We conclude with future steps for this project.

The research-to-practice paper presents applications of genetic algorithms using multi-criterion optimization to designate student-teams in an academic setting. Teamwork skills are becoming a more desirable trait in industry today and hence more instructors are using teamwork in their engineering courses. ABET also requires engineering degree programs to have student outcomes that provide them with "an ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives." This, however, comes with its challenges because the literature suggests that random or student-selected teams tend to lead to dysfunctional behaviors. Instructor-designated academic teams based on skills, learning personalities, and demographics are known to be more effective in promoting learning and positive interactions. Creating optimal homogeneous or heterogeneous teams based on multiple criteria (e.g., prior knowledge, skills, abilities, psychosocial, demographics) can be a complex and time-consuming task when done manually, especially when large numbers of students are involved.This study presents an expansion of previous work that used single-criterion genetic algorithm optimization of teams in a first-year classroom. The research question answered in this study is, how do teams formed using an algorithm that optimizes multiple criteria with genetic algorithms represent heterogeneity when compared to teams formed manually? Using a Vector Evaluated Genetic Algorithm (VEGA), optimization of multiple skills is conducted to attain uniform heterogeneity in the classroom. The algorithm uses discrete integer-type representations as a basic unit of team configuration. Self-reported student competency data on three different computational skills were used. Teams were formed for approximately 1300 students enrolled in a first-year engineering design thinking course at a large Midwestern University. Four-member teams were maximized with a minimum number of teams with 3 students in each section. Average skills of the teams are calculated and the standard deviation in class is minimized for each of three skills parallelly. The algorithm also aims to maintain diversity of teams based on gender and ethnicity.Results presented include visualization of team-configurations and comparison with teams formed manually using the same criteria. The aim of the algorithm is to optimize students into teams that have skills and demographics uniformly distributed across the classroom. Future implications of this study include the potential to use flexible algorithm-based team formations that are built with standard genetic operators so that optimization criteria can be modified by the instructor. Such algorithms can reduce the time needed to manually form teams by a considerable amount and optimize the distribution of skills more uniformly. Steps to further this study will involve investigating whether teams formed using these criteria are more effective.

This work-in-progress study synthesizes the literature pertaining to programs that aim to develop the pedagogical skills of STEM graduate students pursuing a career in academia. The United States continues to experience a severe lack of STEM professionals needed to be able to stay at the forefront of global technological advancement. Persistence rates in undergraduate STEM programs have shown minimal improvement over the last decade. Research suggests that inadequate teaching is one of the major factors for students leaving STEM fields. In this light, there is an increasing interest to improve and reward contributions to teaching excellence. But conventional systems, particularly at R1 institutions, continue to prioritize research productivity in faculty hiring and tenure/promotion decisions. While new faculty are expected to be teaching-ready, little to no training is provided to graduate students pursuing an academic career. Since 1993, programs such as Preparing Future Faculty (PFF) and the Center for the Integration of Research, Teaching, and Learning (CIRTL) have been actively trying to fill this gap in training future faculty for teaching. A comprehensive assessment of current practices in pedagogical training provided to graduate students can inform future changes in policy and practice. This study is the first step of a larger study and aims to synthesize and summarize the current state of programs in universities across the United States that focus on such pedagogical training. Initiatives focused on professional development of graduate students in STEM fields, with special attention to engineering, were screened for programs specifically contributing to pedagogical development. Published articles in the Journal of Engineering Education, Conference proceedings of the American Society for Engineering Education, and several databases available through the University of Cincinnati library Summon search engine were used to find relevant publications on program implementations and evaluations over the last two decades. Qualifying papers were summarized and compared to construct a description of the past and current state of pedagogical training received by graduate students in STEM majors. The ultimate goal of the larger study will be a list of identified promising practices that show evidence of effectiveness in preparing graduate students for teaching and a set of recommendations for redesigning or implementing such programs in engineering.

This work in progress study aims at developing a system to designate teams to relatively large groups of students in a classroom setting. It is motivated by recent changes in the ABET Criterion 3 accreditation guideline that requires students to demonstrate "an ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives." Outside of accreditation guidelines, team-based learning is becoming the prescribed pedagogical tool to enhance student collaboration and prepare them for professional work environments upon graduation. One important factor in effective team-based learning is to ensure teams are balanced in their abilities and characteristics across team members. Demographical memberships of students can also play an important role in team dynamics and impact the overall success in content learning. Instructors usually assign teams to students by manually looking into their abilities from previous grades and other performance factors. This can then become tedious when aiming to maintain uniform heterogeneity in classrooms of students coming from diverse backgrounds of knowledge, skills, ethnicity, and gender. While some studies have presented the use of computer-aided tools, visualization categorization, and other Artificial Intelligence techniques, this work proposes the use of the Genetic Algorithm (GA) to form teams optimized for heterogeneity. The Genetic Algorithm presented uses a discrete integer-based chromosome representation and groups alleles to represent each team. Standard GA operators enable the algorithm to be adapted to any optimizing criteria deemed fit by the instructor. The algorithm presented in this work uses self-reported competency data on 3 different computational skills for 1300 students enrolled in a firstyear engineering design thinking course divided into over 20course sections of strength 40-60 each. Net team scores for each computational tool are calculated and the variation across each skill minimized by the fitness function for individual sections. Constraints based on gender and ethnicity are applied to minimize demographical imbalance between teams. The algorithm is tested for all sections in the Fall 2019 cohort to give consistent team configuration. Discussions on the stability and validity of configurations generated are discussed. Future work include methods that use a fuzzy logic decision-making tool for multiple criteria optimization.