Students often confuse the practice of teaching, scholarly teaching, scholarship of teaching and learning, and education research, often associating engineering education research with improving individual teachers’ practices and assessment and failing to recognize its greater potential contributions to advancing all aspects of engineering education. This REU program provides opportunities to introduce students to the significance and rigor of the field of engineering education research. The program will allow students to fully participate in engineering education research topics that span a range of disciplines and contexts and provide a pathway into graduate level engineering education research.
Competitive stipend: $6,000
Suite-style room and meal plan
Travel expenses to and from Lincoln
Campus parking and/or bus pass
Full access to the Campus Recreation Center and campus library system
Evidencing Epidemic Change in Engineering Education (3 student openings possible)
The use of a wide array of teaching practices and strategies (WATPS) in higher STEM education has been shown to improve students’ conceptual understanding, appeal to a diverse set of students, and increase persistence in engineering, especially among underrepresented groups (Freeman et al., 2014; Kuh et al., 2006; President's Council, 2012; Seymour & Hewitt, 1997). Prior to the COVID-19 pandemic, many engineering instructors continued to use traditional teaching methods, hindering the formation of engineers. When universities switched to emergency remote teaching (Hodges et al., 2020), instructors experienced crisis-induced motivation to adopt teaching practices/strategies they had not used before. The overarching research question is: To what extent did instructors sustain, enhance, or extend their use of these practices and strategies?
The research objective for this project is to investigate and document the effects of the COVID-19 pandemic on instructors’ teaching practices and sustained use of a WATPS relative to instructors’ adaptability (“the effectiveness of an individual’s response to new demands resulting from the novel and often ill-defined problems created by uncertainty, complexity, and rapid changes in the work situation” (Chan, 2000, p. 3)) and course complexity (a measure of an instructors’ use of WATPS in a course and the challenge of implementing the teaching practices and strategies used a course). Student Participation: The REU participant(s) will learn to apply a Course Complexity Typology to classify the complexity of engineering courses using course artifacts (e.g., syllabi and learning management feature use data) and investigate changes in course complexity over time. Data will have been gathered prior to the REU program from multiple engineering departments and academic years. REU participants will work with an appropriate subset of the data to answer their specific research question(s). REU participant does not need any prior experience or have any preferred qualifications.
Analyzing Assessments for Virtual/Augmented-Reality-Based Discipline Exploration Rotations (VADERs)
The path to proficiency in engineering is long and difficult, often lacking displays of what it would be like to be an engineer and the positive societal impacts of engineering, weakening students’ interest (engagement) and confidence (self-efficacy) and perpetuating issues of retention and capacity building (National Academies, 2016). Virtual/Augmented-Reality-Based Discipline Exploration Rotations (VADERs) provide students with a platform to explore Architectural Engineering and its subdisciplines through virtual, mock-up healthcare spaces and interactions. VADERs are open-ended, human-computer interactions informed by the Model of Domain Learning (MDL, Kulilowich & Hepler, 2018) framework to help students visualize themselves in their chosen careers and enhance resiliency against the challenges of an engineering degree program.
VADERs are embedded into courses through assignments to allow students to better link concepts learned in the classroom to realistic work examples. The overarching research question guiding this work is: Do VADERs positively impact student interest and self-efficacy in engineering? Data will be available from two architectural engineering departments and multiple courses over a two-year period. Student Participation: The REU participant(s) will analyze a series of structured assessments and self-reflections aligned to Social Cognitive Career Theory (SCCT, Lent et al., 1994) to gauge the impact of VADERs on (1) students’ interest, self-efficacy, and outcome expectations with attention to general statistical trends, (2) differences across subject demographics, and (3) emerging themes across multiple exposures to VADER modules.REU participant does not need any prior experience or have any preferred qualifications.
Building Broader Perspectives in Brazil: Construction Engineering Students’ Development of Intercultural Maturity During a Study Abroad Experience
This research project aims to investigate how construction engineering students' intercultural maturity changes as a result of a study abroad experience in Brazil. Intercultural maturity refers to the ways in which people develop the capacity to understand and act in ways that are interculturally aware and appropriate. Six construction students from a large university in the midwestern United States are the subjects of this work. Participants had opportunities to take courses, visit job sites, and connect with local Brazilian students throughout their experience abroad. The study will be conducted using a qualitative case study approach and leverage thematic analysis to identify the ways in which students’ intercultural maturity changed as a result of the experience.
The results of this study will help to improve our understanding of how study abroad experiences can impact students' intercultural competencies and provide insights into the effectiveness of international study programs in promoting intercultural maturity and enhancing students' understanding of construction engineering in a global context. Furthermore, the findings have the potential to inform the development of future study abroad programs and provide guidance for educators seeking to enhance their curricula to better prepare students for the global workforce. Student Participation: The REU student will assist in conducting a thematic analysis of interview and reflection data collected before, during, and after the study abroad experience. This data will be triangulated to answer a specific research question of interest, determined collaboratively with the student and the PI. The student will then work with the PI to begin creating a publication based on the findings. REU participant does not need any prior experience or have any preferred qualifications.
Eye Movement Modeling Examples for Program Comprehension and Debugging
The goal of this project is to collect eye movement modeling examples from experts doing a) program comprehension tasks and b) debugging tasks on code examples commonly found in undergraduate computing curricula. Educators are typically aware of several major problems in comprehension and debugging that hinder student progress. The purpose is to expose the thought process of an expert through eye movement modeling examples specific to a context. The project proposes the creation of a repository of eye movement modeling video examples recorded by an expert for program comprehension and debugging tasks. The experts can be advanced graduate students fluent in the language of choice. This project helps novices learn by watching eye movements of experts as they work on comprehending programs (e.g., how to spot a code beacon) and while debugging. The videos will have a voice-over of the expert vocalizing their thought process. These videos can be used as teaching aids to help novices learn where to look, how to read code, and how to avoid areas that are not important to the task.
Student Participation: The REU participant(s) will devise a set of common problems in comprehension and debugging in alignment with computing curriculum at UNL. These will be translated into programs and tasks. The student will then learn how to design and conduct the eye tracking recordings for program comprehension and debugging tasks. The screen will also be recorded during the process and in a post processing step, the gaze data will be visualized on top of the recorded screen. The student will be using iTrace to record and post process this data. The raw gaze can also be used for further analysis as needed. The contributions of this project are a) helping novices learn the thought processes of experts as they work by watching the eye movement modeling examples and b) a audio/video repository of eye movement examples for educators. REU participant does not need any prior experience or have any preferred qualifications.
Towards Factors Classifying Novices and Experts in Debugging Tasks via Eye Tracking
Experts and novices have different reading behaviors on source code. Novices have a hard time understanding what a program does and finding the source of bugs during the run/edit programming cycle. Most expert/novice data on eye tracking has been on Java. This project seeks to collect data on Python from experts and novices solving debugging tasks. This will help build models that help us discern the different behaviors that separate experts from novices. The main research question will be to characterize factors that separate an expert from a novice solely based on eye tracking data.
Student Participation: The REU participant(s) will learn how to design and conduct a pilot eye-tracking study related to program comprehension in software engineering. The collected gaze data will then be analyzed through a data processing pipeline where differences will be observed between experts and novices based on several different eye-tracking metrics. Both quantitative and qualitative measures will be used to analyze the data. REU participant does not need any prior experience or have any preferred qualifications.
Intelligent Web-based Platform to Support Online Professional Learning Communities in CS Education (2 student openings possible)
Professional Learning Communities (PLCs) are a proven approach to improve teacher identity and engagement, leading to increased motivation and self-efficacy. Thus, PLCs can help teachers improve their instruction, directly impacting student learning and performance. However, teachers at rural areas might not have sufficient peers to form a PLC. This is particularly challenging for K-12 CS teachers as many schools either do not offer CS courses or only have one teacher teaching CS courses. Meanwhile, the advances in online or virtual community or collaborative platforms have grown tremendously due to the social media technologies. These platforms have features that enable or encourage collaboration and community activities, such as intelligent recommendations, informed sharing of resources, individualized experiences, and so on. This research project is aimed to develop a platform to support an online PLC for K-12 CS teachers using intelligent features, and to investigate the effectiveness of such features and how they impact teacher identity and their instruction.
Student Participation: The REU participant(s) will help develop and implement online features for the intelligent Web-based platform to engage and motivate K-12 CS teachers. They will also help carry out investigations including establishing research questions, analyzing qualitative and quantitative data, and reporting on findings. This research will help us better understand barriers and facilitators involved in online Professional Learning Communities (PLCs) for K-12 CS teachers in particular, and for K-12 teachers in general, especially those in the rural areas. REU participant does not need any prior experience or have any preferred qualifications.