More Like this

1. Using Visualization to Reduce the Cognitive Load of Threshold Concepts in Computer Programming

   https://doi.org/10.1109/FIE43999.2019.9028612  

   Winter, Victor; Friend, Michelle; Matthews, Michael; Love, Betty; Vasireddy, Sanghamithra (October 2019, 2019 IEEE Frontiers in Education Conference (FIE))

   This Full Paper in the Research to Practice Category reports on an empirical empirical study in which novel educational tools and techniques were employed to teach fundamentals of problem decomposition - a cognitive task transcending disciplines. Within the discipline of computer science, problem decomposition is recognized as a foundational activity of software development. Factors that contribute to the complexity of this activity include: (1) recognizing patterns within an algorithm, (2) mapping the understanding of an algorithm to the syntax of a given programming language, and (3) complexity intrinsic to the problem domain itself. Cognitive load theory states that learning outcomes can be positively affected by reducing the extraneous cognitive load associated with learning objectives as well as by changing the nature of what is learned. In the study reported upon here, a novel instructional method was developed to decrease students' cognitive load. Novel instructional content supported by a custom visualization tool was used in a classroom setting in order to help novice programmers develop an understanding of function-based problem decomposition within the context of a visual domain. Performance on outcome measures (a quiz and assignment) were compared between the new method and the traditional teaching method demonstrated that students were significantly more successful at demonstrating mastery when using the new instructional method. 

   more » « less
2. Full Scale Augmented Reality to Support Construction Sequencing Education Case Study

   https://doi.org/10.1061/9780784483985.003  

   McCord, K; Ayer, S; Patil, K; Wu, W; London, J; and Perry, L. (January 2022, Construction Research Congress 2022)

   Providing students with hands-on construction experiences enables them to apply conceptual knowledge to practical applications, but the high costs associated with this form of learning limit access to it. Therefore, this paper explores the use of augmented reality (AR) to enable students in a conventional classroom or lab setting to interact with virtual objects similar to how they would if they were physically constructing building components. More specifically, the authors tasked student participants with virtually constructing a wood-framed wall through AR with a Microsoft HoloLens. Participants were video-recorded and their behaviors were analyzed. Subsequently, observed behaviors in AR were analyzed and compared to expected behaviors in the physical environment. It was observed that students performing the tasks tended to mimic behaviors found in the physical environment in how they managed the virtual materials, leveraged physical tools in conjunction with virtual materials, and in their ability to recognize and fix mistakes. Some of the finer interactions observed with the virtual materials were found to be unique to the virtual environment, such as moving objects from a distance. Overall, these findings contribute to the understanding of how AR may be leveraged in classrooms to provide learning experiences that yield similar outcomes to those provided in more resource-intensive physical construction site environments. 

   more » « less
3. Developing a Writing Rubric to Answer Research Questions (not for Grading!)

   Lynch, J W; Aller, B M; Sorby, S A; Murphy, T J (April 2024, ASEE Peer)

   Industry leaders emphasize that engineering students' technical communication and writing skills must be improved. Despite various institutional efforts, which include technical communication courses or engineering design projects aimed at enhancing students’ communication abilities, many believe there has been only slow improvement in this area. There has also been a dearth of longitudinal studies that examine the development of engineering students’ technical communication competencies from undergraduate to industry. This paper aims to contribute to this area through the creation of a rubric that specifically examines the writing competencies and technical communication ability of engineering students. This paper is part of a larger, NSF-funded research study that examines the quality of students’ written and oral communication skills and seeks to understand their relationship to the students’ spatial abilities. First-year engineering students in their second semester at a large R1 Midwestern university were examined. Students were tasked with creating a written report responding to a set of questions that asked about their team-based engineering design project completed in their first semester. As this occurred months prior, this non-graded report became a reflection on their experience and innate abilities. While low stakes, it mimicked a more authentic writing experience students encounter in industry. Students' responses were examined collaboratively by an interdisciplinary team which created a rubric through an iterative process. This rubric was distributed to the interdisciplinary team and outside evaluators composed of individuals in industry and engineering faculty. An inter-rater reliability analysis was conducted to examine levels of agreement between the interdisciplinary team and outside evaluators, and implications of this inter-rater reliability score and the process of rubric application were documented. Results of this paper include details on the development of a rubric that examine students’ technical communication and writing skills. Traditional rubrics utilized by engineering faculty usually address an entire project for engineering students, which includes students' content knowledge, writing capabilities, and the requirements of the project. Such rubrics are often used to provide feedback to students and evaluation in the form of grades. The narrower focus of the rubric being developed here can provide insights into communication and writing competencies of engineering students. Scores secured through the use of this rubric will aid in the research study’s goal of finding correlations between engineering students’ communication skills and spatial abilities (assessed outside of this current effort). Spatial ability has been well-documented as an effective indicator of success in STEM, and interventions have been developed to support development in students with weaker spatial skills. 23, 24This has prompted this research to explore links between spatial skills and communication abilities, as validated spatial interventions may help improve communication abilities. These current results may also provide unique insights into first-year engineering students’ writing competencies when reporting on a more authentic (non-graded) engineering task. Such information may be useful in eventually shaping guidance of students’ communication instruction in hopes of better preparing them for industry; this is the focus of a planned future research study. 

   more » « less
4. Introduction to the Special Issue on EDM in Computer Science Education (CSEDM)

   https://doi.org/10.5281/zenodo.7728208  

   Price, Thomas W; Hsiao, Sharon; Akram, Bita; Brusilovsky, Peter; Leinonen, Juho (January 2023, Journal of educational data mining)

   Educational Data Mining in Computer Science Education (CSEDM) is an interdisciplinary research community that combines discipline-based computing education research (CER) with educational data-mining (EDM)  to advance knowledge in ways that go beyond what either research community could do on its own. The JEDM Special Issue on CSEDM received a total of 12 submissions. Each submission was reviewed by at least three reviewers, who brought expertise from both the EDM and CER communities, as well as one of special issue editors. Ultimately, three papers were accepted, for an acceptance rate of 25%.   These three papers cover a variety of important topics in CSEDM research. Edwards et al. discuss the challenges of collecting, sharing and analyzing programming data, and contribute two high-quality CS datasets. Gitinabard et al. contribute new approaches for analyzing data from pairs of students working on programs together, and show how such data can inform classroom instruction. Finally, Zhang et al. contribute a novel model for predicting students' programming performance based on their past performance. Together, these papers showcase the complexities of data, analytics and modeling in the domain of CS, and contribute to our understanding of how students learn in CS classrooms. 

   more » « less
5. Internship Prevalence and Factors Related to Participation

   Atwood, S. A.; Gilmartin, S. K.; Mostoller, A. M.; Chen, H. L.; Sheppard, S. (January 2021, 2021 ASEE Virtual Annual Conference)

   null (Ed.)

   The value of internship experiences for engineering students is widely discussed in the literature. With this analysis, we seek to contribute knowledge addressing 1) the prevalence of internship experiences amongst engineering students drawn from a large, multi-institutional, nationally-representative sample, 2) if the likelihood of having an engineering internship experiences is equitable amongst various student identities, and 3) what additional factors influence the likelihood of a student having an internship experience, such as field of study and institution type. Data were drawn from a 2015 multi-institutional nationally representative survey of engineering juniors and seniors, excluding one institution with a mandatory co-op program (n = 5530 from 26 institutions). A z-test was used to analyze differences in internship participation rates related to academic cohort (e.g., junior, senior), gender, underrepresented minority (URM) status, first-generation, and low-income status, as well as a subset of identities at the intersection of these groups (gender + URM; first-generation + low-income). A logistic regression model further examined factors such as GPA, engineering task self-efficacy, field of engineering, and institution type. We found that amongst the students in our dataset, 64.7% of the seniors had “worked in a professional engineering environment as an intern/co-op” (41.1% of juniors, 64.7% of 5th years). Significantly less likely (p<0.05) to have internship experiences were men compared to women (52.9% vs 58.3%), URM students compared to their majority counterparts (41.5% vs 56.8%), first-generation students compared to continuing (47.6% vs 57.2%), and low-income students compared to higher-income peers (46.2% vs 57.4%). Examined intersectional identities significantly less likely to have an internship were URM men (37.5%) and first-generation low-income students (42.0%), while non-URM women (60.5%) and continuing high-income students (58.2%) were most likely to report having an internship. Results from the logistic regression model indicate that significant factors are cohort (junior vs senior), GPA, engineering task self-efficacy, and engineering field. When controlling for the other variables in the model, gender, URM, first-generation, and low-income status remain significant; however, the interaction effect between these identities is not significant in the full model. Institution type did not have much impact. Having a research experience was not a significant factor in predicting the likelihood of having an internship experience, although studying abroad significantly increased the odds. Amongst engineering fields, industrial and civil engineering students were the most likely to have an internship, while aerospace and materials engineering students were the least likely. Full results and discussion will be presented in the paper. This analysis provides valuable information for a variety of stakeholders. For engineering programs, it is useful to benchmark historic students’ rates of internship participation against a multi-institutional, nationally representative dataset. For academic advisors and career services professionals, it is useful to understand in which fields an internship is common to be competitive on the job market, and which fields have fewer opportunities or prioritize research experiences. Ultimately, for those in higher education and workforce development it is vital to understand which identities, and intersectional identities, are accessing internship experiences as a pathway into the engineering workforce. 

   more » « less