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1. Impacts Resulting from a Large-Scale First-Year Engineering and Computer Science Program on Students’ Successful Persistence Toward Degree Completion

   Ragusa, Gisele ; Allen, Emily L. ; Menezes, Gustavo B. ( June 2020 , ASEE Annual Conference Proceedings 2020)

   There is a critical need for more students with engineering and computer science majors to enter into, persist in, and graduate from four-year postsecondary institutions. Increasing the diversity of the workforce by inclusive practices in engineering and science is also a profound identified need. According to national statistics, the largest groups of underrepresented minority students in engineering and science attend U.S. public higher education institutions. Most often, a large proportion of these students come to colleges and universities with unique challenges and needs, and are more likely to be first in their family to attend college. In response to these needs, engineering education researchers and practitioners have developed, implemented and assessed interventions to provide support and help students succeed in college, particularly in their first year. These interventions typically target relatively small cohorts of students and can be managed by a small number of faculty and staff. In this paper, we report on “work in progress” research in a large-scale, first-year engineering and computer science intervention program at a public, comprehensive university using multivariate comparative statistical approaches. Large-scale intervention programs are especially relevant to minority serving institutions that prepare growing numbers of students who are first in their family to attend college and who are also under-resourced, financially. These students most often encounter academic difficulties and come to higher education with challenging experiences and backgrounds. Our studied first-year intervention program, first piloted in 2015, is now in its 5th year of implementation. Its intervention components include: (a) first-year block schedules, (b) project-based introductory engineering and computer science courses, (c) an introduction to mechanics course, which provides students with the foundation needed to succeed in a traditional physics sequence, and (d) peer-led supplemental instruction workshops for calculus, physics and chemistry courses. This intervention study responds to three research questions: (1) What role does the first-year intervention’s components play in students’ persistence in engineering and computer science majors across undergraduate program years? (2) What role do particular pedagogical and cocurricular support structures play in students’ successes? And (3) What role do various student socio-demographic and experiential factors play in the effectiveness of first-year interventions? To address these research questions and therefore determine the formative impact of the firstyear engineering and computer science program on which we are conducting research, we have collected diverse student data including grade point averages, concept inventory scores, and data from a multi-dimensional questionnaire that measures students’ use of support practices across their four to five years in their degree program, and diverse background information necessary to determine the impact of such factors on students’ persistence to degree. Background data includes students’ experiences prior to enrolling in college, their socio-demographic characteristics, and their college social capital throughout their higher education experience. For this research, we compared students who were enrolled in the first-year intervention program to those who were not enrolled in the first-year intervention. We have engaged in cross-sectional 2 data collection from students’ freshman through senior years and employed multivariate statistical analytical techniques on the collected student data. Results of these analyses were interesting and diverse. Generally, in terms of backgrounds, our research indicates that students’ parental education is positively related to their success in engineering and computer science across program years. Likewise, longitudinally (across program years), students’ college social capital predicted their academic success and persistence to degree. With regard to the study’s comparative research of the first-year intervention, our results indicate that students who were enrolled in the first-year intervention program as freshmen continued to use more support practices to assist them in academic success across their degree matriculation compared to students who were not in the first-year program. This suggests that the students continued to recognize the value of such supports as a consequence of having supports required as first-year students. In terms of students’ understanding of scientific or engineering-focused concepts, we found significant impact resulting from student support practices that were academically focused. We also found that enrolling in the first-year intervention was a significant predictor of the time that students spent preparing for classes and ultimately their grade point average, especially in STEM subjects across students’ years in college. In summary, we found that the studied first-year intervention program has longitudinal, positive impacts on students’ success as they navigate through their undergraduate experiences toward engineering and computer science degrees. 

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2. Retention Focused S-STEM Support Program

   Hensel, Robin ; Morris, Melissa ; Dygert, Joseph ( September 2019 , AAAS/NSF 2019 S-STEM Symposium)

   POSTER. Presented at the Symposium (9/12/2019) Abstract: The Academy of Engineering Success (AcES) employs literature-based, best practices to support and retain underrepresented students in engineering through graduation with the ultimate goal of diversifying the engineering workforce. AcES was established in 2012 and has been supported via NSF S-STEM award number 1644119 since 2016. The 2016, 2017, and 2018 cohorts consist of 12, 20, and 22 students, respectively. Five S-STEM supported scholarships were awarded to the 2016 cohort, seven scholarships were awarded to students from the 2017 cohort, and six scholarships were awarded to students from the 2018 cohort. AcES students participate in a one-week summer bridge experience, a common fall semester course focused on professional development, and a common spring semester course emphasizing the role of engineers in societal development. Starting with the summer bridge experience, and continuing until graduation, students are immersed in curricular and co-curricular activities with the goals of fostering feelings of institutional inclusion and belonging in engineering, providing academic support and student success skills, and professional development. The aforementioned goals are achieved by providing (1) opportunities for faculty-student, student-student, and industry mentor-student interaction, (2) academic support, and student success education in areas such as time management and study skills, and (3) facilitated career and major exploration. Four research questions are being examined, (1) What is the relationship between participation in the AcES program and participants’ academic success?, (2) What aspects of the AcES program most significantly impact participants’ success in engineering, (3) How do AcES students seek to overcome challenges in studying engineering, and (4) What is the longitudinal impact of the AcES program in terms of motivation, perceptions, feelings of inclusion, outcome expectations of the participants and retention? Students enrolled in the AcES program participate in the GRIT, LAESE, and MSLQ surveys, focus groups, and one-on-one interviews at the start and end of each fall semester and at the end of the spring semester. The surveys provide a measure of students’ GRIT, general self-efficacy, engineering self-efficacy, test anxiety, math outcome efficacy, intrinsic value of learning, inclusion, career expectations, and coping efficacy. Focus group and interview responses are analyzed in order to answer research questions 2, 3, and 4. Survey responses are analyzed to answer research question 4, and institutional data such as GPA is used to answer research question 1. An analysis of the 2017 AcES cohort survey responses produced a surprising result. When the responses of AcES students who retained were compared to the responses of AcES students who left engineering, those who left engineering had higher baseline values of GRIT, career expectations, engineering self-efficacy, and math outcome efficacy than those students who retained. A preliminary analysis of the 2016, 2017, and 2018 focus group and one-on-one interview responses indicates that the Engineering Learning Center, tutors, organized out of class experiences, first-year seminar, the AcES cohort, the AcES summer bridge, the AcES program, AcES Faculty/Staff, AcES guest lecturers, and FEP faculty/Staff are viewed as valuable by students and cited with contributing to their success in engineering. It is also evident that AcES students seek help from peers, seek help from tutors, use online resources, and attend office hours to overcome their challenges in studying engineering. 

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3. The Affordance of Computer-Supportive Collaborative Learning in a Dynamics Course

   Lee, Y. ( July 2021 , Zone 1 Conference of the American Society for Engineering Education)

   null (Ed.)

   Over the past two decades, educators have used computer-supported collaborative learning (CSCL) to integrate technology with pedagogy to improve student engagement and learning outcomes. Researchers have also explored the diverse affordances of CSCL, its contributions to engineering instruction, and its effectiveness in K-12 STEM education. However, the question of how students use CSCL resources in undergraduate engineering classrooms remains largely unexplored. This study examines the affordances of a CSCL environment utilized in a sophomore dynamics course with particular attention given to the undergraduate engineering students’ use of various CSCL resources. The resources include a course lecturebook, instructor office hours, a teaching assistant help room, online discussion board, peer collaboration, and demonstration videos. This qualitative study uses semi-structured interview data collected from nine mechanical engineering students (four women and five men) who were enrolled in a dynamics course at a large public research university in Eastern Canada. The interviews focused on the individual student’s perceptions of the school, faculty, students, engineering courses, and implemented CSCL learning environment. The thematic analysis was conducted to analyze the transcribed interviews using a qualitative data analysis software (Nvivo). The analysis followed a six step process: (1) reading interview transcripts multiple times and preliminary in vivo codes; (2) conducting open coding by coding interesting or salient features of the data; (3) collecting codes and searching for themes; (4) reviewing themes and creating a thematic map; (5) finalizing themes and their definitions; and (6) compiling findings. This study found that the students’ use of CSCL resources varied depending on the students’ personal preferences, as well as their perceptions of the given resource’s value and its potential to enhance their learning. For example, the dynamics lecturebook, which had been redesigned to encourage problem solving and note-taking, fostered student collaborative problem solving with their peers. In contrast, the professor’s example video solutions had much more of an influence on students’ independent problem-solving processes. The least frequently used resource was the course’s online discussion forum, which could be used as a means of communication. The findings reveal how computer-supported collaborative learning (CSCL) environments enable engineering students to engage in multiple learning opportunities with diverse and flexible resources to both address and to clarify their personal learning needs. This study strongly recommends engineering instructors adapt a CSCL environment for implementation in their own unique classroom context. 

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4. Developing Deliberate Practice for Learning Engineering Dynamics by Analyzing Students’ Mental Models

   Tang, Yan ; Bai, Haiyan ; Catrambone, Richard ( June 2022 , ASEE annual conference exposition)

   Practice plays a critical role in learning engineering dynamics. Typical practice in a dynamics course involves solving textbook problems. These problems can impose great cognitive load on underprepared students because they have not mastered constituent knowledge and skills required for solving whole problems. For these students, learning can be improved by being engaged in deliberate practice. Deliberate practice refers to a type of practice aimed at improving specific constituent knowledge or skills. Compared to solving whole problems requiring the simultaneous use of multiple constituent skills, deliberate practice is usually focused on one component skill at a time, which results in less cognitive load and more specificity. Contemporary theories of expertise development have highlighted the influence of deliberate practice (DP) on achieving exceptional performance in sports, music, and various professional fields. Concurrently, there is an emerging method for improving learning efficiency of novices by combining deliberate practice with cognitive load theory (CLT), a cognitive-architecture-based theory for instructional design. Mechanics is a foundation for most branches of engineering. It serves to develop problem-solving skills and consolidate understanding of other subjects, such as applied mathematics and physics. Mechanics has been a challenging subject. Students need to understand governing principles to gain conceptual knowledge and acquire procedural knowledge to apply these principles to solve problems. Due to the difficulty in developing conceptual and procedural knowledge, mechanics courses are among those that receive high DFW rates (percentage of students receiving a grade of D or F or Withdrawing from a course), and students are more likely to leave engineering after taking mechanics courses. Deliberate practice can help novices develop good representations of the knowledge needed to produce superior problem solving performance. The goal of the present study is to develop deliberate practice techniques to improve learning effectiveness and to reduce cognitive load. Our pilot study results revealed that the student mental effort scores were negatively correlated with their knowledge test scores with r = -.29 (p < .05) after using deliberate practice strategies. This supports the claim that deliberate practice can improve student learning while reducing cognitive load. In addition, the higher the students’ knowledge test scores, the lower their mental effort was when taking the tests. In other words, the students who used deliberate practice strategies had better learning results with less cognitive load. To design deliberate practice, we often need to analyze students’ persistent problems caused by faulty mental models, also referred to as an intuitive mental model, and misconceptions. In this study, we continue to conduct an in-depth diagnostic process to identify students’ common mistakes and associated intuitive mental models. We then use the results to develop deliberate practice problems aimed at changing students’ cognitive strategies and mental models. 

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5. Developing Deliberate Practice for Learning Engineering Dynamics by Analyzing Students’ Mental Models

   Tang, Yan ; Bai, Haiyan ; Catrambone, Richard ( June 2022 , ASEE annual conference exposition)

   Practice plays a critical role in learning engineering dynamics. Typical practice in a dynamics course involves solving textbook problems. These problems can impose great cognitive load on underprepared students because they have not mastered constituent knowledge and skills required for solving whole problems. For these students, learning can be improved by being engaged in deliberate practice. Deliberate practice refers to a type of practice aimed at improving specific constituent knowledge or skills. Compared to solving whole problems requiring the simultaneous use of multiple constituent skills, deliberate practice is usually focused on one component skill at a time, which results in less cognitive load and more specificity. Contemporary theories of expertise development have highlighted the influence of deliberate practice (DP) on achieving exceptional performance in sports, music, and various professional fields. Concurrently, there is an emerging method for improving learning efficiency of novices by combining deliberate practice with cognitive load theory (CLT), a cognitive-architecture-based theory for instructional design. Mechanics is a foundation for most branches of engineering. It serves to develop problem-solving skills and consolidate understanding of other subjects, such as applied mathematics and physics. Mechanics has been a challenging subject. Students need to understand governing principles to gain conceptual knowledge and acquire procedural knowledge to apply these principles to solve problems. Due to the difficulty in developing conceptual and procedural knowledge, mechanics courses are among those that receive high DFW rates (percentage of students receiving a grade of D or F or Withdrawing from a course), and students are more likely to leave engineering after taking mechanics courses. Deliberate practice can help novices develop good representations of the knowledge needed to produce superior problem solving performance. The goal of the present study is to develop deliberate practice techniques to improve learning effectiveness and to reduce cognitive load. Our pilot study results revealed that the student mental effort scores were negatively correlated with their knowledge test scores with r = -.29 (p < .05) after using deliberate practice strategies. This supports the claim that deliberate practice can improve student learning while reducing cognitive load. In addition, the higher the students’ knowledge test scores, the lower their mental effort was when taking the tests. In other words, the students who used deliberate practice strategies had better learning results with less cognitive load. To design deliberate practice, we often need to analyze students’ persistent problems caused by faulty mental models, also referred to as an intuitive mental model, and misconceptions. In this study, we continue to conduct an in-depth diagnostic process to identify students’ common mistakes and associated intuitive mental models. We then use the results to develop deliberate practice problems aimed at changing students’ cognitive strategies and mental models. 

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