Computational modeling skills are critical for the success of both engineering students and practicing engineers and are increasingly included as part of the undergraduate curriculum. However, students' belief in the utility of these skills and their ability to succeed in learning them can vary significantly. This study hypothesizes that the self-efficacy and motivation of engineering students at the outset of their degree program varies significantly and that engineering students pursuing some disciplines (such as computer, software, and electrical engineering) will begin with a higher initial self-efficacy than others (such as materials science and engineering and biomedical engineering). In this pilot study, a survey was used to investigate the motivational and efficacy factors of approximately 70 undergraduate students in their first year of engineering studies at a large public university. Surveys were implemented after students were introduced to MATLAB in their first-year engineering design course. The data was analyzed for variations in baseline motivation based on the students' intended major. The results of this survey will help determine whether efficacy and interest related to computational modeling are indeed lower for certain engineering disciplines and will inform future studies in this area.
more »
« less
Improving Engineering Mechanics Self-efficacy by Focusing on Abstracting the Physical World as a Precursor to Analysis
Sophomore level engineering mechanics classes typically have high rates of failure or withdrawal. Some explanations posited for this phenomenon include lack of student preparation, the difficulty of the material, ineffective instructional methods, and lack of context. Instructors and textbook authors attempt to overcome these issues with a range of pedagogical approaches such as math reviews, worked examples focused on problem solving processes, “real-world” problems, and active learning focused on physical understanding. However, the first step in the problem-solving process, abstracting the problem, is very often missing. At a fundamental level, engineers follow a four-step design process: (1) Describing or abstracting the physical world with diagrams, words, numbers, and equations (2) Analyzing their model (3) Designing something based on that analysis, and (4) Constructing the designed system. Sophomore mechanics classes traditionally focus on step (2) largely bypassing step (1), instead presenting students with drawings, numbers, and text and teaching them to apply appropriate equations. The goals of this research are (1) to develop a sophomore-level mechanics class that flips the traditional approach by starting with the physical world application and focusing on developing students’ ability to abstract as a precursor to analysis; and (2) to assess if this new approach improves student self-efficacy in basic mechanics. The hypothesis of the proposed research is that, by starting with abstraction, students will build a stronger connection between the physical world and the mechanics modeling. In turn, this will improve student’s perceptions about their ability to solve engineering mechanics problems and their motivation to pursue careers as engineers in the future. The specific research questions we seek to answer are: (1) In what ways does teaching students how to abstract the physical world affect their self-efficacy to solve problems in a basic mechanics class? and (2) In what ways does showing students how to abstract the physical world into tractable engineering science problems affect their future-oriented motivation? We are employing a mixed methods approach that combines quantitative survey data with observations, interviews, and course artifacts to address our research questions. The first phase of our research will establish baseline survey data from statics classes taught in a traditional lecture style that will be compared in future iterations of the course in which students engage in problem abstraction as the first step in the problem-solving process. Results will be presented on the baseline survey data assessing students’ problem-solving self-efficacy and future oriented motivation. In addition to the baseline survey results, we will present example lesson plans, worksheets, class assessments, and an example physical model to illustrate how abstraction will be used in the classroom. Future directions for this project will also be discussed.
more »
« less
- Award ID(s):
- 2306156
- PAR ID:
- 10518930
- Publisher / Repository:
- nemo.asee.org
- Date Published:
- Format(s):
- Medium: X
- Location:
- https://nemo.asee.org/public/conferences/344/papers/41936
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Problem solving is a signature skill of engineers. Here, problem solving is employed when students apply course concepts to reverse engineer YouTube videos and solve new student-written, homework-style problems (YouTube problems). Replacing textbook problems with YouTube problems, this research focuses on examining the rigor of YouTube problems as well as students’ problem-solving skills on textbook and YouTube problems. A quasi-experimental, treatment/control group design was employed, and data was collected and evaluated using multiple instruments. First, rigor of homework problems was examined using the NASA Task Load Index. Also, problem solving was assessed using a previously-developed rubric called PROCESS Problem definition, Representing the problem, Organizing the information, Calculations, Evaluating the solution, Solution communication, and Self-assessment. PROCESS was modified to independently measure completeness and accuracy of student responses, as well as identify errors committed in material and energy balances. In the treatment group, students were assigned ten textbook problems and nine YouTube problems. In addition to obtaining an evidence-based assessment of problem solving via PROCESS, students’ learning attitudes, overall and with respect to problem solving, were measured via a self-reported survey known as Colorado Learning Attitudes about Science Survey (CLASS). Utilizing YouTube problems in classroom did not influence learning attitudes of students negatively. Students reported that YouTube problems possessed similar rigor as Textbook problems. Instead, students solving YouTube problems measured small effect size improvement in problem- solving skills.more » « less
-
It is increasingly critical that engineering students develop proficiency with computational modeling tools, and many curricula include some introduction to such tools during their first year. It is clear that student interest and skill can vary significantly based on prior experiences, but it is less clear whether student motivation specifically related to computational modeling varies as well. This study hypothesizes that the self-efficacy and utility value related to computational methods varies significantly in students’ first year and that engineering students pursuing some disciplines (such as computer, software, and electrical engineering) will begin with a higher initial self-efficacy than others (such as chemical, materials, and biomedical engineering). A survey was used to investigate the utility value and efficacy of approximately 700 undergraduate students in their first year of engineering studies at both a large public institution and a small private institution. Data is analyzed for variations in baseline motivation based on the students’ intended major. This analysis also considers known confounding factors such as gender, race, and prior experience with programming. The results of this survey will help determine whether efficacy and interest related to computational methods vary based on intended major early in an engineering student’s academic career. Ultimately, it is hoped that this study can inform future studies related to what types of interventions might benefit students.more » « less
-
This work-in-progress paper shares findings of the early stage of a 3-year research funded by the National Science Foundation. The major aim of the project is to advance engineering and mathematics (EM) education theory and practice related to students’ self-regulation of cognition and motivation skills during problem-solving activities. The self-regulation includes students’ metacognitive knowledge about task (MKT) and self-regulation of cognition (SRC). The motivational component of self-regulation (SRM) includes self-control of the motivation needed to maintain the level of engagement and deliberate practice necessary for scientific thinking and reasoning. To be effective problem-solvers, students must understand the relationship between the MKT, SRC and SRM throughout the problem-solving activities. Four research questions will guide the research: (1) How do students perceive their self-regulation of cognition (SRC) and motivation (SRM) skills for generic problem-solving activities in EM courses; (2) How does students’ metacognitive knowledge about problem-solving tasks (MKT) inform their Task interpretation?; (3) How do students’ SRC and SRM dynamically evolve?; and (4) How do students’ SRC and SRM reflect their perceptions of self-regulation of cognition and motivation for generic EM problemsolving activities? A sequential mixed-methods research design involving quantitative and qualitative methods are used to develop complementary coarse- and fine-grained understandings of undergraduate students’ SRC and SRM during academic problem-solving activities. Two 2nd year EM courses: Engineering Statics, and Ordinary Differential Equations were purposefully selected for the contexts of the study. One hundred forty two students from both courses were invited and participated in quantitative data collection using two validated surveys during spring 2022 semester. Later in the semester, qualitative data will be generated with twenty students in both courses through one-on-one interviews with students and course instructors, think-aloud protocols with students, and classroom observations. Coarse-grained understandings of students’ SRC and SRM are currently developed through analysis of quantitative data collected using self-report surveys (i.e., BRoMS and PMI). Fine-grained understandings of students’ SRC and SRM will be developed through analysis of qualitative data gathered via one-on-one interviews, think-aloud protocols, classroom observations, and course artifacts gathered as students engage in EM problem-solving activities.more » « less
-
The research and evaluation team of an S-STEM project at a large, research-intensive Southeastern public university conducted a cross-sectional survey as a first step to compare factors which may influence undergraduate student persistence in engineering and computing. All engineering and computing students were invited to participate in the survey, and 282 (10.4%) provided responses. The respondents included 15 high financial need students who were participating in the S-STEM program, of which 7 were first-year students and 8 were sophomores. The remaining 267 respondents were undergraduates ranging from first-year to seniors. Survey questions were adapted from previously developed instruments on self-efficacy, sense-of-belonging, identity, community involvement, and overall college experience. Additional questions related to stress levels, academic life, use and effectiveness of academic supports, and the impacts of COVID-19 on their college experiences. The team compared responses by level of academic progression, declared major, gender, and race/ethnicity. Student responses showed a variety of similarities and differences between subgroups. Overall, the students said that they often attended lectures (in-person or online) and came to class prepared. At the same time, students rated these activities as the least effective academic supports. On the other hand, the students rated working assigned or extra homework problems and studying for exams as their most effective activities. Consistently among the subgroups, the students said their community involvement and identity as developing engineers were relatively low while self-efficacy and team self-efficacy were seen as stronger personal skills. The students said they were highly stressed about their grades and academic success in general, and about finances and future careers. They reported feeling less stress about aspects such as living away from home and negotiating the university social scene. Students reported spending the most time preparing for class in their first year compared to students in later years. Female students (104 responses) reported higher levels of community involvement, engineering identity, and engagement in college life compared to male students (142 responses) while there was little gender-related difference in self-efficacy and sense of belonging. Levels of self-efficacy and team self-efficacy did not show large differences based on year in college. Interestingly, first-year students expressed the highest levels of engineering identity while senior students the lowest. Senior students reported the lowest community involvement, sense of belonging, and engineering identity compared to other students. Overall, students from different races self-reported the same levels of self-efficacy. Black/African American students reported the highest levels of community involvement, college life, and identity. There were no substantial differences in self-efficacy among the different engineering and computing majors. This study is a first step in analysis of the students’ input. In addition to surveying the students, the team also conducted interviews of the participating S-STEM students, and analysis of these interviews will provide greater depth to interpretation of the survey results. Overall, the research and evaluation team’s intention is to provide insight to the project’s leadership in how best to support the success of first-year engineering and computing students. https://peer.asee.org/student-persistence-factors-for-engineering-and-computing-undergraduatesmore » « less
An official website of the United States government

