Can a Five-Minute, Three-Question Survey Foretell First-Year Engineering Student Performance and Retention?

Improving the creativity in introductory engineering course applying fuzzy network process: A pilot study

Introductory engineering courses can benefit from the flipped classroom model as these courses develop hands-on skills such as engineering design and critical thinking skills. Providing students with instruction outside the classroom with e-learning tools frees up class time to engage in activities that promote deeper learning, e.g. classroom discussions, problem-solving sessions, design activities, etc. In this paper, we discuss the implementation of a flipped classroom version of an introductory engineering design course. We evaluate student performance and perception of a flipped classroom version using data collected during the course. We also discuss elements of the course that were found to be the most beneficial for the students.

A discipline-specific first year introductory course has been the norm in engineering education. Most of these courses are often theme-based and use engineering design as a device to emphasize the relationship between the various components of a field like Electrical and Computer Engineering. Online learning has also been incorporated in such common courses to enhance student experiences. As an alternative to a first year discipline-specific engineering course, there have been recent efforts to develop common first year engineering courses that target all engineering disciplines. One rationale for such a common courses has been an early instillation of common engineering education elements like ethical and social dimension of engineering design. A problem-based curriculum is often used in such first year introductory engineering courses to also ensure that students find relevance in the Physics and Math course being taken in the first two years of engineering. Students are also challenged to work on a design project in a multi-disciplinary team. A college wide engineering course allows students to learn about various engineering disciplines and wisely choose an engineering discipline. In this paper, we introduce the "Introduction to Engineering" freshman course offered to all students enrolled in the College of Engineering. The course consists of lectures, labs, recitation sessions, and a competition. Students are introduced to the major responsibilities of engineers, presentation skills, documentation, teamwork, problem solving, design process, ethics, and the basic concepts in each of the six engineering disciplines offered in the College. We evaluate the course outcomes by collecting and analyzing the student's feedback over 2 years. The results are very encouraging and reflect a positive learning experience. We share some of the challenging multidisciplinary project ideas offered to the students. We also discuss the topics covered in the course and the relationship of each topic to the ABET criteria.

Introductory engineering courses that include computer science components—particularly for non-computer science majors—often provoke anxiety, especially among students with limited or no prior programming experience. This apprehension is exacerbated by the presence of peers who possess advanced coding backgrounds. To address these challenges, this study explores the implementation of assignment choice and competency-based grading in a Computer Science I (CS-1) service course. Grounded in Self-Determination Theory, the intervention allows students to select from multiple assignment pathways, each aligned with the same core learning objectives. This autonomy is intended to promote student agency, intrinsic motivation, and engagement while still ensuring mastery of fundamental programming concepts. A mixed-methods design compares two parallel course sections: one using traditional instructional methods and another offering assignment choice and competency-based assessment. Quantitative data includes standard performance metrics, such as D, F, and withdrawal (DFQ) rates, while qualitative data is drawn from student feedback and course evaluations. Results indicate that students in the assignment choice section report increased satisfaction, deeper engagement, and a stronger sense of ownership over their learning. The section also showed a reduced DFQ rate, suggesting improved student retention. The approach was especially effective for non-CS majors, many of whom felt more confident and capable navigating the course content at their own pace and with personalized relevance. This study contributes a flexible instructional framework that preserves rigor while promoting student-centered learning. The findings support the use of assignment choice and competency-based grading as strategies to improve student outcomes in engineering service courses involving programming. Implications for scaling and adaptation to other technical domains are also discussed.

We propose and execute an introductory engineering course for investigating the history of technology and the philosophy of technology. Soft...

Written communication is an important, however often neglected, component of an engineering curriculum. The civil engineering department at North Dakota State University has included word processing in its CE 101 course in order to teach students to write reiteratively and to gain an appreciation for the convenience associated with word processing. Several conclusions were drawn from this first attempt at including word processing in an introductory engineering course. Student comments were generally favorable and they appreciated the exposure to microcomputers. The shareware program that was distributed to each student worked well, but the necessity for each student to purchase the full documentation became evident. Finally, the classical lecture style of presentation was not adequate and will be replaced by smaller computer lab sessions and hands‐on experience.

The objective of this study is to develop and incorporate a multidisciplinary engineering design experience into an academic success and professional development course that aims to retain non-calculus-ready first-year engineering students. The project followed the five-step engineering design process using knowledge from multiple engineering disciplines. Students were tasked to design a scale model of a safe, sustainable, and cost-efficient oil derrick with PASCO kits, engage in discussion to consider societal, global, cultural, and further factors in design, practice an elevator pitch with entrepreneurship specialists from the university start-up incubator, and present the final design to a multidisciplinary judge panel from academia and industry in engineering, math, social science, and business at a Poster Expo. This project-based learning aligned with the student outcomes of ABET and the Engineering for One Planet framework for sustainability education in engineering. Opportunities and challenges of this multidisciplinary learning experience were analyzed using triangulated data sources from student course performance, a student perception survey (N = 16; Cronbach’s α = 0.959), and student retention data. Results showed a positive student learning experience with 88% of students reporting that the multidisciplinary design experience was positive to their learning and increased their interest in engineering. Ninety-four percent of student retention in engineering was reported by the end of the semester (N = 17).

The aim of this study is to undertake an empirical inquiry and comparison of the effectiveness of various classifiers with ensembles classifiers in the prediction of student academic performance. A single classifier algorithm will be compared against the performance and efficiency of ensemble classifiers. Reducing student attrition is a major problem for educational institutions all over the world. The search for solutions to increase student retention and graduation rates continues for educators. This is only possible if at-risk students are identified and intervened with as soon as possible. However, the majority of regularly used prediction models are inefficient and inaccurate as a result of inherent classifier limitations and the inclusion of insignificant inputs in their calculations. The majority of data mining and machine learning researcher focused on developing an algorithm that can extract useful information from massive amounts of data after being processed by a computer. The most difficult problem in predictive modelling is identifying the most effective prediction algorithms that are also accurate enough to be useful. Therefore, a multi-level homogeneous ensemble predictive (MLHoEP) model is designed, which uses the different techniques of data mining like feature selection, ensemble learning techniques like boosting and bagging. Seven distinct machine learning algorithms were used on this model to predict and analyse the academic performance of the students. The performance of the classification algorithms in terms of prediction was evaluated using k-fold cross-validation. The study contributes to the body of knowledge by suggesting the development of homogeneous classifiers that may be used to accurately predict students’ academic success. It also proposes the construction of homogeneous classifiers, which may be deployed for accurate student performance prediction, in order to provide a better explanation for the poor performance prediction. As a result of this research, it has been demonstrated that the technique of applying homogeneous ensemble approaches is incredibly efficient and accurate in terms of predicting student performance and assisting in identifying students, who are in danger of dropping out of school. The study compared the accuracy and efficiency of single classifiers to ensembles of classifiers in terms of performance. It was discovered in the research that a homogeneous model with excellent accuracy and efficiency might be developed for anticipating student performance. These key problems have been successfully addressed by the findings of this research study: Which characteristics of students are the most effective predictors of academic performance? How accurate are approaches such as bagging and boosting ensembles for predicting student academic performance? The approach offered in this study will aid educational administrators and policymakers in designing new policies and curriculum-linked to student retention in higher education. This research can also aid in the identification of students who are at risk of dropping out of school early, providing for timely intervention and support. Prospective research will examine the creation and implementation of an automated prediction system known as the students’ academic performance forecast framework, which will collect data from students via online submission and produce a prediction result for their academic performance.KeywordsEducational data miningEnsemble learningMultilayer perceptronRandom forestNaïve BayesCorrelation attribute evaluationInformation gainGain ratio

Spatial ability, such as the ability to correctly visualize three dimensional objects when they are represented in two dimensions (such as in computer-aided design (CAD) software or in a detailed part drawing), is an essential skill for engineers. Research has shown that spatial ability is positively correlated with retention and performance of students within other disciplines which rely on spatial ability, (e.g. chemistry). However, whether spatial ability affects the retention of students in engineering has been disputed in the literature. Furthermore, research has not yet been conducted which assesses the relationship between self efficacy, that is the student's confidence, with respect to spatial skills and retention. In this paper, results of both spatial ability, using a subset of the Purdue spatial visualization test: rotations (PSVT:R), and self efficacy, using a developed test for this research, was presented for over 200 students from five different engineering disciplines. Statistically significant differences were found for gender, type of CAD training, class standing and whether or not the student is declared or undeclared in engineering with respect to self efficacy and/or spatial ability

Roudkovski was a faculty member and director of Counseling at a state university in Alabama.As director of Counseling, she was responsible for coordinating retention efforts for all programs and coordinated necessary efforts and interventions to retain students in jeopardy of leaving the university.Additionally, Dr. Roudkovski

A growing trend in the recruitment of undergraduate students to engineering programs atCanadian universities is to establish an international presence at a branch campus in a different part of the world. This paper focuses on an example case study of the international collaboration of an introductory engineering course called “Sustainability in Engineering Design” between the University of Prince Edward Island (UPEI) and the Universities of Canada (UofCanada-Cairo) inCairo, Egypt. This course set out as being foundational in the implementation of an identical, international offering of the UPEI Faculty of Sustainable Design Engineering’s (FSDE) Bachelor of Science in Sustainable Design Engineering (BSc-SDE) program at the Cairo campus. This paper describes the implementation of the course at the branch (UofCanada-Cairo) campus and the coauthors, as instructors, provide a self-assessment of the success of the coordination to deliver the learning outcomes for the course. An assessment of how well the main graduate attributes (i.e. GA9: Impact on Society and Environment; GA6: Individual and Team Work, and GA13:Lifelong Learning) linked to the course’s learning outcomes were achieved in developing a sustainability mindset in UPEI FSDE undergraduate students will be presented and recommendations for the course in the context of international collaboration will be discussed.The projects' assessment also affirms that students at both campuses can work in teams to evaluate the impact of current engineering designs on society and environment and design more sustainable products in the future.

Stanley Pogrow shares a decade's worth of experience in developing an approach to teaching math to upper-elementary and middle school students that simultaneously increases basic skills, improves problem solving, raises test scores, and sparks student interest in math. For details, read on. MY WORK over the past 24 years has focused on using technology to produce more sophisticated forms of learning after the third grade. This work started with the Higher Order Thinking Skills (HOTS) program for Title I and learning-disabled students. (For more information on the HOTS program, visit www.hots.org.) We used lessons learned from this pioneering effort as we developed Supermath, an alternative approach to teaching math to all students. Supermath provides interesting opportunities for districts to better meet the accountability challenges of No Child Left Behind (NCLB) while also producing reflective mathematics learning as celebrated in the November 2003 issue of the Kappan. Background Of Supermath In the early 1990s, the National Science Foundation (NSF) funded the development of Supermath as part of an effort to produce new and innovative approaches to and materials for teaching mathematics. In designing Supermath, we sought to develop an approach that would simultaneously increase students' basic skills, problem-solving ability, test scores, and interest in math. We aimed to achieve these results for both advantaged and disadvantaged students and to be far more successful in doing so than conventional approaches, even those that used technology, had been. Given these ambitious goals, it was clear that the Supermath materials would have to be creative and engaging and would have to incorporate and systematize powerful curricular and pedagogical techniques. The use of technology to improve learning has traditionally focused on presenting content in an individualized fashion and then testing students. Fancy graphics and animation are used to intrigue the students. Technology serves to automate instruction rather than to create new forms of instruction. While this approach has value, it generally produces gains only in the early grades -- and primarily on the program vendor's test. There are more creative simulations and tools available on which to build new approaches to instruction. But, while valuable and able to provide for exploratory learning, these technologies tend to be hit or miss and place heavy burdens on teachers to figure out how to use them effectively. It is also hard to demonstrate the impact that these simulations and tools have on improved content skills and test scores. HOTS demonstrated that it was possible to use technology systematically in ways that produced progressive outcomes -- comprehension and self- concept -- while also producing even greater gains on traditional achievement measures, such as test scores. The primary lesson from HOTS is that developing a more powerful approach to learning requires insight into what is inhibiting learning and ideas about how to overcome these inhibitors. Then, and only then, should one think about using technology to improve instruction and student performance. In the case of HOTS, I discovered that for disadvantaged students the key inhibitor of reading comprehension and general learning was not understanding understanding, a problem that could be addressed only through intensive conversations about ideas.1 Based on this insight, HOTS uses technology to provide students a common experience that serves as the basis for shared conversations, much as a family uses life experience as the basis for dinner-table conversation. In this approach to technology use, curriculum and training were designed to generate an environment conducive to sophisticated and intensive Socratic conversation and to maintain it over the one- to two-year period needed to develop students' sense of understanding. Using technology indirectly to promote conversation, as a table setter so to speak, worked powerfully. …

As one component of a college-wide redesign of its introductory engineering courses, a project-based introduction to computer science is being developed using open-source software running under the GNU/Linux operating system. The course components include hardware assembly, operating system and software installation, basic system administration, and a working introduction to the engineering design process through the completion of several collaborative projects involving content-based graphic design and computer animation. Through the three semesters, the course has gravitated away from a module-based survey of fundamental concepts in computer science and engineering toward greater student exploration of fewer topics most relevant to the projects. Continuing assessment of each phase of the course provides formative and summative feedback and has played an important role in the maturation of the course.

This paper reports on a study that was performed over a 4-year long period in which the performance of undergraduate mechanical engineering students on a team project, enrolled in a senior mechanical systems course at the University of Rhode Island, was correlated with their learning styles as measured by the Brain Dominance Model. To measure the learning style of each student, the Brain Works program, developed by Synergistic Learning Incorporated, was used in this study due to its ease of administration and explanation of results. The students were asked to report to the instructor the two numbers that the program generated: one is a left/right brain measure and the other, an auditory/visual measure. In the first two years of this study, the 4-5 members of each team were grouped based on their learning styles score with the objective of forming teams with members whose scores are in three or more different quadrants of the left/visual plane. In the last two years, the teams were formed randomly, but the students were asked to report their learning styles scores. Data was also collected on the performance of each student in the course and in the team project. To determine if the learning styles have any correlation to the performance of the team, a correlation analysis was performed on combination of many variables some of which are exam grade, project grade, and composite learning score for the team. The results show that the competence level of the team as measured by the exam grade has the most influence on the team performance, while the learning style makeup of the team has a less pronounced effect.

The EXCEL Program began as a National Science Foundation-sponsored STEM Talent Expansion Program in 2006 and, because of its significant impact on retention of STEM majors, has since become an institutionalized program at the University of Central Florida. The University of Central Florida EXCEL Program annually recruits approximately 200 first-year STEM students into a learning community with residential/social and curricular components. First-year retention, long-term retention, and graduation rates are all higher for the EXCEL cohorts than the comparison groups. Overall retention of students in a STEM major is 43% higher for the EXCEL Program than the comparison group. In particular, women, African Americans, and Hispanics in the EXCEL Program have demonstrated high retention (54%, 57%, and 67%) and graduation rates (35%, 27%, and 50%). The large cohort size and all-inclusive nature of the EXCEL Program makes it a unique model for addressing the current need for STEM graduates.