persistence.Psychosocial Factors Influencing Engineering PersistenceSAT math scores, ACT math scores, high school GPA, first-year college GPA and Calculus-readiness upon college entrance are not the only variables that have been identified asinfluencing engineering persistence. Some scholars have undertaken a psychosocialinvestigative approach into uncovering non-cognitive and affective factors influencingpersistence in engineering (or STEM) degree programs and careers. Students’ contextualidentities in STEM (e.g., engineering identity) are central to many of these investigationsexamining factors influencing STEM persistence [16]-[20]. In particular, several scholars havedocumented the significant, positive influence of students’ engineering identities to their
mathematics programs. Given our institution’s focus on career preparation and real-world problem solving, future offerings present an opportunity to develop students’ interest andbetter meet their needs. In this paper, we will give details about the course and student feedback.Possible curricular and pedagogical changes will also be discussed.IntroductionThis work-in-progress paper discusses the design and implementation of a “Dynamical Systemsand Chaos” course as an upper-level undergraduate elective at Wentworth Institute ofTechnology. The course can serve as a technical elective for majors and minors in appliedmathematics, with many students in engineering or computer science majors pursuing this minor.The course material combines topics from
of the difficult series of math courses required for an engineering degree and thenegative impact it has on underserved populations of students, this work-in-progress researchbegins to explore the effects of math courses on students who do not enter collegiate engineeringprograms with the traditionally expected math readiness. This case study narrative inquiryhighlights trends for this type of student during year one – when retention is the lowest - as partof a larger study that will follow students through their entire collegiate career. While“traditional” engineering students come into most engineering programs ready to start mathcoursework at the calculus level, some students who elect to pursue an engineering degree do nothave the test
universal basis, suggesting that everyone can developsome level of interest in the subjects they are learning [12]. Therefore, fostering math interest iscrucial for motivating individuals to pursue engineering careers and engage in engineeringlearning [9]. Moreover, interest plays a pivotal role in the development of a positive STEM self-concept [8]. When individuals have an interest in STEM, they are more likely to seek outinformation and opportunities to engage in STEM activities, further contributing to their self-concept [8]. Therefore, we expected math interest to impact course grades, even after accountingfor engineering self-efficacy.Math Self-Concept Math self-concept relates to an individual's self-perception of their competence
value agree+ disagree+ strongly agree strongly disagreeMATLAB will beuseful in my future 112 4.78 4.37 *** 2.6217 .0099 59% 28%courseworkI can see myselfdoing a project in the 2.0853 .0393 112 4.60 4.26 ** 55% 32%future that utilizesMATLABMy future career will 1.7186 .0885likely include work 113 3.85 3.58 * 32% 49%with
does not prepare engineering students forfurther coursework and careers in engineering. At our engineering school, we offer a traditionalthree-semester calculus sequence with 3 different starting points. Depending on their priormathematical background, students have the option to begin their first semester with Calculus I,Calculus II, or Multivariable Calculus. In 2016, a two-semester honor’s engineering mathsequence was developed for the students with the strongest math background who wouldtypically begin with Multivariable Calculus in their first semester. The sequence enhances thetraditional calculus curriculum by addressing gaps in Calculus I and II skills, providing a morein-depth exploration of Multivariable Calculus topics, and
© cube. Users are then able to modify the orientation of theAR model in response to the user rotating or translating the cube. The findings of the studysuggest that AR improved students' spatial reasoning, facilitated the development of shiftsbetween mathematical and physical reasoning, and decreased cognitive load.The AR system developed and evaluated in this paper can be implemented by curriculum andeducational designers at any level, from K-12 to university to professional career training in anySTEM field.IntroductionStudents often face challenges with learning abstract concepts and spatial visualization,particularly when engaging with new 3D content in physics and engineering [1-3]. Thesedisciplines rely heavily on foundational knowledge
to education, sense of community, retention, college transitions, living-learning communities, career readiness, mentoring and persistence to graduation for students in STEM programs.Rachid Ait Maalem Lahcen, University of Central Florida ©American Society for Engineering Education, 2025 Accelerating Student Success in Mathematics through Personalized Adaptive LearningAbstractMath Launch is a program designed to help incoming first-year students prepare for calculus 1and set them up for success in their chosen STEM major. With a focus on expanding students’knowledge and capabilities in algebra, trigonometry and precalculus, Math Launch helpsstudents become calculus ready in
interested in engineering who started in Precalculus ended up majoring in engineering),and the majority (68%) of those who placed into Single Variable Calculus also left engineering.While retention increases to 56% and 59%, respectively, for students who placed intoMultivariable and Vector Calculus, there are still many students leaving at this point. Dartmouthengaged in an extensive self-study in 2022 to better understand how aspects of the STEMecosystem attract, retain, or deter students from historically underserved groups from pursuingSTEM courses, majors, and career paths in these fields. The following main issues related toDartmouth STEM courses were identified (Char and Jewiss, 2022): ● Courses are too theoretical, with little context or
. It is also core to the understanding of numerous probability distributions instatistics, hence, fundamental knowledge of this concept is crucial for a successful career inscience, technology, engineering, and math (STEM). The proposed experiment will ease thecomplexities involved in the learning of calculus students by using experimental centric pedagogy(ECP), which entails providing simple yet relevant experiments that would boost the students’interest in this field. The concepts of differentiation and integration would be practicallydemonstrated to students using Hooke’s law, velocity, acceleration with respect to time, and rulerexperiment. The project would employ readily available utilities to demonstrate integration anddifferentiation
engineering early in their academicpathway. However, while the class connects students to peers, campus resources, and morecontext for what a career in engineering might look like, it does not actively incorporate largeportions of the math curriculum as other first year programs have attempted [6]. Traditionally,students who place into Intermediate Algebra (MATH 099) in the fall of their first year ofcollege must take this course as well as a two-part Precalculus sequence (MATH 141 and MATH142) before being ready for a Calculus 1 (MATH 151) class. Students can enroll in ENGR 101concurrent with MATH 141.The Engineering in Context learning community changes this sequencing by offering students amultidisciplinary cohort experience over two quarters [7
. ● Professional development for teachers: Providing ongoing professional development for STEM teachers to enhance their teaching methods and better support students’ learning needs. ● Ground in applications: In STEM, there is more emphasis on academic mastery of concepts, rather than career applications and relevancy. Cited sources indicate that mathematics studied independently of applications remains abstract, dull, and difficult. They also show that instructional practices need to be adjusted to meet these challenges.DiscussionCertain common themes emerge from the studies found despite the variety of math topics addressed.Students' tendency to carry misconceptions through multiple courses speaks to the persistence
Paper ID #45610WiP: Metacognitive and social-emotional-learning interventions in first-yearCalculusMaureen Tang, Drexel University Maureen Tang joined the faculty of Chemical and Biological Engineering at Drexel University in 2014 and obtained tenure in April 2020. She received her BS in Chemical Engineering from Carnegie Mellon University in 2007 and her PhD from the University of California, Berkeley in 2012. Dr. Tang completed postdoctoral work at Stanford University and research internships at Kyoto University, the University of Dortmund, and DuPont. She is the recipient of a NSF CAREER award. Her research at Drexel
on the changing academic needs of the students withincreasing focus on career development in later years. Future studies to understand the fullimpact of the SS Program over the course of their academic tenure are expected.Given the measured success of the SS students in their first quarter, the expansion of anintegrated math and engineering peer mentor led SI program could be worthwhile. Bringing thesupport this program provides to a broader range of incoming students in the first-yearengineering curricula may have an impact beyond this small subset of students leading to apositive effect on grades and retention rates on a larger scale.Acknowledgement of Support and DisclaimerThis material is based upon work supported by the National Science
effectiveness of these methods wasdemonstrated in accurately calculating velocity, displacement, and higher-order derivatives likejerk. The study underscores the importance of proper noise handling and drift correction forachieving precise results when using sensor data to predictive analysis. Overall, incorporatingaccelerometer data into numerical methods education equips students with valuable analyticalskills and technical proficiency, preparing them for future careers in various engineeringdisciplines.References[1] Pendrill, A. M., & Eager, D. (2020). Velocity, acceleration, jerk, snap and vibration: Forces in our bodies during a roller coaster ride. Physics Education, 55(6), 065012.[2] Musto, J. C. (2002). A project-based approach
Res., vol. 10, no. 1, pp. 381–391, Jan. 2021, doi: 10.12973/eu-jer.10.1.381.[32] W. Schneider and C. Artelt, “Metacognition and mathematics education,” ZDM Mathematics Education, vol. 42, no. 2, pp. 149–161, Feb. 2010, doi: 10.1007/s11858- 010-0240-2.[33] D. T. Conley, College and Career Ready. San Francisco, CA, USA: Jossey-Bass a Wiley Imprint, 2010. doi: 10.1002/9781118269411.[34] G. M. Maruyama, Basics of Structural Equation Modeling. Thousand Oaks, CA, United States of America: SAGE Publications, 1997.[35] D. L. Jackson, J. A. Gillaspy Jr, and R. Purc-Stephenson, “Reporting practices in confirmatory factor analysis: An overview and some recommendations,” Psychological Methods, vol. 14, no. 1
Alejandro Mejia, University of Cincinnati Dr. Joel Alejandro (Alex) Mejia is a Professor of Engineering Education in the Department of Engineering and Computing Education at the University of Cincinnati. His work examines the intersections of engineering, social justice, and critical pedagogies. He focuses on dismantling deficit ideologies in STEM, centering Latino/a/x student experiences—especially of those along the U.S.-Mexico border. His work draws on Chicana/o/x studies, raciolinguistics, and bilingual education to explore how language, race, and socialization shape engineering pathways and engineering practice. In 2025, Dr. Mejia received the Presidential Early Career Award for Scientists and Engineers (PECASE
profession. Later on, thisreference was extended to other university careers, that is, to undergraduate programs inwhich this science is useful, but that do not have as an objective to train people who will havemathematics as a future area of professional activity.According to Camarena [15], unlike most educational theories that focus on teaching andlearning in Basic Education, this theory began at the university level, from questions thatstudents made about the teaching of mathematics, more specifically in the Engineeringprogram. The students asked questions such as: "Why do we study this content?", "Where dowe apply what we are studying?", "How does this content help me?", and so on. According toLima et al [16], based on [15], these questions
higher education research. The types of transitions include Transition as Induction (T1), Transition as Development (T2), and Transition as Becoming (T3). 1, Transition as Induction, describes the pathway that students take by moving into higherTeducation. This often describes the transition from high school to college, but other circumstances could be considered. Students who experience this type of transition must navigate the structures, systems, and policies of the institution. From here on out, this will be referred to as “Transition to the University.” T2, Transition as Development, describes students' life stage and their transformation from one identity to another (i.e., major, career, etc.). Students who