Model for the Secondary-TertiaryTransition in Mathematics developed by Clark and Lovric [1],[2] is completed. The theoreticalmodel proposes that the secondary-tertiary transition in mathematics is a rite of passage [1],[2].During the transition, students may struggle due to differences in high school and collegemathematics [1],[2]. Because of this struggle, mathematics is commonly characterized as agatekeeper to Science, Technology, Engineering, and Mathematics (STEM) degrees [3].Therefore, for mathematics-heavy STEM majors, such as engineering, mathematics courserequirements could inhibit STEM degree completion. By better understanding the secondary-tertiary transition in mathematics, student accessibility to college mathematics could
engineering students to leave their degree program aretheir first mathematics courses [1], upon which all subsequent engineering concepts rely. Beyondmastering foundational calculus concepts and their practical applications, engineering studentsare honing their skills in mathematically framing, executing, and articulating solutions withindiverse problem-solving contexts [2]. While success in these endeavors is often connected tocognitive predictors such as the student’s GPA and past academic success, test scores, andintelligence [3], they only account for about 15% of the variance in academic success [4]. Incontrast, non-cognitive predictors, generally defined as those skills, attitudes, beliefs andstrategies that affect academic performance but are
) Award for his contributions to engineering education. ©American Society for Engineering Education, 2025 The Role of Mathematical Modeling in Integrating Disciplinary and Societal Knowledge: An Epistemic Network Analysis StudyIntroductionMathematical modeling is a critical component of the engineering design process [1]. Since thedesign process distinguishes engineering from other disciplines, mathematical modeling plays afundamental role in engineering practice, allowing engineers to describe, analyze, and predicttheir designs [2]. These mathematical models contribute in addressing questions that arise duringthe engineering design process [3]. Mathematical modeling is essential for
1and more accessible understanding of the function, moving away from abstract ex-planations. Instructors may choose to incorporate these materials into their lessonsto achieve similar outcomes. This paper should be viewed as a work in progress. The material presented is notintended to replace any existing curriculum or textbook chapters but rather to serveas a supplementary resource, offering a deeper and more intuitive understanding ofthe concepts. The content was introduced to students in three classes, followed by a detailedquestionnaire: 25 students in the undergraduate course ”Circuits 1,” 43 students inthe undergraduate course ”Stochastic Models for CS,” and 8 students in the graduate-level course ”Modern Control.” The feedback
improving the retention of under-performingstudents, but these tools are too labor-intensive for faculty to apply in large introductory courses.Additionally, many struggling students are limited by non-cognitive factors such as fear offailure, social anxiety, and general overwhelm. There is a need for large-format, scalableinstructional tools that both engage students in course material and address non-cognitive factorsin an appropriate way.This Work In Progress will present the effects of a remedial intervention, the “reflectiveknowledge inventory”, at improving student outcomes in Calculus 1. In the intervention, studentsimprove their exam score by submitting a “reflective knowledge inventory”. Expert learnersknow that new skills are best built
, relying on the Views about Mathematics Survey(VAMS), a standard instrument for collecting data on student views. The second one conducted byHyland (2020), employed a 2+1 structure, consisting of two lectures followed by a tutorial orrecitation each week.In contrast, our institution uses a fully worksheet/tutorial-based approach with no formal lectures.Additionally, while Hyland’s research questions focus on the changes made to their tutorialmethods, our study centers on students’ perception about the IBL method, drawing on Laursen andRasmussen’s four pillars of Inquiry-Based Mathematics Education (IBME), Laursen (2019).Research questions:Specifically, we investigate students’ perceptions on the following research questions: 1. To what extent
theirdeeper interests, but far too many leave engineering because of gatekeeper courses. Rather thanallowing students to explore engineering, the gatekeeper mathematics courses discouragestudents from continuing to engineering; and the lower students’ math placement, the longer theymust wait to experience engineering, as they slog through courses taught by and formathematicians. Figure 1. Number of students who left versus persisted in the engineering major, by math placement (cumulative data from Dartmouth graduating classes of 2014 through 2023)Figure 1 paints a dire picture around these inequities: Students at Dartmouth with an engineeringinterest who placed into Precalculus were almost never retained in engineering (only ~5% of thestudents
engineering.IntroductionThe author has observed that many engineering majors perceive the calculus sequence as ahindrance to their true interests in engineering. This perception is puzzling, given that calculus isa foundational component of any engineering curriculum. However, there is evidence supportingthe author’s observation that the way calculus is traditionally taught does not always align withthe motivations of engineering students [1]. These students are often more responsive toinstruction that emphasizes real-world relevance and concrete problem-solving, rather thanabstract theory. A lack of such practical emphasis in mathematics courses has been identified as afactor contributing to student attrition in engineering programs [1]. In response, the author
a single semester through an accelerated boot camp style,math course. Students selected to participate in the Math Launch pathway begin their first term(fall or spring) as part-time students (9-11 credit hours) and are provided with a dedicatedsupport team to assist them throughout their first semester math course. Math Launch givesstudents in calculus critical majors the opportunity to prepare for calculus 1 through a structured,accelerated program while providing additional services to support them in their designated mathcourse. Students register for a 3-credit hour math course (MAC1906 – Independent Study) andhave the opportunity to master three subject areas (courses), potentially becoming calculus 1ready by the beginning of their
functions by exploring axial deformation under tension in barsof variable cross-sectional area. The paper discusses these examples and others along with theoverall sequence of labs, how they intersect with the concurrent engineering courses or previewfuture engineering/physics courses, and how they fit together as a whole to support both theprecalculus course learning outcomes and the larger goals of the learning community experience.We also share initial student feedback on the lab activities.IntroductionPlacing into an algebra or precalculus course can be a “death sentence” [1] for some students’goals to study engineering as it means they must wade through quarters, if not years, ofprerequisite material for which they might see little relevance
example, numericalintegration is used to estimate velocity and displacement from accelerometer data, which iscrucial in fields like automotive crash testing. In crash testing, accelerometers captureacceleration during an impact. By integrating this data, engineers can calculate velocity anddisplacement to assess vehicle deformation and passenger movement, informing the design ofairbags, seatbelts, and crumple zones. Additionally, numerical differentiation allows for thecalculation of higher-order derivatives, including jerk, snap, crackle, pop, and lock (first to fifthderivatives of acceleration) [1]. Among these, jerk—the rate of change of acceleration—isparticularly important in crash safety. High jerk values indicate sudden changes in force
lecture time for interactive programming exercises andcollaborative problem-solving. Peer Learning Group (PLG) sessions also provide extra opportunities forpractice and peer-assisted learning.Preliminary feedback and assessment data suggest that this project-based approach significantly enhancesstudents’ understanding of mathematical and computational concepts and their ability to apply them inengineering contexts. By integrating MATLAB programming with real-world applications, the courseprepares students with both the theoretical foundation and practical expertise required for advancedcoursework and professional engineering challenges. 1. Introduction:The growing complexity of engineering problems requires students to master computational
welcome constructive feedback from the audience to further refine and improve ourapproach. Through shared insights, we hope to enhance the effectiveness of our approach andultimately improve outcomes in mathematics courses for engineering students. In futureiterations, we aim to explore, and measure how context-based education support the overall well-being of learners throughout their educational journeys. 1. IntroductionToday's most pressing challenges, such as climate change, are often called complex or wickedproblems because of their complexity and interconnectedness [1,2]. Solving such problemsdemands professionals who can effectively balance their specialized knowledge with a broaderperspective. Particularly related to engineering
, Integral,Power Series, and Function ConceptsAbstractPower series is a concept that requires knowledge of extensive calculus sub-conceptual knowledgethat includes rate of change and antiderivative knowledge and the pedagogical efforts to measureconceptual understanding of STEM students’ is recent ([1]-[9].) If and only if (Iff) is one of thepedagogical techniques introduced in [10] to analyze calculus questions and educators areencouraged to use this technique to structure questions. In this work, we utilize iff methodologyintroduced in [10] and analyze empirical data collected at a university located on the Northeasternside of the United States. The research received Institutional Review Board Approval (IRB) tocollect written and interview data
understanding. This often gives rise toundesirable behaviors in our students, such as superficial learning and disengagement [1], as wellas undesirable outcomes. “The chair of the department of a Big Ten university once observed, probably after a bad day, that it was possible for a student to graduate with a mathematics major without ever having solved a single problem correctly. Partial credit can go a long way.” —Dudley Underwood [2]Recently, in an effort to combat these problems, segments of the mathematics community havebegun to employ grading techniques[3] other than WAG in their courses, known collectively asalternative grading. While these approaches to
leaving learners in a state ofliminality, a transitional phase marked by incomplete and inauthentic understandingcharacterized by reliance on memorization [1,2]. Overcoming these "stuck places" demands notmerely the acquisition of knowledge but an ontological transformation, fundamentally reshapingone’s way of thinking and being [3]. Examples of threshold concepts in mathematics includelimits [1,4], complex numbers [1], mathematical proofs [3] and functions [5,6].Functions as a Threshold ConceptFunctions pose two key troublesome aspects that contribute to their nature as a thresholdconcept. The first is their representational complexity, requiring students to integrate andtranslate between various modes, including symbolic, graphical, tabular 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
by ASEE in 2024 suggests that the way collegiate engineeringeducation programs currently employ mathematics coursework is inherently problematic andrecommends that educators no longer allow the sequential calculus courses required by mostengineering programs to serve as a weed-out series for students interested in engineering [1].Instead, it recommends that “every motivated student [should] have a path to success, increasingthe number and diversity of students earning engineering degrees by removing math as anartificial barrier to the engineering career [1].” This ideology is supported by its notion that muchof the content of upper-level math courses required for an engineering degree is not needed bypeople who practice engineering after
avoid using advanced mathematical concepts in theircourses.1. Introduction In engineering students’ early college experience, math courses often pose a point ofstruggle. While some students see them as a “gateway to engineering”, others view them as“gatekeepers” [1]. Math courses, particularly, calculus-focused courses, are often perceived withboth scaffolding and litmus properties, which on one hand, prepare students for their higher-levelSTEM education and, on the other, filter them out [1], [2]. However, the effects of filtering arefelt more prominently than those of scaffolding [2]. Hence, these math courses often lead to highdropout rates in the initial years and continued challenges in the later years through application-related
the literature. Some of these misconceptions havebeen shown to persist between high school students and university students. Improved strategies inclarifying misconceptions to students have also been reported, ranging from individualized remediationto course level strategies. The content of this review should serve as a concise starting point for contentdevelopers and instructors to help engineering students who struggle with math in their curriculum, andto provide specific misconceptions to target in efforts to remediate math understanding for thesestudents.IntroductionA large body of literature exists on math misconceptions (e.g.[1] ) and remediation at primary andsecondary levels of education, and is of great value for informing
available for ordinary diAerentialequations and linear algebra. I also want to investigate how much of a role, if any, havingtaken courses in multivariable calculus, ordinary diAerential equations, linear algebra, andpartial diAerential equations, plays in course grades, and whether time since having takenthese classes has an impact. ReferencesBrelin-Fornari, J. (2003, June), Comparison Of Math Skills To Final Course Grade In A MathIntensive Dynamic Systems Course Paper presented at 2003 Annual Conference,Nashville, Tennessee. 10.18260/1-2—12465Loch, B., Jordan, C. R., Lowe, T. W., & Mestel, B. D. (2014). Do screencasts help to reviseprerequisite mathematics? An investigation of student performance
students experiencing a slight decline indisciplinary identity and URM students reporting reduced disciplinary sense of belonging overtime. These trends, though not statistically significant, highlight the need for targeted efforts tobetter support these groups.Keywords: Calculus, mastery grading, long-term effects, student success, student perceptionIntroductionAlternative grading practices have been increasingly adopted in STEM education due to its focuson student growth and well-being. Mastery grading, as one of the alternative grading approaches,breaks course material into specific learning targets, and students are allowed multiple attemptsto demonstrate mastery in each learning target [1]. The goal is to create a supportive andinclusive
differential equations, linear algebra, computerprogramming, and more broadly, mathematical modeling of physical and social systems. Assuch, it provides a domain for students to synthesize their mathematical knowledge and apply itto real-world problems.Many examples of dynamical systems are most clearly related to topics in engineering and thenatural sciences. However, we expect opportunities for computer science and data science majorsto gain from this course as well. Previous discussion has highlighted some of the connectionsbetween computer science and the study of dynamical systems [1]. Students may find itinteresting to experience how computational tools and scientific programming are used inmathematical modeling and related research; meanwhile