studies materials and processes for electrochemical energy storage and conversion.Dr. Jennifer S Atchison, Drexel University Jennifer Atchison received her Ph.D in Materials Science and Engineering in 2012 from Drexel University. Dr. Atchison’s professional interests include nanofibrous textiles, engineering design, engineering education, especially active learning, diversity, ©American Society for Engineering Education, 2025 Work In Progress: Metacognitive and social-emotional-learning interventions in first-year CalculusAbstractStudent performance and retention in STEM majors is a major concern in higher education.Individual attention and coaching are effective at
during the students transition to college.Dr. Megan Che ©American Society for Engineering Education, 2025 A Citation Analysis of the Theoretical Model for Secondary-Tertiary Transition in MathematicsKaren C. Enderle Dr. S. Megan Che, Ph.D.Dept of Teaching and Learning Dept of Teaching and LearningCollege of Education College of EducationClemson University Clemson University,Clemson, SC, USA Clemson, SC, USAIntroductionIn this conceptual essay, a citation analysis of the Theoretical
engineering students as well as other engineering education efforts.Dr. Patricia A Ralston, University of Louisville Patricia A. S. Ralston is Professor in the Department of Engineering Fundamentals and directs J.B. Speed School’s Center for Teaching and Learning Engineering. She teaches undergraduate engineering mathematics and is currently involved in various educational research projects focused on the retention of engineering students as well as faculty development. ©American Society for Engineering Education, 2025Predicting academic behaviors of first-year engineering students by modeling non-cognitive factors and their interactionsIntroductionA common reason for many first-year
waytowards ensuring student persistence in engineering programs. Works Cited[1] N. Hamzah, S. M. Maat, and Z. Ikhsan, “A Systematic Review on Pupils’ Misconceptions and Errorsin Trigonometry,” Pegem Journal of Education and Instruction, vol. 11, no. 4, pp. 209-218, Oct. 2021,doi: 10.47750/pegegog.11.04.20.[2] A. Sithole, E. T. Chiyaka, P. McCarthy, D. M. Mupinga, B. K. Bucklein, and J. Kibirige, “StudentAttraction, Persistence and Retention in STEM Programs: Successes and Continuing Challenges,”Higher Education Studies, vol. 7, no. 1, pp. 46-59, Jan. 2017. doi: 10.5539/hes.v7n1p46.[3] T. J. Reagan, S. Claussen, and E. Lyne, “Systematic Review of Rigorous Research in TeachingIntroductory Circuits,” Proc
. Kolmos, "Emerging learning environments in engineering education,"Austral. J. Eng. Educ., vol. 25, no. 1, pp. 3–16, 2020.S. P. Hong, "Different numericaltechniques, modeling, and simulation in solving complex problems," J. Mach. Comput., vol. 3,no. 2, p. 58, 2023.[4] S. Huda, S. Alyahya, L. Pan, and H. Al-Dossari, "Combining Innovative Technology andContext Based Approaches in Teaching Software Engineering," Int. J. Adv. Comput. Sci. Appl.,vol. 13, no. 10, pp. 123-130, 2022.[5] M. S. Kleine, K. Zacharias, and D. Ozkan, "A scoping literature review on contextualizationin engineering education," J. Eng. Educ., vol. 113, no. 4, pp. 894–918, 2024.[6] J. E. Mills and D. F. Treagust, "Engineering education—Is problem-based or project-basedlearning the
Conference & Exposition, Portland, OR, June 23-26, 2024[2] R. D. Knight, “The vector knowledge of beginning physics students”, The Physics Teacher,vol. 33, p. 74, Feb. 1995, doi: 10.1119/1.2344143[3] I.A. Halloun and D. Hestenes, “The initial knowledge state of college physics students”, Am.Jour. Phys., vol. 53, pp 1043-1055, Nov. 1985, doi: 10.1119/1.14030[4] J.E. Bell, C. Cheng, H. Klautke, W. Cain, D.J. Freer, and T.J. Hinds, “A study of augmentedreality for the development of spatial reasoning ability” in 2018 Annual Conference &Exposition, Salt Lake City, UT, June 24-27, 2018.[5] J.E. Bell, T. Lister, S. Banerji, and T.J. Hinds, “A study of an augmented reality app for thedevelopment of spatial reasoning ability” in 2019 Annual
course. The name “chaos” garners attentioncolloquially as well. However, the viability of the course as an elective depends on its perceivedusefulness towards fulfilling degree requirements and career goals, along with difficulty inscheduling it among the students’ other program requirements. Future offerings should beactively promoted in differential equations classes during the previous semester(s); by recruitingstudents in this context, connections will be built between their current learning and further studywithin the discipline, even if they choose not to take the course.There are several benefits of a moderate class size (10-20 students), particularly in relation to themodeling project, including: • Representation of more disciplinary
situation”[7, p. 17]. Under the MMP approach, models are “Conceptual systems (consisting of elements, relations, operations, and rules governing interactions) that are expressed using external notation systems, and that are used to construct, describe, or explain the behaviors of other system(s)—perhaps so that the other system can be manipulated or predicted intelligently. A mathematical model focuses on structural characteristics (rather than, for example, physical or musical characteristics) of the relevant systems” [14, p. 10]The evolution of these models is not linear but occurs through iterative development cycles [16,17, 18]. These iterative processes emerge as students solve MEAs and interact with peers
supportstudents who struggle with math coursework. To stay true to the mission of the Mindset Reportand the overarching goal of having more diverse representation in the engineering field,continued work needs to be done to address this issue, and this ongoing study aims to discoverand highlight targeted interventions that can be implemented to help achieve these goals byincreasing self-efficacy and ultimately retention.References[1] Bertoline, G. R. (Ed.). The Engineering Mindset Report: A Vision for Change in Undergraduate Engineering andEngineering Technology Education. Retrieved August 1, 2024, from https://mindset.asee.org/wp-content/uploads/2024/09/The-Engineering- Mindset-Report.pdf, 2024.[2] Carvell, J. D., Klanderman, S., & Cohen, S. T. Work
assignments.And attendance was high, with only a few students missing during each class period. In addition,all students successfully built a kalimba and an electronic piano.Students were surveyed prior to and after the mini-course to get feedback on their interests,background, and confidence. When asked to list the major(s) that they were considering, 44 ofthe 81 students (~54%) listed a STEM major, and 14 students listed engineering, specifically. Onboth the pre- and post-course surveys, students were asked to rank their confidence on differentactivities from breadboarding to succeeding in an engineering course using a scale from 1 (noconfidence) to 7 (full confidence). Pre/post responses for several of the questions asked are listedin Table 2. While
, where students engage with foundational material before class,supports active programming practice and collaborative problem-solving during class time. Addingstructured group work, peer feedback opportunities, and teaching assistant support will further enhancethe learning experience and maintain its interactive nature.Requiring MathWorks certification and using auto-graded programming modules help ensure consistentengagement and measurable learning outcomes. This approach provides a solid framework for fosteringactive learning and supports the development of both individual and team-based skills in engineeringeducation.References[1] P. T. Goeser, W. Johnson, S. L. Bernadin, and D. A. Gajdosik-Nivens, “Work-in-Progress: TheImpact of MatLab
impacts student learning andengagement. With continued investigation and collaboration, SBG has the potential to transformgrading practices in STEM education, promoting mastery and meaningful learning for allstudents.References [1] C. Vatterott, Rethinking grading: meaningful assessment for standards-based learning. Alexandria, Virginia: ASCD, 2015. [2] U. Dudley, “What is Mathematics For?” in The Best Writing on Mathematics 2011, M. Pitici, Ed. Princeton University Press, Dec. 2011, pp. 1–12. [Online]. Available: https://www.degruyter.com/document/doi/10.1515/9781400839544.1/html [3] S. B. Kleinman, M. B. Leidman, and A. J. Longcore, “The changing landscape of grading systems in US higher education,” Perspectives: Policy and
expansions in statistical mechanics, Physics Education Research, 9, 020110, (2013).2. Alcock L. and Simpson A., Convergence of sequences and series: Interactions between visual reasoning and the learner’s beliefs about their own role, Educ. Stud. Math. 57, 1 (2004).3. Alcock L. and Simpson A., Convergence of sequences and series 2: Interactions between nonvisual reasoning and the learner’s beliefs about their own role, Educ. Stud. Math. 58, 77 (2005).4. Habre S., Multiple representations and the understanding of Taylor polynomials, PRIMUS 19, 417 (2009).5. Martin J., Expert conceptualizations of the convergence of Taylor series yesterday, today, and tomorrow, Ph.D. thesis, University of Oklahoma, 2009.6. Martin J., Oehrtman M., Hah
–79.[4] S. Breen and A. O’Shea, ‘Threshold Concepts and Undergraduate Mathematics Teaching’, PRIMUS, vol. 26, no. 9, pp. 837–847, Oct. 2016, doi: 10.1080/10511970.2016.1191573.[5] K. Pettersson, ‘The Threshold Concept of a Function – A Case Study of a Student’s Development of Her Understanding’, Sweden: MADIF-8, 2012.[6] K. Pettersson, E. Stadler, and T. Tambour, ‘Development of students’ understanding of the threshold concept of function’, in Proc. 8th Congress of the European Society for Research in Mathematics Education CERME8, Antalaya, Turkey, Feb. 2013.[7] A. O’Shea, S. Breen, and B. Jaworski, ‘The Development of a Function Concept Inventory’, Int. J. Res. Undergrad. Math. Ed., vol. 2, no. 3, pp. 279–296, Oct. 2016, doi
Journal of Research in Educaiton and Sciences, vol. 5, no. 1, pp. 355-373, 2019.[2] C. F. Oretaga-Barba, H. X. Ramirez-Perez and S. M.-P. Andrade, "Who are we receiving at the university? The impact of COVID-19 on mathematics and reading learning in high school," Frontiers in Education, vol. 9, 2024.[3] D. Dewey, E. Fahle, T. J. Kane, S. F. Reardon and D. O. Staiger, "Federal Pandemic Relief and Academic Recovery," 2024.[4] M. Polikoff, I. Clay and D. Silver, "Beyond test scores: Broader academic consequences of the COVID-19 pandemic on American students," Center for Reinventing Public Education, 2023.[5] J. Hampikian, J. Gardner, A. Moll, P. Pyke and C. Schrader, "Integrated Pre-Freshman Engineering and Precalculus
specifies eight different mathematical competencies, and defines amathematical competency as “a well-informed readiness to act appropriately in situationsinvolving a certain type of mathematical challenge” [11, p. 49, italics in original]. Thesecompetencies cover two broader capabilities: 1) asking and answering mathematical questionsand 2) working and communicating with mathematical language and tools. While competenciescan overlap in that one competency may be needed to achieve other(s), each competency isindividually distinct from the others. Table 1 provides the working definitions of eachcompetency, redefined based on the authors’ interpretation. Table 1: KOM Competencies and Definitions Competency Definition
. Sensors and Materials, 35(4), 1161. https://doi.org/10.18494/sam4015[6] Barbosa, G., Varanis, M., Delgado, K., & Oliveira, C. (2020). An acquisition system framework for mechanical measurements with python, raspberry-pi and mems sensors. Revista Brasileira De Ensino De Física, 42. https://doi.org/10.1590/1806-9126-rbef-2020- 0167[7] Cruz-Ramírez, S. R., García-Martínez, M., & Olais-Govea, J. M. (2022). NAO robots as context to teach numerical methods. International Journal on Interactive Design and Manufacturing (ijidem), 16(4), 1337-1356.[8] Herceg, Đ., & Herceg, D. (2019, September). Arduino and numerical mathematics. In Proceedings of the 9th Balkan Conference on Informatics (pp. 1
Course Design and Instructional Practices.”[18] Y. Akbulut and C. S. Cardak, “Adaptive educational hypermedia accommodating learning styles: A content analysis of publications from 2000 to 2011,” Computers & Education, vol. 58, no. 2, pp. 835–842, Feb. 2012, doi: 10.1016/j.compedu.2011.10.008.[19] H. L. Eyre, “The Behavior Analyst Today Keller’s Personalized System of Instruction: Was it a Fleeting Fancy or is there a Revival on the Horizon?” [Online]. Available: http://www.webcapsi.com[20] S. Purao, M. Sein, H. Nilsen, and E. A. Larsen, “Setting the Pace: Experiments with Keller’s PSI,” IEEE Transactions on Education, vol. 60, no. 2, pp. 97–104, May 2017, doi: 10.1109/TE.2016.2588460.[21] A