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Record W4401521593 · doi:10.46690/elder.2024.02.02

Exploring first-year engineering students’ learning strategies and academic performance

2024· article· en· W4401521593 on OpenAlexaboutno aff
Shaoan Zhang, Qingmin Shi, Tiberio Garza, Chengcheng Li

Bibliographic record

VenueEducation and lifelong development research. · 2024
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationComputer sciencePsychology

Abstract

fetched live from OpenAlex

This study investigated the learning strategies of 450 U.S. engineering freshmen and their academic performance. Paired-samples t-tests indicated significant improvements in learning strategies, with higher mean scores on the post-survey compared to the pre-survey, except for the attitude subscale. Variation in two subscales, selecting main ideas and test strategies, was observed among demographic groups. Pell, first-generation, racially minoritized, and female students initially reported lower levels of learning strategies, but these differences diminished in the post-survey. Hierarchical linear regression analyses revealed learning strategies related to (coping with) anxiety and motivation significantly predicted academic performance, with effective anxiety management and higher motivation scores associated with better academic performance. This study provides insights into the learning strategies employed by first-year engineering students and their relationship with academic performance. It highlights the potential for improvements in these strategies over time and how they vary among different demographic groups. Cited as: Zhang, S., Shi, Q., Garza, T., Li, C. (2024). Exploring first-year engineering students’ learning strategies and academic performance. Education and Lifelong Development Research, 1(2), 58-71. https://doi.org/10.46690/elder.2024.02.02 References: Alzubaidi, E., Aldridge, J. M., & Khine, M. S. (2016). Learning English as a second language at the university level in Jordan: Motivation, self-regulation and learning environment perceptions. Learning Environments Research, 19(1), 133–152. American Society for Engineering Education. (2016). Engineering by the numbers: ASEE retention and time-to-graduation benchmarks for undergraduate engineering schools, departments and programs. Washington, D.C. Anais, M. J., Hojas, A. M., Bustos, A., Letelier, C., Zuzulich, M. S., Cabieses, B., &Zubiaguirre, M. (2012). Motivational and cognitive learning strategies used by first-year engineering undergraduate students at Universidad Católica in Chile. Creative Education, 3(6A), 811–817. Ashcraft, M. H., & Kirk, E. P. (2001). The relationships among working memory, math anxiety, and performance. Journal of Experimental Psychology: General, 130, 224–237. Attinasi Jr, L. C. (1989). Getting in: Mexican Americans’ perceptions of university attendance and the implications for freshman year persistence. The Journal of Higher Education, 60(3), 247–277. Barrett, D. E., Katsiyannis, A., & Zhang, D. (2006). Predictors of offense severity, prosecution, incarceration and repeat violations for adolescent male and female offenders. Journal of Child and Family Studies, 15(6), 708–718. Bonous-Hammarth, M. (2000). Pathways to success: Affirming opportunities for science, mathematics, and engineering majors. Journal of Negro Education, 69(1-2), 92–111. Brake, N. A., & Curry, J. C. (2016). The impact of one-credit introductory engineering courses on engineering self-efficacy: Seminar v. project-based. ASEE’s 123rd Annual Conference & Exposition. New Orleans, LA. Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27, 1–13. Capik, D., & Shupp, M. (2021). Addressing the sophomore slump: First-generation college students’ completion of year two of study in a rural bachelor’s degree-granting college. Journal of College Student Retention: Research, Theory & Practice, 0(0), 1–25. Chen, C. S. (2002). Self-regulated learning strategies and achievement in an introduction to information systems course. Information Technology, Learning, and Performance Journal, 20(1), 11–25. Chen, X. (2013). STEM attrition: College students’ paths into and out of STEM fields (NCES 2012-001). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109–132. Eisenberg, D., Golberstein, E., & Hunt, J. B. (2009). Mental health and academic success in college. The B. E. Journal of Economic Analysis & Policy, 9. Ergen, B., & Kanadli, S. (2017). The effect of self-regulated learning strategies on academic achievement: A meta-analysis study. Eurasian Journal of Educational Research, 17(69), 55–74. Fan, H. et al., (2012). An engineering introductory seminar course for first-year college students. Proceedings of IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), Hong Kong, pp. H1B-18-H1B-21, Fear-Fenn, M., & Kapostasy, K. K. (1992). Math + science + technology = vocational preparation for girls: A difficult equation to balance. Monograph, 7(1), n1. Finley, A., & McNair, T. (2013). Assessing underserved students’ engagement in high- impact practices. https://www.aacu.org/sites/default/files/files/assessinghips/AssessingHIPS_TGGrantReport.pdf Fong, C. J., Krou, M. R., Johnston-Ashton, K., et al. (2021). LASSI’s great adventure: A meta-analysis of the learning and study strategies inventory and academic outcomes. Educational Research Review, 34. Fong, C. J., Lee, J., Krou, M. R., et al. (2023). Meta-analyzing the factor structure of the learning and study strategies inventory. The Journal of Experimental Education, 91(2), 380–400. García-Ros, R., Pérez-González, F., Cavas-Martínez, F., et al. (2018). Social interaction learning strategies, motivation, first-year students’ experiences and permanence in university studies. Educational Psychology, 38(4), 451–469. Geisinger, B., & Raman, R. (2013). Why they leave: Understanding student attrition from engineering majors. International Journal of Engineering Education, 29(4), 914–925. Heffler, B. (2001). Individual learning style and the learning style inventory. Educational Studies, 27(3), 307–316. Hsieh, P. H., Sullivan, J. R., Sass, D. A., & Guerra, N. S. (2012). Undergraduate engineering students’ beliefs, coping strategies, and academic performance: An evaluation of theoretical models. The Journal of Experimental Education, 80(2), 196–218. Ishitani, T. T. (2006). Studying attrition and degree completion behavior among first-generation college students in the United States. The Journal of Higher Education, 77(5), 861–885. Kuh, G. D. (2008). High-impact educational practices: What they are, who has access to them, and why they matter. Washington, DC: Association of American Colleges and Universities. http://provost.tufts.edu/celt/files/High-Impact-Ed-Practices1.pdf Kulturel-Konak, S., D’Allegro, M. L., & Dickinson, S. (2011). Review of genderdifferences in learning styles: Suggestions for STEM education. Contemporary Issues in Education Research (CIER), 4(3), 9–18. Liebendörfer, M., Göller, R., Gildehaus, L., et al. (2022). The role of learning strategies for performance in mathematics courses for engineers. International Journal of Mathematical Education in Science and Technology, 53(5), 1133–1152. Loeb, E., & Hurd, N. M. (2019). Subjective social status, perceived academic competence, and academic achievement among underrepresented students. Journal of College Student Retention: Research, Theory & Practice, 21(2), 150–165. McFarland, J., Hussar, B., Zhang, J., Wang, X., Wang, K., Hein, S., Diliberti, M., Cataldi, E. F., Mann, F. B., & Barmer, A. (2019). The condition of education 2019 (NCES 2019-144). U.S. Department of Education. National Center for Education Statistics. Micari, M., & Pazos, P. (2021). Beyond grades: Improving college students’ social-cognitive outcomes in STEM through a collaborative learning environment. Learning Environments Research, 24(1), 123–136. Noel-Levitz Inc. (2013). Research, trend reports, and strategies for higher education. Ong, M., Jaumot‐Pascual, N., & Ko, L. T. (2020). Research literature on women of color in undergraduate engineering education: A systematic thematic synthesis. Journal of Engineering Education, 109(3), 581–615. Owens, M., Stevenson, J., Hadwin, J. A., & Norgate, R. (2012). Anxiety and depression in academic performance: An exploration of the mediating factors of worry and working memory. School Psychology International, 33, 433–449. Padgett, R. D., & Keup, J. R. (2011). 2009 national survey of first-year seminars: Ongoing efforts to support students in transition. National Resource Center for The First-Year Experience and Students in Transition. Pascarella, E. T., & Terenzini, P. T. (2005). How college affects students: A third decade of research. Jossey-Bass Publishers. Pandey, M., Paul, R., Johnston, K., Dawood, A.,et al. (2022). Learning from learners: Wellness seminars and self-reflections for first-year engineering students to enhance their journey in engineering education. Proceedings of the Canadian Engineering Education Association (CEEA). Permzadian, V., & Crede, M. (2016). Do first-year seminars improve college grades and retention? A quantitative review of their overall effectiveness and an examination of moderators of effectiveness. Review of Educational Research, 86(1), 277–316. Prus, J., Hatcher, L. Hope, M., & Grabiel, C. (1995). The Learning and Study Strategies Inventory (LASSI) as a predictor of first-year college academic success. Journal of The Freshman Year Experience, 7(2), 7–26. Roy, J. (2019). Engineering by the numbers. Ryan, M. P., & Glenn, P. A. (2014). What do first-year students need most: Learning strategies instruction or academic socialization? Journal of College Reading and Learning, 34(2), 4–28. Seabi, J. (2011). Relating learning strategies, self-esteem, intellectual functioning with academic achievement among first-year engineering students. South African Journal of Psychology, 41(2), 239–249. Sebesta, A. J., & Speth, B. E. (2017). How should I study for the exam? Self-regulated learning strategies and achievement in introductory biology. CBE—Life Sciences Education, 16(2), ar30, 1–12. Schmeck, R. R. (1983). Learning styles of college students. In N. R. F. Dillon and R. R. Schmeck (Eds.), Individual differences in cognition, Volume 1 (pp. 233–279). Academic Press Inc. https://doi.org/10.1007/978-1-4899-2118-5 Schrader, P. G., & Brown, S. W. (2008). Evaluating the first-year experience: Students’ knowledge, attitudes, and behaviors. Journal of Advanced Academics, 19(2), 310–343. Schumaker, J. B., & Deshler, D. D. (1992). Validation of learning strategy interventions for students with learning disabilities: Results of a programmatic research effort. In Wong, B. Y. L. (Eds). Contemporary intervention research in learning disabilities: An international perspective. (pp. 22–46). Springer. Stephen, J. S., & Rockinson-Szapkiw, A. J. (2021). A high-impact practice for online students: the use of a first-semester seminar course to promote self-regulation, self-direction, online learning self-efficacy. Smart Learning Environments, 8(6). https://doi.org/10.1186/s40561-021-00151-0 Tindall, T., & Hamil, B. (2004). Gender disparity in science education: The causes, consequences, and solution. Education, 125(2), 282–295. Tseng, H., Yi, X., & Yeh, H. (2019). Learning-related soft skills among online business students in higher education: Grade level and managerial role differences in self-regulation, motivation, and social skill. Computers in Human Behavior, 95, 179–186. Uddin, M. M. (2020). Best practices in advising engineering technology students for retention and persistence to graduation. Journal of Technology, Management & Applied Engineering, 36(1), 1–13. Wang, C. C. D., & Castañeda-Sound, C. (2008). The role of generational status, self-esteem, academic self-efficacy, and perceived social support in college students’ psychological well-being. Journal of College Counseling, 11(2), 101–118. Wang, M-T., & Degol, J. L. (2017). Gender gap in science, technology, engineering, and mathematics (STEM): Current knowledge, implications for practice, policy, and future directions. Educational Psychology Review, 29(1), 119–140. Wei, C. C., & Horn, L. (2002). Persistence and attainment of beginning students with Pell Grants (NCES 2002-169). Postsecondary Education Descriptive Analysis Reports. U.S. Department of Education, National Center for Education Statistics. http://nces.ed.gov/pubs2002/2002169.pdf Weinstein, C. E., Acee, T. W., & Jung, J. (2011). Self-regulation and learning strategies. New Directions for Teaching and Learning, 126, 45–53. Weinstein, C. E., Husman, J., & Dierking, D. R. (2000). Self-regulation interventions with a focus on learning strategies. In M. Boekaerts, P. R. Pintrich & M. Zeidner (Eds.), Handbook of self-regulation (pp. 727–747). Academic Press. Weinstein, C. E., Palmer, D. R., & Acee, T. W. (2016). Learning and Study Strategies Inventory (LASSI) user’s manual (3rd ed.). H&H Publishing Company, Inc. Wild, S., & Neef, C. (2023). Analyzing the associations between motivation and academic performance via the mediator variables of specific mathematic cognitive learning strategies in different subject domains of higher education. International Journal of STEM Education, 10(32), 1–14. Yip, M. C. (2013). The reliability and validity of the Chinese version of the learning and study strategies inventory (LASSI-C). Journal of Psychoeducational Assessment, 31(4), 396–403. Zhao, C. M., & Kuh, G. D. (2004). Adding value: Learning communities and student engagement. Research in Higher Education, 45(2), 115–138.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.643
Threshold uncertainty score0.738

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.086
GPT teacher head0.329
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2024
Admission routes1
Has abstractyes

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