Is motivation the key? Factors impacting performance in first year service mathematics modules

Maryna Lishchynska 1 * , Catherine Palmer 1, Seán Lacey 2, Declan O’Connor 1
More Detail
1 Department of Mathematics, Munster Technological University, Cork, IRELAND
2 Research Integrity & Compliance Officer, Munster Technological University, Cork, IRELAND
* Corresponding Author
EUR J SCI MATH ED, Volume 11, Issue 1, pp. 146-166. https://doi.org/10.30935/scimath/12529
Published Online: 14 October 2022, Published: 01 January 2023
OPEN ACCESS   1713 Views   1001 Downloads
Download Full Text (PDF)

ABSTRACT

Taught to non-mathematics undergraduates (business, science, engineering, and other technical programs), service mathematics is commonly associated with poor exam performance and low skill/knowledge attainment. The primary objective of the present study was to examine the range of factors thought to impact mathematics performance in higher education and establish which of the variables (i.e., motivation, mathematical background, growth mindset, preference for understanding, and time invested in independent learning) are of value in explaining the differences in students’ performance in service mathematics modules. A survey of first year business and engineering students who sat service mathematics modules was conducted. A multivariable proportional odds regression model was applied to detect and evaluate the association of each explanatory variable with mathematics performance. Motivation was found to be an important contributor to mathematics performance in first year service modules (p£0.05), second only to mathematical background (p<0.001). The work also investigated trends in motivation for studying mathematics across different student cohorts, where a significant difference in motivation was found between business and engineering students (p<0.001). The findings are discussed in terms of implications for learners and educators and should be of interest to fellow academics, those tasked with improving retention rates and policy makers.

CITATION

Lishchynska, M., Palmer, C., Lacey, S., & O’Connor, D. (2023). Is motivation the key? Factors impacting performance in first year service mathematics modules. European Journal of Science and Mathematics Education, 11(1), 146-166. https://doi.org/10.30935/scimath/12529

REFERENCES

  • Acee, T., & Weinstein, C. (2010). Effects of a value-reappraisal intervention on statistics students’ motivation and performance. The Journal of Experimental Education, 78(4), 487-512. https://doi.org/10.1080/00220970903352753
  • Aikens, M., Eaton, C., & Highlander, H. (2021). The case for biocalculus: Improving student understanding of the utility value of mathematics to biology and affect toward mathematics. CBE Life Sciences Education, 20(1). https://doi.org/10.1187/cbe.20-06-0124
  • Alibraheim, E. A. (2021). Factors affecting freshman engineering students’ attitudes toward mathematics. EURASIA Journal of Mathematics, Science and Technology Education, 17(6), 1-14. https://doi.org/10.29333/ejmste/10899
  • Alyahyan, E., & Dustegor, D. (2020). Predicting academic success in higher education: Literature review and best practices. International Journal of Educational Technology in Higher Education, 17(3), 1-21. https://doi.org/10.1186/s41239-020-0177-7
  • Artigue, M., Batanero, C., & Kent, P. (2007). Mathematical thinking and learning at post-secondary level. In F. K. Lester (Ed.), Second handbook of research on mathematics teaching and learning (pp. 1011-1049). Information Age Publishing.
  • Bargmann, C., Thiele, L., & Kauffeld, S. (2021). Motivation matters: Predicting students’ career decidedness and intention to drop out after the first year in higher education. Higher Education, 83, 845-861. https://doi.org/10.1007/s10734-021-00707-6
  • Biggs, J., Kember, D., & Leung, D. (2001). The revised two factor study process questionnaire: R-SPQ-2F. British Journal of Educational Psychology, 71(1), 133-149. https://doi.org/10.1348/000709901158433
  • Bischof, G., Zwölfer, A., & Rubeša, D. (2015). Correlation between engineering students’ performance in mathematics and academic success. In Proceedings of the ASEE Annual Conference & Exposition (pp. 23749). https://doi.org/10.18260/p.23749
  • Boaler, J. (2013). Ability and mathematics: The mindset revolution that is reshaping education. FORUM: For Promoting 3-19 Comprehensive Education, 55(1), 143-152. https://doi.org/10.2304/forum.2013.55.1.143
  • Boaler, J. (2015). Mathematical mindsets: Unleashing students’ potential through creative math, inspiring messages, and innovative teaching. Jossey-Bass/Wiley.
  • Brahm, T., Jenert, T., & Wagner, D. (2017). The crucial first year: A longitudinal study of students’ motivational development at a Swiss business school. Higher Education, 73, 459-478. https://doi.org/10.1007/s10734-016-0095-8
  • Brandenberger, C., Hagenauer, G., & Hascher, T. (2018). Promoting students’ self-determined motivation in maths: Results of a 1-year classroom intervention. European Journal of Psychology of Education, 33, 295-317. https://doi.org/10.1007/s10212-017-0336-y
  • Brant, R. (1990). Assessing proportionality in the proportional odds model for ordinal logistic regression. Biometrics, 46(4), 1171-1178. https://doi.org/10.2307/2532457
  • Campbell, A., Craig, T., & Collier-Reed, B. (2019). A framework for using learning theories to inform ‘growth mindset’ activities. International Journal of Mathematical Education in Science and Technology, 1-18. https://doi.org/10.1080/0020739X.2018.1562118
  • Campos-Sánchez, A., López-Núñez, J. A., Carriel, V., & Martín-Piedra, M.-A. (2014). Motivational component profiles in university students learning histology: A comparative study between genders and different health science curricula. BMC Medical Education, 14, 46. https://doi.org/10.1186/1472-6920-14-46
  • Canning, E., & Harackiewicz, J. (2015). Teach it, don’t preach it: The differential effects of directly-communicated and self-generated utility value information. Motivation Science, 1(1), 47-71. https://doi.org/10.1037/mot0000015
  • Cassidy, S. (2016). The academic resilience scale (ARS-30): A new multidimensional construct measure. Frontiers in Psychology, 7(1787). https://doi.org/10.3389/fpsyg.2016.01787
  • Code, W., Merchant, S., Maciejewski, W., Thomas, M., & Lo, J. (2016). The mathematics attitudes and perceptions survey: An instrument to assess expert-like views and dispositions among undergraduate mathematics students. International Journal of Mathematical Education in Science and Technology, 47(6), 917-937. https://doi.org/10.1080/0020739X.2015.1133854
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Routledge.
  • Dweck, C. (2008). Mindsets and math/science achievement. Carnegie Corporation of New York, Institute for Advanced Study, Commission on Mathematics and Science Education.
  • Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109-132. https://doi.org/10.1146/annurev.psych.53.100901.135153
  • Faulkner, F., Hannigan, A., & Fitzmaurice, O. (2014). The role of prior mathematical experience in predicting mathematics performance in higher education. International Journal of Mathematical Education in Science and Technology, 45(5), 648-667. https://doi.org/10.1080/0020739X.2013.868539
  • Gaspard, H., Dicke, A.-L., Flunger, B., Brisson, B. M., Häfner, I., Nagengast, B., & Trautwein, U. (2015). Fostering adolescents’ value beliefs for mathematics with a relevance intervention in the classroom. Developmental Psychology, 51(9), 1226-1240. https://doi.org/10.1037/dev0000028
  • Gijbels, D., Van de Watering, G., & Van den Bossche, P. (2005). The relationship between students’ approaches to learning and the assessment of learning outcomes. European Journal of Psychology of Education, 20, 327-341. https://doi.org/10.1007/BF03173560
  • Golsteyn, B., Non, A., & Zölitz, U. (2021). The impact of peer personality on academic achievement. Journal of Political Economy, 129(4), 1052-1099. https://doi.org/10.1086/712638
  • Gynnild, V., Tyssedal, J., & Lorentzen, L. (2005). Approaches to study and the quality of learning. Some empirical evidence from engineering education. International Journal of Science and Mathematics Education, 3, 587-607. https://doi.org/10.1007/s10763-005-5178-4
  • Harackiewicz, J. M., Rozek, C. S., Hulleman, C. S., & Hyde, J. S. (2012). Helping parents to motivate adolescents in mathematics and science: An experimental test of a utility-value intervention. Psychological Science, 23(8), 899-906. https://doi.org/10.1177/0956797611435530
  • Harris, D., & Pampaka, M. (2016). ‘They [the lecturers] have to get through a certain amount in an hour’: First year students’ problems with service mathematics lectures. Teaching Mathematics and Its Applications, 35, 144-158. https://doi.org/10.1093/teamat/hrw013
  • Harris, D., Black, L., Hernandez-Martinez, P., Pepin, B., & Williams, J. (2014). Mathematics and its value for engineering students: what are the implications for teaching? International Journal of Mathematical Education in Science and Technology, 46(3), 321-336. https://doi.org/10.1080/0020739X.2014.979893
  • Herrmann, K. J., McCune, V., & Bager-Elsborg, A. (2017). Approaches to learning as predictors of academic achievement: Results from a large scale, multi-level analysis. Högre Utbildning [Higher Education], 7(1), 29-42. https://doi.org/10.23865/hu.v7.905
  • Hirschi, A., & Spurk, D. (2021). Striving for success: Towards a refined understanding and measurement of ambition. Journal of Vocational Behavior, 127, 103577. https://doi.org/10.1016/j.jvb.2021.103577
  • Hulleman, C., & Harackiewicz, J. (2009). Promoting interest and performance in high school science classes. Science, 326(5958), 1410-1412. https://doi.org/10.1126/science.1177067
  • Jerrim, J., Shure, N., & G., W. (2020). Driven to succeed? Teenagers’ drive, ambition, and performance on high-stakes examinations. IZA Discussion Paper No. 13525. https://doi.org/10.2139/ssrn.3660272
  • Kaldo, I., & Reiska, P. (2012). Estonian science and non-science students’ attitudes towards mathematics at university level. Teaching Mathematics and Its Applications, 31, 95-105. https://doi.org/10.1093/teamat/hrs001
  • Kappe, R., & van der Flier, H. (2012). Predicting academic success in higher education: What’s more important than being smart? European Journal of Psychology of Education, 27, 605-619. https://doi.org/10.1007/s10212-011-0099-9
  • Kaya, S., & Karakoc, D. (2022). Math mindsets and academic grit: How are they related to primary math achievement? European Journal of Science and Mathematics Education, 10(3), 298-309. https://doi.org/10.30935/scimath/11881
  • Kosovich, J., Hulleman, C., Phelps, J., & Lee, M. (2019). Improving algebra success with a utility-value intervention. Journal of Developmental Education, 42(2), 2-10.
  • Kriegbaum, K., Becker, N., & Spinath, B. (2018). The relative importance of intelligence and motivation as predictors of school achievement: A meta-analysis. Educational Research Review, 25, 120-148. https://doi.org/10.1016/j.edurev.2018.10.001
  • Lazowski, R., & Hulleman, C. (2016). Motivation intervention in education: A meta-analytic review. Review of Educational Research, 86(2), 602-640. https://doi.org/10.3102/0034654315617832
  • Liston, M., & O’Donoghue, J. (2009). Factors influencing the transition to university service mathematics: Part 1 a quantitative study. Teaching Mathematics and Its Applications, 28, 77-87. https://doi.org/10.1093/teamat/hrp006
  • Liston, M., & O’Donoghue, J. (2010). Factors influencing the transition to university service mathematics: Part 2 a qualitative study. Teaching Mathematics and Its Applications, 29, 53-68. https://doi.org/10.1093/teamat/hrq005
  • Murayama, K., Perkun, R., Lichtenfeld, S., & vom Hofe, R. (2013). Predicting long-term growth in students’ mathematics achievement: The unique contributions of motivation and cognitive strategies. Child Development, 84(4), 1475-1490. https://doi.org/10.1111/cdev.12036
  • Musso, M., Kyndt, E., Cascallar, E., & Dochy, F. (2012). Predicting mathematical performance: The effect of cognitive processes and self-regulation factors. Educational Research International, 250719. https://doi.org/10.1155/2012/250719
  • Nabizadeh, S., Hajian, S., Sheikhan, Z., & Rafiei, F. (2019). Prediction of academic achievement based on learning strategies and outcome expectations among medical students. BMC Medical Education, 19, 99. https://doi.org/10.1186/s12909-019-1527-9
  • Ó Súilleabháin, G., Farrelly, T., & Lacey, S. (2022). Dataset on student experiences and perceptions of emergency remote teaching (ERT) in an Irish university. Data in Brief, 41. https://doi.org/10.1016/j.dib.2022.107954
  • Pantziara, M., & Philippou, G. N. (2015). Students’ motivations in the mathematics classroom. Revealing causes and consequences. International Journal of Science and Mathematics Education, 13, 385-411. https://doi.org/10.1007/s10763-013-9502-0
  • Plant, E. A., Ericsson, K., Hill, L., & Asberk, K. (2005). Why study time does not predict grade point average across college students: Implications of deliberate practice for academic performance. Contemporary Educational Psychology, 30(1), 96-116. https://doi.org/10.1016/j.cedpsych.2004.06.001
  • R Core Team. (2021). R: A language and environment for statistical computing. R foundation for statistical computing. https://www.R-project.org
  • Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin, 1382, 353-387. https://doi.org/10.1037/a0026838
  • Rowell, L., & Hong, E. (2013). Academic motivation: Concepts, strategies and counselling approaches. Professional School Counselling, 6(3), 158-171. https://doi.org/10.1177/2156759X1701600301
  • Roykenes, K. (2016). “My math and me”: Nursing students’ previous experiences. Nurse Education in Practice, 16, 1-7. https://doi.org/10.1016/j.nepr.2015.05.009
  • Rudd, G., Meissel, K., & Meyer, F. (2021). Measuring academic resilience in quantitative research: A systematic review of the literature. Educational Research Review, 34(100402). https://doi.org/10.1016/j.edurev.2021.100402
  • Ryan, V., Fitzmaurice, O., & O’Donoghue, J. (2021). A study of academic achievement in mathematics after the transition from primary to secondary education. SN Social Sciences, 1, 173. https://doi.org/10.1007/s43545-021-00177-8
  • Rylands, L. J., & Coady, C. (2009). Performance of students with weak mathematics in first-year mathematics and science. International Journal of Mathematical Education in Science and Technology, 40(6), 741-753. https://doi.org/10.1080/00207390902914130
  • Schuman, H., Walsh, E., Olson, C., & Etheridge, B. (1985). Effort and reward: The assumption that college grades are affected by quantity of study. Social Forces, 63(4), 945-966. https://doi.org/10.2307/2578600
  • Skilling, K., Bobis, J., & Martin, A. J. (2020). The “ins and outs” of student engagement in mathematics: Shifts in engagement factors among high and low achievers. Mathematics Education Research Journal, 33, 469-493. https://doi.org/10.1007/s13394-020-00313-2
  • State Examinations Commission. (2022). Description of certificate examinations. https://www.examinations.ie/?l=en& mc=ca&sc=sb
  • Stay On Top of Your Maths. (2019). Study tips, exam advice, and much, much more. https://mathematics.cit.ie/stay-on-top-of-your-maths
  • Steinmayr, R., & Spinath, B. (2009). The importance of motivation as a predictor of school achievement. Learning and Individual Differences, 19, 80-90. https://doi.org/10.1016/j.lindif.2008.05.004
  • Steinmayr, R., Weidinger, A. F., Schwinger, M., & Spinath, B. (2019). The importance of students’ motivation for their academic achievement–Replicating and extending previous findings. Frontiers in Psychology, 10, 1730. https://doi.org/10.3389/fpsyg.2019.01730
  • Symonds, R., Lawson, D., & Robinson, C. (2010). An investigation of physics undergraduates’ attitudes towards mathematics. Teaching Mathematics and Its Applications, 29, 140-154. https://doi.org/10.1093/teamat/hrq009
  • Tahar, N. F., Ismail, Z., Zamani, N. D., & Adnan, N. (2010). Students’ attitude toward mathematics: The use of factor analysis in determining the criteria. Procedia-Social and Behavioral Sciences, 8, 476-481. https://doi.org/10.1016/j.sbspro.2010.12.065
  • Tossavainen, T., Rensaa, R. J., & Johansson, M. (2021). Swedish first-year engineering students’ views of mathematics, self-efficacy and motivation and their effect on task performance. International Journal of Mathematical Education in Science and Technology, 52(1), 23-38. https://doi.org/10.1080/0020739X.2019.1656827
  • Trigwell, K., Ashwin, P., & Millan, E. (2013). Evoked prior learning experience and approach to learning as predictors of academic achievement. Educational Psychology, 83(3), 363-378. https://doi.org/10.1111/j.2044-8279.2012.02066.x
  • Vroom, V. H. (1964). Work and motivation. Wiley.
  • West-Burnham, J., & Coates, M. (2005). Personalizing learning. Transforming education for every child. MPG Books Ltd.
  • Wigfield, A., & Eccles, J. S. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25, 68-81. https://doi.org/10.1006/ceps.1999.1015
  • Zakariya, Y. F. (2021). Undergraduate students’ performance in mathematics: Individual and combined effects of approaches to learning, self-efficacy, and prior mathematics knowledge [Doctoral dissertation, University of Agder].