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RESEARCH PAPER
Predictive models, as an idea, to advance the secondary to tertiary transition in science courses
1
Department of Statistics, University of Johannesburg, Johannesburg, SOUTH AFRICA
2
Department of Botany and Plant Biotechnology, University of Johannesburg, Johannesburg, SOUTH AFRICA
3
Mathematics Education Division, School of Education, University of the Witwatersrand, Johannesburg, SOUTH AFRICA
Publication date: 2024-09-02
EURASIA J. Math., Sci Tech. Ed 2024;20(9):em2502
KEYWORDS
academic readinessat-risk studentsCOVID-19life science studentspredictivemodelssecondary-tertiary transition
ABSTRACT
Investigating the transition between the secondary and the tertiary levels is a main theme in
mathematics and science education. More so, this paper considers the transition that intersects
with the after-effects of COVID-19, or the transition together with an educational context
dominated by sociocultural differences and educational disadvantages. With this knowledge in
mind, we investigated the effects of predictive mathematical models (multiple regression, logistic
regression, and decision trees) to predict at-risk students at three time intervals (weeks one, three,
and seven) in the semester. The idea was implemented with a first-year life science class of 130
students. Variables from an academic readiness questionnaire along with early assessment grades
were used to build these models. Through a Monte Carlo cross validation method, the
performance of the executed predictive models was assessed, and limitations were reported. We
argue that the results obtained from predictive models can support both lecturers and students
in the transition phase. The idea can be expanded to other courses in STEM fields and other
educational contexts.
REFERENCES (40)
1.
Bitzer, E. (2003). Assessing students’ changing perceptions of higher education. South African Journal of Higher Education, 17, 164-177. https://doi.org/10.4314/sajhe.....
2.
Blum, W., & Leiβ, D. (2007). How do students and teachers deal with modelling problems? In C. Haines, P. Galbraith, W. Blum, & S Khan (Eds.), Mathematical modelling: Education, engineering and economics (pp. 222-231). Harwood. https://doi.org/10.1533/978085....
3.
Braatvedt, G., & Durandt, R. (2021). A glimpse into the mathematical readiness of students undertaking a university third-year mathematics major. In M. Qhobela, M. M. E. Ntsohi, & L. G. Mohafa (Eds.), Proceedings of the 29th Annual Conference of the Southern African Association for Research in Mathematics, Science and Technology Education (pp. 2-15).
4.
Chirinda, B., Ndlovu, M., & Spangenberg, E. (2021). Teaching mathematics during the COVID-19 lockdown in a context of historical disadvantage. Education Sciences, 11(4), Article 177. https://doi.org/10. 3390/educsci11040177.
5.
Di Martino, P., & Gregorio, F. (2019). The mathematical crisis in secondary-tertiary transition. International Journal of Science and Mathematics Education, 17(4), 825-843. https://doi.org/10.1007/s10763....
6.
Di Martino, P., Gregorio, F., & Iannone, P. (2023). The transition from school to university in mathematics education research: New trends and ideas from a systematic literature review. Educational Studies in Mathematics, 113(1), 7-34. https://doi.org/10.1007/s10649....
7.
Du Plessis, A., Müller, H., & Prinsloo, P. (2005). Determining the profile of the successful first-year accounting student. South African Journal of Higher Education, 19(4), 684-698. https://doi.org/10.4314/sajhe.....
8.
Durandt, R., Blum, W., & Lindl, A. (2021). Exploring first-year engineering student’s prior knowledge in mathematics. In M. Qhobela, M. M. E. Ntsohi, & L. G. Mohafa (Eds.), Proceedings of the 29th Annual Conference of the Southern African Association for Research in Mathematics, Science and Technology Education (pp. 28-31).
9.
Efron, B., & Tibshirani, R. (1994). An introduction to the bootstrap. Chapman and Hall/CRC. https://doi.org/10.1201/978042....
10.
Engelbrecht, J., Borba, M. C., & Kaiser, G. (2023). Will we ever teach mathematics again in the way we used to before the pandemic? ZDM–Mathematics Education, 55, 1-16. https://doi.org/10.1007/s11858....
11.
Gale, T., & Parker, S. (2014). Navigating change: A typology of student transition in higher education. Studies in Higher Education, 39, 734-753. https://doi.org/10.1080/030750....
12.
Geisler, S. (2021). Early dropout from university mathematics: The role of students’ attitudes towards mathematics. In M. Inprasitha, N. Changsri, & N. Boonsena (Eds.), Proceedings of the 44th Conference of the International Group for the Psychology of Mathematics Education (Vol. 2, pp. 281-288). PME.
13.
Geisler, S., & Rolka, K. (2021). “That wasn’t the math I wanted to do!”–Students’ beliefs during the transition from school to university mathematics. International Journal of Science and Mathematics Education, 19, 599-618. https://doi.org/10.1007/s10763....
14.
Glaesser, D., Holl, C., Malinka, J., McCullagh, L., Meissner, L., Harth, N. S., Machunsky, M., & Mitte, K. (2024). Examining the association between social context and disengagement: Individual and classroom factors in two samples of at-risk students. Social Psychology of Education, 27(1), 115-150. https://doi.org/10.1007/s11218....
15.
Granger, C. W. J., & Jeon, Y. (2007). Long-term forecasting and evaluation, International Journal of Forecasting, (23)4, 539-551. https://doi.org/10.1016/j.ijfo....
16.
Greefrath, G., Koepf, W., & Neugebauer, C. (2017). Is there a link between preparatory course attendance and academic success? A case study of degree programmes in electrical engineering and computer science. International Journal of Research in Undergraduate Mathematics Education, 3, 143-167. https://doi.org/10.1007/s40753....
17.
Hernandez-Martinez, P., & Williams, J. (2013). Against the odds: Resilience in mathematics students in transition. British Educational Research Journal, 39(1), 45-59. https://doi.org/10.1080/014119....
18.
Horton, J. (2015). Identifying at-risk factors that affect college student success. International Journal of Process Education, 7, 83-102.
19.
Jacobs, M., & Pretorius, E. (2016). First-year seminar intervention: Enhancing first year mathematics performance at the University of Johannesburg. Journal of Student Affairs in Africa, 4(1), 77-86. https://doi.org/10.14426/jsaa.....
20.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning. Springer. https://doi.org/10.1007/978-1-....
21.
Jansen, E. P. W. A., & Van der Meer, J., (2012). Ready for university? A cross-national study of students’ perceived preparedness for university. Australian Educational Researcher, 39, 1-16. https://doi.org/10.1007/s13384....
22.
Kuhn, M., & Johnson, K. (2016). Applied predictive modeling. Springer. https://doi.org/10.1007/978-1-....
23.
Lemmens, J. (2010). Students’ readiness for university education [PhD thesis, University of Pretoria].
24.
Leong, E., Mercer, A., Danczak, S. M., Kyne, S., & Thompson, C. D. (2021). The transition to first year chemistry: Student, secondary and tertiary educator’s perceptions of student preparedness. Chemistry Education Research and Practice, 22, 923-947. https://doi.org/10.1039/D1RP00....
25.
Lim, W., Bae, Y., & Know, O. (2022). The development of sociomathematical norms in the transition to tertiary exam-oriented individualistic mathematics education in an East Asian context. Educational Studies in Mathematics, 113, 57-78 https://doi.org/10.1007/s10649....
26.
Marbouti, F., Diefes-Dux, H. A., & Madhavan, K. (2016). Models for early prediction of at-risk students in a course using standards-based grading. Computers and Education, 103, 1-15. https://doi.org/10.1016/j.comp....
28.
Morton, W., & Durandt, R. (2023). Learning first-year mathematics fully online: Were students prepared, how did they respond? EURASIA Journal of Mathematics, Science and Technology Education, 19(6), Article em2272. https://doi.org/10.29333/ejmst....
29.
Mullis, I. V. S, Martin, M. O., Foy, P., Kelly, D. L., & Fishbein, B. (2020). TIMSS 2019 international results in mathematics and science. TIMSS & PIRLS International Study Center.
30.
Nicholson, N. (1990). The transition cycle: Causes, outcomes, processes and forms. In S. Fisher, & L. Cooper (Eds.), On the move: The psychological effects of change and transition (pp. 83-108). John Wiley.
31.
Niss, M., & Blum, W. (2020). The learning and teaching of mathematical modelling. Routledge. https://doi.org/10.4324/978131....
32.
Nouwen, W., & Clycq, N. (2020). Assessing the added value of the self-system model of motivational development in explaining school engagement among students at risk of early leaving from education and training. European Journal of Psychology of Education, 36(2), 243-261. https://doi.org/10.1007/s10212....
33.
Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82, 33-40. https://doi.org/10.1037/0022-0....
34.
Ribeiro, L., Rosário, P., Núñez, J. C., Gaeta, M., & Fuentes, S. (2019). First-year students background and academic achievement: The mediating role of student engagement. Frontiers in Psychology, 10, Article 2669. https://doi.org/10.3389/fpsyg.....
35.
Rossato, L., Morotti, A. C. V., & Scorsolini-Comin, F. (2024). Transition and adaptation to higher education in Brazilian first-year nursing students. Journal of Latinos and Education, 23(1), 2-12. https://doi.org/10.1080/153484....
36.
Shan, G. (2022). Monte Carlo cross validation for a study with binary outcome and limited sample size. BMC Medical Informatics and Decision Making, 22(270), 1-15. https://doi.org/10.1186/s12911....
37.
Sun, J. C.-Y., Joo Oh, Y., Seli, H., & Jung, M. (2017). Learning behavior and motivation of at-risk college students: The case of a self-regulatory learning class. The Journal of At-Risk Issues, 20(2), 12-24.
38.
Tall, D. (2008). The transition to formal thinking in mathematics. Mathematics Education Research Journal, 20(2), 5-24. https://doi.org/10.1007/BF0321....
39.
Van Appel, V., & Durandt, R. (2019). Investigating possibilities of predictive mathematical models to identify at-risk students in the South African higher education context. Perspectives in Education, 37(2), 1-15. https://doi.org/10.18820/25195....
40.
van der Put, C. E. (2020). The development of a risk and needs assessment instrument for truancy. Children and Youth Services Review, 109, Article 104721. https://doi.org/10.1016/j.chil....
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