Developing a Risk Model to identify factors which critically affect Secondary School students’ performance in Mathematics.

Year 2020, Volume: 1 Issue: 2, 63 - 72, 15.12.2020

Stylıanos Tsakirtzis Ioannis Georgakopoulos

Abstract

A concrete knowledge on Mathematics is essential on the ground that it constitutes to be a key-ingredient to a brilliant academic career. Though, a lot of students encounter insurmountable difficulties and as a consequence they fail their Mathematical courses. That holds true particularly on the case of secondary school students. Thereby, controlling the risk of students’ failure in Mathematics is of utmost importance. The paper demonstrates a risk model which identifies factors that critically affect secondary school students’ performance and prioritize them according to their contribution to the risk occurrence. The risk model has been built on the base of a binary logistics regression analysis on students’ behavioral engagement data. These data reflect students’ effort and involvement in the entire learning process. The risk model development process is presented in the context of a case study on a specific Mathematical course, delivered at a Greek private Secondary School (Gymnasium). The binary logistics’ regression outcome has proved that students’ achievement on schoolwork and review packages are factors which critically affect the students’ performance in the respective course. It is also important to highlight that schoolwork completed appeared to have significant contribution to the risk occurrence, indicating that schoolwork completed could be regarded as a cardinal factor which critically affects students’ performance in the context of the respective study.

Keywords

risk factors, risk model, students’ achievement, engagement

Supporting Institution

University Of West Attica

Thanks

We thank Mrs. Aikaterini Karahaliou, (Director and Owner) and Mrs. Aggeliki Ntouska (the head of the Private High School's IT department) for their help in the data collection process.

References

• Allison, P. D. (2014). Measures of fit for logistic regression, Paper No. 1485-2014. Paper presented at the SAS Global Forum 2014 Conference, Washington D.C. 23-26 March, 2014.
• Anagnostopoulos, T., Kytagias, C., Xanthopoulos, T., Georgakopoulos, I., Psaromiligkos, I., Salmon, I. (2020). Intelligent Predictive Analytics for Identifying Students at Risk of Failure in Moodle Courses. International Conference on Intelligent Tutoring Systems. June, 2020, 152-162, Springer, Cham
• Carolan, J. & Guinn, A. (2007). Differentiation. Lessons from Master Teachers. Educational Leadership, 64(5), 44–47.
• Casillas A., Robbins S., Allen J., Kuo Yi., Hanson M., Schmeiser C. (2012). Predicting Early Academic Failure in High School from Prior Academic Achievement, Psychosocial Characteristics, and Behavior. Journal of Educational Psychology, 104(2), 407-420.
• Flores M., Taylor M. (2007). The Effects of a Direct Instruction Program on the Fraction Performance of Middle School Students At-risk for Failure in Mathematics. Journal of Instructional Psychology, 34(2), 84-94
• Fredericks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59-109.
• Georgakopoulos, I., Kytagias, C., Psaromiligkos, Y. Voudouri, A. (2018). Identifying risks factors of students’ failure in e-learning systems: towards a warning system International Journal of Decision Support Systems, 3(3), 190-206.
• Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate Data Analysis (6th ed.). Upper Saddle River, New Jersey: Pearson Prentice Hall. Hemmings B., Grootenboer P., Kay R. (2011). International Journal of Science and Mathematics Education, 9(3), 691-705.
• Hopf D., Xochellis P. (2003). Gymnasium and Lyceum in Greece. Athens. [in Greek].
• Instructions on the Teaching of Mathematics at Gymnasium for the years 2018-2020. Ministry of Education. Available at: https://www.minedu.gov.gr/gymnasio-m-2/didaktea-yli-gymn/37395-28-09-18-odigies-gia-ti-didaskalia-ton-mathimatikon-ton-fysikon-epistimon-sto-gymnasio-gia-to-sxol-etos-2018-2020 [in Greek].
• Kajander A., Zuke C., Walton G. (2008). Teaching Unheard Voices: Students At-Risk in Mathematics. Canadian Journal of Education, 31(4), 1039-1064.
• Macfadyen L., Dawson S. (2010). Mining LMS data to develop an ‘‘early warning system” for educators: A proof of concept. Computer and Education, 54(2), 588-599.
• Marks, H. M. (2000). Student engagement in instructional activity: Patterns in the elementary, middle, and high school years. American Educational Research Journal, 37, 153-84.
• McConney A., Perry L. (2010). Socioeconomic status, self-efficacy, and mathematics achievement in Australia: a secondary analysis. Educational Research for Policy and Practice, 9, 77-91.
• Sciarra, D.T. & Seirup, H.J. (2008). The multidimensionality of school engagement and math achievement among racial groups. Professional School Counseling, 11(4), 218-228. DOI: http://dx.doi.org/10.5330/PSC.n.2010-11.218
• Smith, T. J., & McKenna, C. M. (2013). A comparison of the logistic regression pseudo Rsquared indices. Multiple Linear Regression Viewpoints, 39(2), 17-26.
• Sullivan, P., Cheeseman, J., Michels, D., Mornane, A., Clarke, D., Roche, A. & Middleton, J. (2011). Challenging mathematics tasks: What they are and how touse them. In L. Bragg (Ed.), Maths is Multi-dimensional. Proccedings of the 48thAnnual Conference of the Mathematical Association of Victoria, 33–46. Melbourne: Mathematical Association of Victoria.
• Tomlinson, C. (2001). How to differentiate instruction in mixed-ability classrooms (2nd ed.). Alexandria. VA: Association for Supervision and Curriculum Development.
• Tomlinson, C. (2003). Fulfilling the promise of the differentiated classroom: Tools and strategies for responsive teaching. Alexandria, VA: Association for Supervision and Curriculum Development.
• Tzoka D., (2019). Mathematical challenge and diversified teaching in the math class. Diplomatic work. Inter-university – interdepartmental postgraduate program "Didactics and Methodology of Mathematics".
• Vose D. (2008). Risk Analysis: A Quantitative Guide, 3rd Edition. Hoboken NJ Wiley
• Willms, J. D. (2003). Student engagement at school: A sense of belonging and participation. Paris: Organisation for Economic Co-Operation and Development.
• Xin Y., Jiendra K., Buchman A., (2005). Effects of Mathematical Word Problem–Solving Instruction on Middle School Students with Learning Problems. Journal of Special Education, 39(3),181-192.
• Yurt E., (2014). The Predictive Power of Self-Efficacy Sources for Mathematics Achievement. Education and Science, 39(176), 159-169