Modeling the Influence of Bridging Course on the Accounting Performance of the University Students using Educational Data Mining

Jasten Jeneth Trecene(1,Mail), Eduardo Edu Cornillez Jr(2), Reynalyn Barbosa(3), Jessie Richie delos Santos(4), Erap Gultian(5)

(1) Eastern Visayas State University – Tanauan Campus, Philippines
(2) Eastern Visayas State University – Tanauan Campus, Philippines
(3) Eastern Visayas State University – Tanauan Campus, Philippines
(4) Eastern Visayas State University – Tanauan Campus, Philippines
(5) Eastern Visayas State University – Tanauan Campus, Philippines

MailCorresponding Author

Copyright (c) 2021 Jasten Jeneth Trecene, Eduardo Edu Cornillez Jr, Reynalyn Barbosa, Jessie Richie delos Santos, Erap Gultian
Article Metrics→
              
Indexing & Metadata Harvesting→



Download Full Text: PDF

Abstract

Modelling the Influence of Bridging Course on the Accounting Performance of the University Students Using Educational Data Mining. Objectives: This study intends to determine the level of performance of the students in their Bridging Course (BC) and Accounting Education (AE) courses, and to model their significant influence. Methods: Descriptive and Predictive Correlation research design was used. The Educational Data Mining technique was utilized to extract data from the database of the university. Out of 331 datasets extracted, only 281 were included in the analysis, where datasets with no grades, and with dropped marks were excluded. The datasets are the grades of the students enrolled in BC and AE 113 and 114 for the school year, 2018–2019 and 2019–2020. Findings: Results showed a very good rating of the student’s performance in all courses both bridging course and accounting education courses where it revealed a positive and linear relationship. Moreover, the model shows that an increase in the performance in the BC is an increase also in their performance in their AE courses. Conclusion: The study proved that the curriculum is serving its purpose in rendering the highest possible opportunity for students to learn basic and even advanced accounting education.

Keywords: accounting performance, bridging course, educational data mining, modelling.

DOI: http://dx.doi.org/10.23960/jpp.v11.i3.202103


References

Arquero, J. L., Byrne, M., Flood, B., & Gonzalez, J. M. (2009). Motives, expectations, preparedness and academic Performance: A study of students of accounting at a Spanish university. Revista de Contabilidad-Spanish Accounting Review, 12(2), 279–299. https://doi.org/10.1016/S1138-4891(09)70009-3

Ashraf, M., Zaman, M., & Ahmed, M. (2020). An intelligent prediction system for educational data mining based on ensemble and filtering approaches. Procedia Computer Science, 167, 1471-1483.

Cornillez Jr, E. E. C. (2019). Instructional quality and academic satisfaction of university students. European Journal of Education Studies, 6(4), 13-31. http://dx.doi.org/10.46827/ejes.v0i0.2507

Cornillez Jr, E. E., Treceñe, J. K., & de los Santos, J. R. (2020). Mining educational data in predicting the influence of mathematics on the programming performance of university students. Indian Journal of Science and Technology, 13(26), 2668-2677.

Costa, E. B., Fonseca, B., Santana, M. A., de Araújo, F. F., & Rego, J. (2017). Evaluating the effectiveness of educational data mining techniques for early prediction of students’ academic failure in introductory programming courses. Computers in Human Behavior, 73, 247-256. DOI: http://dx.doi.org/10.1016/j.chb.2017.01.047

Cox, K. A. (2019). 4 Quantitative Research Designs. Research Design and Methods: An Applied Guide for the Scholar Practitioner. MD: Laureate Publishing.

Czibula, G., Mihai, A., & Crivei, L. M. (2019). SPRAR: A novel relational association rule mining classification model applied for academic performance prediction. Procedia Computer Science, 159, 2029. https://doi.org/10.1016/j.procs.2019.09.156

Darlington, E., & Bowyer, J. (2016). Accounting for students’ mathematical preparedness for Finance and Business degrees. Geography, 10, 8.

Driessnack, M., Sousa, V. D., & Mendes, I. A. C. (2007). An overview of research designs relevant to nursing: part 2: qualitative research designs. Revista latino-americana de enfermagem, 15(4), 684-688. https://doi.org/10.1590/S0104-11692007000300022

Engel, A. M. (2018). Literature review of student characteristics and performance in an accounting course. Community College Journal of Research and Practice, 42(10), 748-751. https://doi.org/10.1080/10668926.2017.1328320

Eastern Visayas State University. (2017). University Code of the Eastern Visayas State University. https://www.evsu.edu.ph/wp-content/uploads/2019/08/2017-Revised-Code-of-the-Eastern-Visayas-State-University.pdf

Garkaz, M., Banimahd, B., & Esmaeili, H. (2011). Factors affecting accounting students’ performance: The case of students at the Islamic Azad university. Procedia - Social and Behavioral Sciences, 29, 122–128. https://doi.org/10.1016/j.sbspro.2011.11.216

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate data analysis: A global perspective. (7th ed.). Upper Saddle River.

Hussain, S., Dahan, N. A., Ba-Alwib, F. M., & Ribata, N. (2018). Educational data mining and analysis of students’ academic performance using WEKA. Indonesian Journal of Electrical Engineering and Computer Science, 9(2), 447-459. https://doi.org/10.11591/ijeecs.v9.i2.pp447-459

Joseph, S., Yusuf, I., & Okpe, J. U. (2018). Prior knowledge and academic performance in first year accounting course. International Journal of Higher Education and Sustainability, 2(1), 1. https://doi.org/10.1504/ijhes.2018.10013657

Kallison Jr, J. M., & Stader, D. L. (2012). Effectiveness of summer bridge programs in enhancing college readiness. Community College Journal of Research and Practice, 36(5), 340-357. https://doi.org/10.1080/10668920802708595

Las Johansen, B. C., & Trecene, J. K. D. (2018). Predicting academic performance of information technology students using c4.5 classification algorithm: a model development. International Journal of Information Sciences and Application. 10(1), 7-21.

MacRae, A. W. (2019). Descriptive and inferential statistics. In Companion Encyclopedia of Psychology (pp. 1099-1121). Routledge.

Muda, S., Hussin, A. H., Johari, H., Sapari, J. M., & Jamil, N. (2013). The Key Contributing Factors of Non-accounting Students’ Failure in the Introduction to Financial Accounting Course. Procedia Social and Behavioral Sciences, 90(InCULT 2012), 712–719. https://doi.org/10.1016/j.sbspro.2013.07.144

Musso, M. F., Boekaerts, M., Segers, M., & Cascallar, E. C. (2019). Individual differences in basic cognitive processes and self-regulated learning: Their interaction effects on math performance. Learning and Individual Differences, 71(July 2017), 58–70. https://doi.org/10.1016/j.lindif.2019.03.003

Newman-Ford, L., Lloyd, S., & Thomas, S. (2007). Evaluating the performance of engineering undergraduates who entered without A-level mathematics via a specialist six-week “bridging technology” programme. Engineering education, 2(2), 33-43. https://doi.org/10.11120/ened.2007.02020033

Roick, J., & Ringeisen, T. (2018). Students’ math performance in higher education: Examining the role of self-regulated learning and self-efficacy. Learning and Individual Differences, 65(May), 148–158. https://doi.org/10.1016/j.lindif.2018.05.018

Schmid, S., Youl, D. J., George, A. V., & Read, J. R. (2012). Effectiveness of a short, intense bridging course for scaffolding students commencing university-level study of chemistry. International Journal of Science Education, 34(8), 1211-1234. http://dx.doi.org/10.1080/09500693.2012.663116

Thompson, B. (2005). Canonical correlation analysis. BS Everitt & DC Howell. Encyclopedia of statistics in behavioral science. https://doi.org/10.1002/0470013192.bsa068

Todd, P., & Wolpin, K. I. (2018). Accounting for mathematics performance of high school students in Mexico: Estimating a coordination game in the classroom. Journal of Political Economy, 126(6), 2608–2650. https://doi.org/10.1086/699977

Wachen, J., Pretlow, J., & Dixon, K. G. (2018). Building college readiness: Exploring the effectiveness of the UNC academic summer bridge program. Journal of College Student Retention: Research, Theory & Practice, 20(1), 116-138. https://doi.org/10.1177/1521025116649739

Yang, H. H., & Farley, A. (2019). Quantifying the impact of language on the performance of international accounting students: A cognitive load theory perspective. English for Specific Purposes, 55, 12-24. DOI: https://doi.org/10.1016/j.esp.2019.03.003

Yingling, L. (2018). Evaluating an Academic Bridge Program Using a Mixed Methods Approach. University of Arkansas.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


View My Stats