Email this article Modelling the Influence of Bridging Course on the Accounting Performance of the University Students Using Educational Data Mining
Abstract
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.
Abstrak: Pemodelan Pengaruh Bridging Course Terhadap Kinerja Akuntansi Mahasiswa Menggunakan Educational Data Mining. Tujuan: Penelitian ini bertujuan untuk mengetahui tingkat kinerja mahasiswa pada mata kuliah Bridging Course (BC) dan Accounting Education (AE), serta memodelkan pengaruh signifikan mereka. Metode: Desain penelitian yang digunakan adalah Deskriptif dan Korelasi Prediktif. Teknik Educational Data Mining digunakan untuk mengekstrak data dari database universitas. Dari 331 kumpulan data yang diekstraksi, hanya 281 yang dimasukkan dalam analisis, di mana kumpulan data tanpa nilai, dan dengan nilai yang dihapus dikeluarkan. Dataset adalah nilai siswa yang terdaftar di BC dan AE 113 dan 114 untuk tahun ajaran 2018–2019 dan 2019–2020. Temuan: Hasil menunjukkan penilaian kinerja siswa yang sangat baik di semua mata kuliah baik mata kuliah bridging maupun pendidikan akuntansi kursus di mana ia mengungkapkan hubungan positif dan linier. Selain itu, model menunjukkan bahwa peningkatan kinerja di BC adalah peningkatan juga dalam kinerja mereka dalam kursus AE mereka. Kesimpulan: Studi ini membuktikan bahwa kurikulum melayani tujuannya dalam memberikan kesempatan setinggi mungkin bagi siswa untuk belajar pendidikan akuntansi dasar dan bahkan lanjutan.
Kata kunci: kinerja akuntansi, bridging course, educational data mining, pemodelan.
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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. doi: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. (2016). Quantitative research designs. In G. J. Burkholder, K. A. Cox, & L. M. Crawford (Eds.), The Scholar-Practitioner’s Guide to Research Design. Baltimore, MD: Laureate Publishing.
Czibula, G., Mihai, A., & Crivei, L. M. (2019). S PRAR: A novel relational association rule mining classification model applied for academic performance prediction. Procedia Computer Science, 159, 20-29.DOI: 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. DOI: 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.DOI: https://doi.org/10.1080/10668926.2017.1328320
EVSU University Code. (2017). University Code – 2017 Revised 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. Upper Saddle, NJ: Pearson.
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. DOI: DOI: 10.11591/ijeecs.v9.i2.pp447-459
James, R., Krause, K., & Jennings, C. (2010). The First Year Experience in Australian Universities: Findings from 1994 to 2009. Melbourne: Centre for the Study of Higher Education, The University of Melbourne. ISBN: 9780734041661. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.723.8190&rep=rep1&type=pdf
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. DOI: 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. Companion Encyclopedia of Psychology: Volume Two, 1099.
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. DOI: 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. DOI: http://dx.doi.org/10.1080/09500693.2012.663116
Thompson, B. (2005). Canonical Correlation Analysis. Encyclopedia of Statistics in Behavioral Science. DOI: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. DOI: 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. M. (2018). Evaluating an Academic Bridge Program Using a Mixed Methods Approach. https://pdfs.semanticscholar.org/9d95/51ecc3b5d18b9d886f145f271b6a4b87f1ff.pdf
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