A Combined Model for Prediction of Financial Software Learning Rate based on the Accounting Students’ Characteristics

الموضوعات :

Bahareh Banitalebi Dehkordi ¹ ( Department of Accounting, Shahrekord branch, Islamic Azad University, Shahrekord, Iran )
Hamed Samarghandi ² ( Department of Finance and Management Science, Edwards School of Business, University of Saskatchewan, Saskatoon, SK, Canada )
Sara Hosseinzadeh Kassani ³ ( Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada )
Hamidreza malekhossini ⁴ ( Department of Accounting, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran )

تاريخ الإرسال : 16 الخميس , صفر, 1443 تاريخ التأكيد : 19 الخميس , جمادى الأولى, 1443 تاريخ الإصدار : 05 السبت , ربيع الأول, 1444

الکلمات المفتاحية: Data mining, accounting software, Accounting information system, Artificial Intelligence,

ملخص المقالة :

The accounting software is considered to be of the most critical components of accounting information system, with particular significance as of accounting and financial systems. the most important problems with accounting education systems is that students do not adequately learn the financial software required by the accounting profession, which, in turn, reduces the credibility and position of the accounting profession. That the main objective of accounting software education is to educate skilled and expert accountants to enter the accounting profession, which is considered as of the success factors of country’s economy. In this study, employ data mining techniques to investigate the accuracy, precision, and recall performance measures and to predict the rate of financial software learning based on accounting students’ emotional intelligence (EI), gender and education level. Accordingly, a machine-learning-based multivariate statistical analysis is performed on 100 Iranian accounting students. The results show that emotional intelligence has the most impact on the rate of financial software learning among the variables. Gender and education level were influential. Also, among the five algorithms, the highest precision and recall are achieved by both Decision Tree and XGBoost and are presented as the most appropriate models for the prediction rate of financial software learning.

المصادر:

[1] Abraham, A., The need for the integration of emotional intelligence skills in business education. Business Renaissance Quarterly, 2006, 1(3), P. 65-80.

[2] Albrecht, W.S., and Sack, R.J., Accounting Education: Changing the Course through a Perilous Future. Accounting Education Series, American Accounting Association, Saratosa., 16. Doi:10.4236/ojacct.2016.52002.

[3] AL-Qadri, A.B., Zhao, W., Emotional intelligence and students' academic achievement. Problems of Education in the 21st Century, 2021, P. 79 (3), 2000-2021. Doi: 10.33225/pec/21.79.360.

[4] Amado-Alonso, D., León-del-Barco, B., Mendo-Lázaro, S., Sánchez-Miguel, P., Iglesias Gallego, D., Emotional intelligence and the practice of organized physical-sport activity in children, Sustainability, 2019, 11(6). Doi: 10.3390/su11061615.

[5] Association to Advance Collegiate Schools of Business (AACSB)., Eligibility procedures and accreditation standards for business accreditation, 2010. 

[6] Baron, R. M., The Bar-On model of emotional-social intelligence (ESI). Psicothema, 2006, 18(1), P.13-25. Doi:  0214-9915.

[7] Bauer, R., Kahavi, R., An Emprical Comparison of Voting Classification Algorithms: Baggin,Boosting,and Variant. Machine Learning, 1999, 36, P.105-139. Doi: 10.1023/A:1007515423169.

[8] Beck, J., Halim. H., Undergraduate internships in accounting: What and how do Singapore interns learn from experience?  Accounting Education: An International Journal, 2008, 17(2), P.151–172. Doi: 10.1080/09639280701220277.

[9] Becker, D., Pernsteiner, A., Drum, D., The roles of technology acceptance and computer learner aptitude on accounting student learning achievement with IDEA software, International Journal of Learning Technology, 2017. 11(4), P. 270-284.

[10] Boulianne, E., Impact of accounting software utilization on students' knowledge acquisition, Journal of Accounting and Organizational Change, 2014, 10(1), P. 22-48. Doi:10.1108/JAOC-12-2011-0064.

[11] Chamizo-Gonzalez, J., Cano-Montero, E., Urquia-Grande, E., Muñoz-Colomina, C., Educational data mining for improving learning outcomes in teaching accounting within higher education, The International Journal of Information and Learning Technology, 2015, 32(5), P. 272-285. Doi: 10.1108/IJILT-08-2015-0020.

[12] Cianciolo, A., T., Sternberg, R., Intelligence: A Brief History. Blackwell Brief Histories of Psychology: Oxford, UK: Blackwell Publishing, 2004, Doi: 10.1002/jhbs.20226

[13] Ciarrochi, J., Frank P Deane, F., Anderson, S., Emotional intelligence moderates the relationship between stress and mental health. Personality and Individual Differences, 2002, 32(2), P.197-209. Doi:10.1016/S0191-8869 (01)00012-5.

[14] Cook, G., Bay, D., Visser, B., E., J., Myburgh, J., Njoroge, J., Emotional Intelligence: The Role of Accounting Education and Work Experience. Accounting Education, 2011, 26(2), P. 267–286. Doi: 10.2308/IACE-10001.

[15] Daff, L., Lange, P., Jackling, B., A Comparison of Generic Skills and Emotional Intelligence in Accounting Education, Accounting Education, 2012, 24(3), P. 627–645. Doi: 10.2308/iace-50145.

[16] Davoodi Kasbi, A., Dadashi, I., Azinfar, K., Stock price analysis using machine learning method (Non-sensory-parametric backup regression algorithm in linear and nonlinear mode), Advances in Mathematical Finance and Applications, 2021, 6(2,), P. 285-301. Doi: 10.22034/amfa.2019.1869838.1232

[17] Doaei, M., Mirzaei, S.A., Rafigh, M., Hybrid Multilayer Perceptron Neural Network with Grey Wolf Optimization for Predicting Stock Market Index, Advances in Mathematical Finance and Applications, 2021, 6(4,), P. 883-894. Doi: 10.22034/amfa.2021.1903474.1452

[18] Doornekamp, B. G., Streumer, J., Sanden, J. M. M., Teurlings, C. C. J., Integration of practice and theory in pre-vocational secondary education, International journal of vocational education and training, 2002, 10(1), P.5-20.

[19] Goleman, D., Children, Parental Guidance, and Emotional Intelligence, The Responsive Community: Rights and Responsibilities, 1995, 5(4), 49.

[20] Goleman, D., Prologue: educating people to be emotionally and socially intelligent: research paper, Perspectives in Education, 2003, 21(4), P. 1-2.

[21] Gumus, M., KiranCrude, M. S., Oil price forecasting using XGBoost, Paper presented at the International Conference on Computer Science and Engineering, 2017, Doi: 10.1109/UBMK.2017.8093500.

[22] Hryshko, A., Downs, T., Development of Machine Learning Software for High Frequency Trading in Financial Markets, Business Applications and Computational Intelligence, 2006, 20, P.406-430. Doi:10.4018/978-1-59140-702-7.ch020.

[23] Humphrey, R., Beard, D., Faculty perceptions of online homework software in accounting education, Journal of Accounting Education, 2014, 32(3), P.238-258. Doi: 10.1016/j.jaccedu.2014.06.001.

[24] Lyons, J., Schneider, R., The influence of emotional intelligence on performance, Personality and Individual Differences, 2005, 39(4), P. 693-703.

[25] Martins, A., Ramalho, N., Morin, E., A comprehensive meta-analysis of the relationship between Emotional Intelligence and health. Personality and Individual Differences, 2010, 49(6), P.554-564. Doi: 10.1016/j.paid.2010.05.029

[26] Mayer, J., D., & Salovey, P., The intelligence of emotional intelligence. Intelligence, 1993, 17(4), P. 433-442. Doi: 10.1016/0160-2896 (93)90010-3.

[27] Mayer, J. D., Salovey, P., Emotional intelligence and the construction and regulation of feelings, Applied and Preventative Psychology, 1995, 4, P. 197 − 208. Doi: 10.1016/S0962-1849 (05)80058-7.

[28] Mikolajczak, M., Olivier Luminet, O., Trait emotional intelligence and the cognitive appraisal of stressful events: An exploratory study, Personality and Individual Differences, 2008, 44(7), P. 1445 −1453. Doi: 10.1016/j.paid.2007.12.012.

[29] O'Connor, D., Myers, J., Ignatian Values in Business and Accounting Education: Towards the Formation of Ethical Leathership, Journal of Business and Educational Leadership, 2018. 1(1), P. 124-136.

[30] Razeghi, S., Sargeran, K., Fathi, F., Ahmadian, M., Emotional Intelligence and Academic Performance of Iranian Senior Dental Students, Journal of Iranian Dental Association (JIDA), 2021, 33(1), P.2001-2021.

[31] Saklofske, D., Austin, E., Mastoras, S., Beaton, L., Osborne, S., Relationships of Personality, Affect, Emotional Intelligence and Coping with Student Stress and Academic Success: Different Patterns of Association for Stress and Success, Learning and Individual Differences, 2012, 22(2), P. 251-257. Doi: 10.1016/j.lindif.2011.02.010.

[32] Samad, S., The Influence of Emotional Intelligence on Effective Leadership Among Managers in Malaysian Business Organizations, The Business Review, Cambridge, 2009, 13(1), 164.

[33] Schleifer, L. L. F., R.B., D., Metacognition and performance in the accounting classroom, Accounting Education, 2009, 24(3), P. 339–367. Doi: https://www.proquest.com/docview/210916327#.

[34] Sternberg, R., Wisdom and Education. Gifted Education Interactional, 2003, 17, P.233-248, Doi: 10.1177/026142940301700304.

[35] Thomas, G., Fabio, S., Reinhard, P., Kou, M., Andrew J, E., Intraindividual relations between achievement goals and discrete achievement emotions: An experience sampling approach, Learning and Instruction, 2016, 41, P. 115-125. Doi: 10.1016/j.learninstruc.2015.10.007.

[36] Wallace, P., Clariana, R., Perception versus Reality – Determining Business Students’ Computer Literacy Skills and Need for Instruction in Information Concepts and Technology, Journal of Information Technology Education, 2005, 4, P. 141-150. Doi:10.28945/269.

[37] Williams, K. M., Paulhus, D. L., Hare.R.D., Capturing the four-factor structure of psychopathy in college students via self-report. Journal of Personality Assessment, 2007, 88, P. 205-219. Doi: 10.1080/00223890701268074.

[38] Williams, M. L., Mac Parthaláin, N., Brewer, P., James, W. P. J., & Rose, M. T., A novel behavioral model of the pasture-based dairy cow from GPS data using data mining and machine learning techniques. Journal of Dairy Science, 2016, 3(99). P. 2063-2075. Doi: 10.3168/jds.2015-10254.

[39] Yousefi, M. R., Mousavi, S. H., Mehregan, N., Presentation of a Mathematical Model for Optimal Portfolio in the Form of a Dynamic Stochastic General Equilibrium Model for Economy of Iran: Advances in Mathematical Finance and Applications, Journal of Dairy Science, 2022, 7(1), P. 1-16. Doi: 10.22034/amfa.2020.1884853.1338 2063-2075.

[40] Zhang, D., Xie, H., He, Z., Wei, Z., & Gu, R., Emotional intelligence moderates the relationship between regional gray matter volume in the bilateral temporal pole and critical thinking disposition. Brain Imaging and Behavior, 2018. 12(2), P. 488-498. Doi: 10.1007/s11682-017-9701-3.