A Combined Model for Prediction of Financial Software Learning Rate based on the Accounting Students’ Characteristics
Subject Areas : Multi-Criteria Decision Analysis and its Application in Financial Management
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)
Keywords: Data mining, accounting software, Accounting information system, Artificial Intelligence,
Abstract :
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.
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