Predicting academician publication performance using decision tree.

Mohd Zakree Ahmad Nazri, Rohayu Abd Ghani, Salwani Abdullah, Mas Ayu, R. Nor Samsiah

Research output: Contribution to journalArticle

Abstract

This research focuses on predicting academician performance in terms of publication rate and investigate the factors that affect academicians’ achievement. This study investigates how scientific publication rate by individual is influenced by factors such as gender, age, number of research grant and academic position of the researchers. Having a decision rules, university leaders can understand upcoming trends with respect to leadership requirements and academicians needs. It is also helping university managements understand challenges and therefore can deploy the right strategies for human resource management interventions. This paper describes the development of the predictive model using a data mining technique. Previous studies have shown that there are many important variables when analysing academicians’ productivity at the individual level. What is unusual with our approach is that this study is using Decision Tree to identify the patterns for predicting next year’s performance. Decision Tree, C4.5, J48 and PART is a common predictive method for prediction as there are other methods that are better suited for predictive analytics such as regression or metaheuristic algorithms. However, with finding knowledge among the attributes obtained from the university’s databases, we can predict the performance of an academician staff. To find strong and valid rules, different measures like min Interest, lift, leverage and conviction are considered. The study, involving almost 3000 university lecturers, shows a number of interesting patterns that can be used for predicting individual performance.

Original languageEnglish
Pages (from-to)180-185
Number of pages6
JournalInternational Journal of Recent Technology and Engineering
Volume8
Issue number2 Special Issue
Publication statusPublished - 1 Jul 2019

Fingerprint

Decision trees
Human resource management
Data mining
Productivity
Decision tree
Factors
Predictive analytics

Keywords

  • Higher Education Institution
  • Predictive Analytics
  • Tree to Rule Induction

ASJC Scopus subject areas

  • Engineering(all)
  • Management of Technology and Innovation

Cite this

Ahmad Nazri, M. Z., Ghani, R. A., Abdullah, S., Ayu, M., & Nor Samsiah, R. (2019). Predicting academician publication performance using decision tree. International Journal of Recent Technology and Engineering, 8(2 Special Issue), 180-185.

Predicting academician publication performance using decision tree. / Ahmad Nazri, Mohd Zakree; Ghani, Rohayu Abd; Abdullah, Salwani; Ayu, Mas; Nor Samsiah, R.

In: International Journal of Recent Technology and Engineering, Vol. 8, No. 2 Special Issue, 01.07.2019, p. 180-185.

Research output: Contribution to journalArticle

Ahmad Nazri, MZ, Ghani, RA, Abdullah, S, Ayu, M & Nor Samsiah, R 2019, 'Predicting academician publication performance using decision tree.', International Journal of Recent Technology and Engineering, vol. 8, no. 2 Special Issue, pp. 180-185.
Ahmad Nazri, Mohd Zakree ; Ghani, Rohayu Abd ; Abdullah, Salwani ; Ayu, Mas ; Nor Samsiah, R. / Predicting academician publication performance using decision tree. In: International Journal of Recent Technology and Engineering. 2019 ; Vol. 8, No. 2 Special Issue. pp. 180-185.
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