A Novel Prediction Model for Educational Planning of Human Resources with Data Mining Approach: A National Tax Administration Case Study

Autor/inn/en	Arfaee, Mohammad; Bahari, Arman; Khalilzadeh, Mohammad
Titel	A Novel Prediction Model for Educational Planning of Human Resources with Data Mining Approach: A National Tax Administration Case Study
Quelle	In: Education and Information Technologies, 27 (2022) 2, S.2209-2239 (31 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Khalilzadeh, Mohammad)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-021-10699-6
Schlagwörter	Prediction; Models; Educational Planning; Data Analysis; Staff Development; Decision Support Systems; Job Performance; Promotion (Occupational); Job Layoff + Suchen Sie Ihr Suchwort? Vorhersage; Analogiemodell; Bildungsplanung; Auswertung; Personnel development; Personalentwicklung; Entscheidungshilfe; Work performance; Arbeitsleistung; Aufstiegsberuf; Berufsförderung; Beurlaubung
Abstract	Human resources training is considered an effective solution in empowering human resources. Organizations try to have effective educational planning for this precious resource by identifying shortcomings through a need assessment. This study provides a model based on organizational data analysis to achieve a unique and appropriate training planning for each staff. Therefore, job performance, organizational promotion and lay-off have become the basis for staff training planning. For this purpose, the tax assessor's information was investigated. Then, the CRISP-DM methodology was selected, and the project was implemented. Furthermore, a decision tree model was selected to extract unknown rules and patterns in the educational decision-making staff; the neural network model was selected as the predictive model to predict the target variables. The results revealed the decision tree for predicting job performance variables and organizational promotion status, and the neural network model was more effective in predicting service lay-off variables. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01