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A neural network approach for assessing quality in technical education: an empirical study

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  • S.S. Mahapatra
  • M.S. Khan

Abstract

The diverse nature of requirements of stakeholders in a Technical Education System (TES) makes it extremely difficult to decide on what constitutes quality. Hence, identification of common minimum quality items suitable to all stakeholders will help to design the system and thereby improve customer satisfaction. To address this issue, a measuring instrument known as EduQUAL is developed and an integrative approach using neural networks for evaluating service quality is proposed. The dimensionality of EduQUAL is validated by factor analysis followed by varimax rotation. Four neural network models based on back-propagation algorithm are employed to predict quality in education for different stakeholders. This study demonstrated that the P-E gap model is found to be the best model for all the stakeholders. Sensitivity analysis of the best model for each stakeholder was carried out to appraise the robustness of the model. Finally, areas of improvement were suggested to the administrators of the institutions.

Suggested Citation

  • S.S. Mahapatra & M.S. Khan, 2007. "A neural network approach for assessing quality in technical education: an empirical study," International Journal of Productivity and Quality Management, Inderscience Enterprises Ltd, vol. 2(3), pages 287-306.
    • Handle: RePEc:ids:ijpqma:v:2:y:2007:i:3:p:287-306
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    Cited by:

    1. Růžena Lukášová & Daria Kuchařová, 2019. "Criteria of Satisfaction with Universities from the Perspective of Czech Students: A Qualitative Research Study," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 67(4), pages 1049-1060.
    2. Kaushik Mandal & Chandan Kumar Banerjee & Iwona Otola, 2019. "Quest for a New Instrument for Measuring Academic Program Quality," Research in World Economy, Research in World Economy, Sciedu Press, vol. 10(4), pages 40-47, December.
    3. Natthan Singh & Millie Pant & Amit Goel, 2018. "ANN embedded data envelopment analysis approach for measuring the efficiency of state boards in India," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(5), pages 1092-1106, October.
    4. Daniela Carlucci & Paolo Renna & Giovanni Schiuma, 2013. "Evaluating service quality dimensions as antecedents to outpatient satisfaction using back propagation neural network," Health Care Management Science, Springer, vol. 16(1), pages 37-44, March.

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