Innovative Applications of O.R.
Capturing and prioritizing students’ requirements for course design by embedding Fuzzy-AHP and linear programming in QFD

https://doi.org/10.1016/j.ejor.2014.02.042Get rights and content

Highlights

  • Addresses the issue of prioritization of students’ requirements in the context of course design.

  • Integrates DEA based LP approach with Fuzzy AHP within the QFD framework.

  • Sensitivity analysis is employed to examine the effects of parameter values modification on the prioritization solution.

  • Implications for course design specifications are discussed.

Abstract

Customer requirements play a vital and important role in the design of products and services. Quality Function Deployment (QFD) is a popular, widely used method that helps translate customer requirements into design specifications. Thus, the foundation for a successful QFD implementation lies in the accurate capturing and prioritization of these requirements. This paper proposes and tests the use of an alternative framework for prioritizing students’ requirements within QFD. More specifically, Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) and the linear programming method (LP-GW-AHP) based on Data Envelopment Analysis (DEA) are embedded into QFD (QFD-LP-GW-Fuzzy AHP) in order to account for inherent subjectivity of human judgements. The effectiveness of the proposed framework is assessed in capturing and prioritizing students’ requirements regarding courses’ learning outcomes within the process of an academic course design. Sensitivity analysis evaluates the robustness of the prioritization solution and implications for course design specifications are discussed.

Section snippets

Introduction and motivation

Quality Function Deployment (QFD) is employed towards the direction of developing and delivering flawless services based on customer requirements. QFD driven by the “voice of the customer” provides a detailed structured process, utilizing a series of planning matrices – Houses of Quality (HOQ) (Cohen, 1995) for service providers to interpret customer requirements into palpable service features and communicate quality throughout the organization assuring customer satisfaction whilst maintaining

Quality in higher education

The primary role of higher education institutions is to generate, enhance, preserve and disseminate knowledge (Clarke, Hough, & Stewart, 1984). However, higher education not only facilitates the acquisition of the desired professional qualifications through a strict study process but also fosters the intellectual development of students, influencing their lives in the long run (Norris, 1978). In line with industry principles, it has been suggested that the ultimate goal of higher education

Integrating LP-GW-Fuzzy AHP into QFD

Reported limitations of QFD (Andronikidis et al., 2009, Bouchereau and Rowlands, 2000) prompted the need for improvements by incorporating other analytical tools such as the Analytic Hierarchy Process (AHP). Specifically, QFD is a planning and development support method, powerful in designing high-quality services (Mazur, 2008). On the other hand, AHP is a versatile and widely used decision making method (Ho, 2008, Madu and Georgantzas, 1991, Shahin and Mahbod, 2007, Zhou et al., 2006) that can

Developing the framework

In this study, we focus on expectations and preferences of a particular customer type in higher education, namely, undergraduate business students. First and fourth-year university students of business administration were asked to express their preference on the expected learning outcomes in selected courses. The objective was to identify desirable learning outcomes based on what students expect to learn, understand, and be able to do by the end of a course and to prioritize learning outcomes

Sensitivity analysis and discussion

The QFD-LP-GW-Fuzzy AHP produced the prioritization and importance weights of the learning outcomes appearing in the first row of Table 5. Specifically, “Key transferable skills” was the learning outcome with the highest preference with a percentage priority of 31.67%. The second learning outcome in ranking is “Practical-based Knowledge”, with a percentage priority of 30.98%. “Generic Academic skills”, follows with a percentage priority of 26.02% and finally, “Theory-based Knowledge” was found

Concluding remarks

This work proposed a framework for interpreting and prioritizing customer preferences in the context of higher education. Limited cognitive processes, incomplete information and finite amount of time, prevent decision-makers from adequately scrutinize all the factors in a complex decision-making process. The interpretation and prioritization of important but ill-defined customer requirements are essential steps in order to take further actions to improve the quality of an offered service.

Acknowledgements

This research has been co-financed by the European Union (European Social Fund – ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) – Research Funding Program: Heracleitus II. Investing in knowledge society through the European Social Fund.

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