A 2-Phase MCDM Approach to Lifelong Learning Policies for Mid-Career individuals in Singapore

Chen Xinjing

Abstract

Academic research on Lifelong learning has increased comprehensively in the past decade. Policies, programmes and initiatives are implemented by Governments to promote learning. As a Multi-Criteria Decision-Making (MCDM) problem, many different techniques and models have been executed to examine and analyse the effectiveness of the policies. However, due to the complexity of the problem, models lack the capability of illustrating full realisticity. Therefore, the purpose of this paper is to propose a 2-Phase MCDM Approach to Lifelong Learning Policies. It aims to evaluate and produce an effective tool for finding the best policy.

The paper would also look into three aspects: Application, Methodology and Modelling. The proposed model is tested through a case study of a significant problem in Singapore. Due to the growing digitalised economy, Professionals, Managers, Executives and Technicians (PMETs) Mid-Career Individuals (MCI) in Singapore are significantly impacted, the structural unemployment rate is high in this group. The Singapore Government has implemented policies to tackle the issue. This paper examined the effectiveness of the policies implemented by the Government and the results emphasise the exceptional job done by them. To extend the effectiveness of the policies, a merger has been recommended. Based on the analysis, findings showed improvement in weight, implying the efficacy of the idea.

The proposed methodology consists of two phases: AHP followed by ANP. In many papers, AHP is the favoured model adopted to tackle MCDM. It is easily approachable but lacks complexity. Therefore, ANP is introduced to cover the interdependence between various criteria. In the proposed model, a 2-phase approach is used. Incorporating the benefits of AHP, relaxation is executed, providing an insightful learning process. With the skeleton formed, ANP is used to acknowledge the connections between factors and refine the model. The 2-Phase model obtained the best policy fitting the policies announced in Budget 2022, indicating the success in both methodology and modelling.

The methodology was able to assist multiple stakeholders in analysing. Moreover, it considers the real-world context of interdependence. The 2-Phase model could be applied to other MCDM problems, assisting more stakeholders in decision-making.