A multi-criteria decision support system to evaluate the effectiveness of training courses on citizens' employability

TL;DR

The paper addresses assessing the impact of lifelong-learning training on employability using a scalable, data-driven framework. It introduces a four-stage pipeline: (i) construct working-life curves $WLC_i(t) = \dfrac{N(t)}{t}$, (ii) build individualized control groups via $K$-medoids clustering, (iii) assess post-course performance on four cost-type criteria using $t$-tests, and (iv) rank courses with an unweighted TOPSIS variant that uses weight bounds and reports interval-based scores $[R_i^-, R_i^+]$ with $R_i^{uw} = k_1 R_i^- + k_2 R_i^+$. Key findings show positive employability effects for courses in administration/management (ADM), hospitality/tourism (HOT), and community and sociocultural services (CSS), but the most effective courses are not the most demanded, highlighting a gap between popularity and impact. The approach is fully data-driven, scalable to large regional datasets, and provides policymakers with objective guidance for resource allocation and program targeting to improve citizens’ working life and employability.

Abstract

This study examines the impact of lifelong learning on the professional lives of employed and unemployed individuals. Lifelong learning is a crucial factor in securing employment or enhancing one's existing career prospects. To achieve this objective, this study proposes the implementation of a multi-criteria decision support system for the evaluation of training courses in accordance with their capacity to enhance the employability of the students. The methodology is delineated in four stages. Firstly, a `working life curve' was defined to provide a quantitative description of an individual's working life. Secondly, an analysis based on K-medoids clustering defined a control group for each individual for comparison. Thirdly, the performance of a course according to each of the four predefined criteria was calculated using a t-test to determine the mean performance value of those who took the course. Ultimately, the unweighted TOPSIS method was used to evaluate the efficacy of the various training courses in relation to the four criteria. This approach effectively addresses the challenge of using extensive datasets within a system while facilitating the application of a multi-criteria unweighted TOPSIS method. The results of the multi-criteria TOPSIS method indicated that training courses related to the professional fields of administration and management, hostel and tourism and community and sociocultural services have positive impact on employability and improving the working conditions of citizens. However, courses that demonstrate the greatest effectiveness in ranking are the least demanded by citizens. The results will help policymakers evaluate the effectiveness of each training course offered by the regional government.

Figures (12)

• Figure 1: Proposed methodology for developing a multi-criteria decision support system
• Figure 2: Example of a working life curve (WLC) for a person. An increasing WLC indicates that the person is employed.
• Figure 3: Methodology of obtaining a control group for a given person taking course $C_j$.
• Figure 4: Distribution of the potential sizes of the control groups. The mean (dashed line) and median (dotted-dashed line) are represented by vertical lines.
• Figure 5: Examples of working life curves of four individuals (black thick lines) and their final control groups (grey lines). The vertical dashed line represents the moment the participants took the course. For clarity, only up to 50 nearest neighbours were plotted.

Theorems & Definitions (2)

• Remark 4.1
• Definition 4.1