The contribution of the MERISE-type Conceptual Data Model (CDM) to the construction of indicators for monitoring and evaluating the effectiveness of training in relation to the needs of the labor market in the Republic of Congo is essential to creating a structured and clear representation of data. This approach makes it possible to identify the relationships between the different entities involved in the training and employment process, thus facilitating the assessment of the adequacy between the training received and the requirements of the labor market.

Main actors necessary for assessing the match between training and employment.

The construction of the Conceptual Data Model (CDM) structures key information and facilitates the development of the Logical Data Model (LDM) to optimize data management.

This step is crucial for implementing the integrated framework for life-cycle assessment and environmental management systems discussed by O'Connor & Lundie (2001) [3] .

Description: Percentage of graduates who found employment after the end of their training.

$\text{Employment Rate}=\frac{\text{Total}\text{number}\text{of}\text{graduates}}{\text{Number}\text{of}\text{employed}\text{graduates}}\times 100$

Necessary Data:

Description: Average time elapsed between the end of training and obtaining a job by graduates.

$\text{Average Placement Time}=\frac{{\displaystyle \sum \left(\text{Start}\text{date}\text{of}\text{employment}-\text{End}\text{date}\text{of}\text{training}\right)}}{\text{Number}\text{of}\text{employed}\text{graduates}}$

Necessary Data:

Description: Percentage of graduates occupying jobs consistent with the skills acquired during their training.

$\text{Relevance Rate}=\frac{\text{Total}\text{number}\text{of}\text{employed}\text{graduates}}{\text{Number}\text{of}\text{graduates}\text{in}\text{relevant}\text{jobs}}\times 100$

Necessary Data:

Description: Percentage of employers satisfied with graduates’ skills.

$\text{Satisfaction Rate}=\frac{\text{Total}\text{number}\text{of}\text{employers}}{\text{Number}\text{of}\text{satisfied}\text{employers}}\times 100$

Necessary Data:

Description: Percentage of graduates remaining employed after a given period (e.g. 6 months, 1 year).

$\text{Retention Rate}=\frac{\text{Total}\text{number}\text{of}\text{diplomas}\text{employed}}{\text{Number of graduates remaining}\text{employed}}\times 100$

Necessary Data:

Description: Percentage of graduates who created their own business after the end of their training.

$\text{Business Creation Rate}=\frac{\text{Total number of graduates}}{\text{Number of graduate entrepreneurs}}\times 100$

Necessary Data:

Description: Percentage of students who abandoned the training before completing it.

$\text{Withdrawal Rate}=\frac{\text{Total number of students enrolled}}{\text{Number of students who withdrew}}\times 100$

Necessary Data:

Description: Average salary of graduates who have found employment, by type of training and sector of activity.

$\text{Salary Level}=\frac{{\displaystyle \sum \text{Graduate Salary}}}{\text{Number of employed graduates}}\times 100$

The formulation and calculation of these indicators for analysis are aligned with the indicators for effective training identified in European contexts by Roelofs & Sanders (2007) [4] .

Improve the match between training and the needs of the labor market using the MERISE conceptual model.

The collection of information from sources dealing with training and employment situations made it possible to identify the following information:

The data dictionary describes the different entities, attributes, and their descriptions necessary for monitoring the match between training and employment. The structure aligns with the methodologies for evaluating vocational training programs discussed by Deschamps & Pierre (2018) [7] and Bertrand (2006) [8] .

This dictionary consists of the following information:

This data dictionary ensures that the information needed for monitoring the match between training and employment is well defined and structured, thus facilitating the collection, analysis, and use of data to improve training programs.

For a CDM adapted to monitoring the match between training and employment, the identification and description of entities are more detailed, as recommended by Levinson (2010) [9] and Zachman (1987) [10] in their frameworks for data analysis and information systems architecture.

Here is a more detailed and optimized identification and description of the entities:

Entities and Attributes

The relationships between entities are crucial for structuring and organizing data, as outlined by Kettinger, Teng, & Guha (1997) [11] and Hoffer, George, & Valacich (2013) [12] .

R1. Student - Student_Training

(A student can follow several training courses, a training course can be followed by several students)

R2. Training - Student_Training

(One training course can be followed by several students)

R3. Training - Institution

(A course belongs to a single institution, an institution offers several courses)

R4. Employer - Employment

(An employer can offer several jobs)

R5. Job - Student_Job

(A job can be held by several students at different times)

R6. Student - Student_Job

(A student can hold several jobs)

R7. Training - Training_Skill

(One training course can teach several skills)

R8. Competence - Training_Competence

(A skill can be taught in several training courses)

R9. Employer - Satisfaction_Survey

(An employer can complete several satisfaction surveys)

R10. Student - Satisfaction_Survey

(A student can be evaluated in several satisfaction surveys)

Figure 1 represents the MERISE modeling that we carried out in relation to the construction and monitoring of efficiency evaluation indicators of training in relation to the needs of Labor market in the Republic of Congo.

The indicators constructed from the attributes of the entities of the MERISE conceptual model make it possible to:

1) Measure the Effectiveness of Training:

2) Evaluate Employer Satisfaction:

3) Analyze Stability and Job Creation:

These indicators provide a clear view of the strengths and weaknesses of training programs, thus enabling continuous improvement of curricula.

MERISE’s methodological approach finds its roots in foundational works such as Chen on the entity-association model and De Marco on structured systems analysis. The use of the Conceptual Data Model (CDM) within the MERISE framework allows for precise modeling of data and processes, essential for developing relevant monitoring and evaluation indicators.

Avison and Fitzgerald emphasize the importance of robust methodologies in information systems development, and how these methodologies can meet the specific needs of end users, such as professional training decision-makers. Integrating these principles, this study utilizes MERISE to develop indicators to assess training effectiveness relative to labor market needs.

Levinson and Zachman underscore the crucial role of data analysis and system architectures in informed decision-making. By applying these concepts, this research illustrates how the MERISE model can adjust training programs based on labor market trends and employers’ skill requirements.

Furthermore, Kettinger, Teng, and Guha on business process change and Hoffer, George, and Valacich on modern systems design provide a theoretical framework for applying MERISE in optimizing training processes. This work supports the idea that continuous improvement of training programs, guided by well-defined performance indicators, can enhance alignment between training offers and labor market needs, thereby increasing employability.

The integration of this bibliography highlights the significance of conceptual models and rigorous methodologies in analyzing and improving professional training systems. It demonstrates how the MERISE approach can be applied to develop contextually relevant solutions, such as in the Republic of Congo.

Contributions of the MERISE-type Conceptual Data Model (CDM):

Limitations of the MERISE Approach:

Practical Implementation of Results:

Use of Conceptual Models in Educational and Professional Contexts: Conceptual models like MERISE have been utilized in various educational and professional contexts to structure and analyze complex data. Notable examples include:

Contributions and Differences of this Study: This study differs from previous work in several ways: