Lesson 3.3: Interoperability Standards and Protocols

Description: In this lesson, you will explore interoperability and its different dimensions (technical, structural, semantic, organisational) in the context of the QCP.

By the end of this lesson, you will be able to:

• Explain the concept of interoperability and its different dimensions (technical, structural, semantic, organisational) in the context of the QCP.
• Describe the importance of structured data and data standards as prerequisites for interoperability.
• Identify key interoperability standards and protocols used in the QCP, such as JSON-LD, controlled vocabularies, and APIs.
• Recognise how these standards facilitate seamless data integration and understanding across diverse national systems and the continental platform.
• Name lessons from international frameworks, such as the EQF, for achieving qualification data interoperability.

Controlled vocabularies

As mentioned previously, controlled vocabularies are essential tools for achieving semantic interoperability within the QCP.

QCP utilises the following controlled lists:

• Accreditation.type (Controlled List of Accreditation Types).
  • Example: http://data.europa.eu/snb/accreditation/003293d2ce (Institutional Licence)
• Credit Point.framework (Credit point frameworks)
  • Example: https://data.acqf-qcp.africa/model/creditpoint-framework/resource/acts (African Credit Transfer System)
• Learning Entitlement Specification.limit national occupation (ESCO Occupations)
  • Example: http://data.europa.eu/esco/occupation/25206eaa-04da-4fd6-bac7-173d7eb142dc (casting machine operator)
• Learning Opportunity. default language  (Language Named Authority List):
  • Example: http://publications.europa.eu/resource/authority/language/POR (Portuguese)
• Learning Outcome. related skill (ESCO Skills)
  • Example: http://data.europa.eu/esco/skill/c7bb240e-4e08-47d2-a4ed-b85185773d3a (polish silverware)
• Location.spatial code (Countries and Territories Authority List):
  • Example: http://publications.europa.eu/resource/authority/country/MUS (Mauritius)
• Qualification.educational level (ACQF levels, as well as NQFs)
  • Example: https://data.acqf-qcp.africa/model/acqf-levels/resource/level-1 (Level 1)
• Qualification.thematic area (ISCED-F 2013)    
  • Example: http://data.europa.eu/snb/isced-f/0110 (Education not further defined)
• Qualification.type (Qualification types — a custom list created for the ACQF QCP)

As you can see, these lists draw on various classification systems. Below, we briefly present the main ones:

ESCO: the European Skills, Competences, Qualifications and Occupations (ESCO) classification is a large-scale, multilingual example of a system to categorise occupations, skills, qualifications, and competences. Originally, it was developed for the European context, but it is applicable globally. Its current version (1.2.1, released in December 2025) contains around 14,000 skills and 3,000 occupations, available in 28 languages.

ISCED-F 2013: a classification system developed by UNESCO to organise education and training programmes and qualifications by their subject matter. It provides a hierarchical structure, moving from broad fields of education and training down to more detailed and specific areas.

APIs (Application Programming Interfaces): APIs are sets of rules and protocols that allow different software applications to communicate and exchange data with each other automatically. For the QCP, APIs are a crucial mechanism for enabling efficient and scalable data integration from national qualifications databases. The platform is designed to support both manual data entry via a curator interface and automated data import features, specifically for countries that already have existing, structured digital qualifications databases and wish to make them publicly accessible through the QCP.

In this context, an API acts as a secure bridge, allowing a country’s national database to programmatically connect and send its qualification data directly to the QCP. This process enables automated, periodic data uploads instead of manual data entry. Manual entry remains the better choice for countries without established digital databases. By using an API, national systems can keep their data on the continental platform synchronised and up-to-date. This supports the QCP’s goal of creating a unified, reliable source of qualifications information that fosters transparency, comparability, and mobility for learners and workers across the continent.

File formatting and extension standards

For systems to exchange data effectively, they must agree on the format in which that data is structured and presented. The choice of file format significantly affects how easily data can be created, parsed, validated, and understood by different systems.

JSON-LD (JavaScript Object Notation for Linked Data): a lightweight, modern data format that builds upon the widely used JSON format to support Linked Data principles. The QCP’s adoption of JSON-LD is significant for enabling data to interoperate at a web scale.

For countries with an existing structured qualifications database, JSON-LD is the preferred format for exchanging qualification data with the QCP. It acts as a bridge between the formally structured African Learning Model (ALM) and the practical needs of web-based data exchange, making it easier for diverse national systems to prepare and share their qualification information.

XML (Extensible Markup Language): while JSON-LD is preferred for its modern web compatibility and ease of use with JSON-based tools, XML has historically been a common standard for structured data exchange. Some legacy systems or other international standards might still use XML. The principles of clear structure and agreed-upon schemas are common to both.

CSV (Comma Separated Values): for bulk data import or simpler datasets, CSV files are often used due to their simplicity. However, CSV inherently lacks the structural and semantic richness of JSON-LD or XML. If data is provided in CSV, it would typically need significant transformation and enrichment to meet the ALM’s requirements for structured, linked data before being integrated into the QCP.

XLSX: Excel’s flexibility makes it widely used for data handling, but that same flexibility often leads to poorly structured workbooks. A “well-structured” Excel file is organised in a way that supports clarity, analysis, and machine processing, extending beyond its file extension. A well-structured Excel file requires that:

• each variable has its own column
• each observation (e.g. a qualification) has its own row
• each value has its own cell (atomic data)

International compatibility

As introduced in Lesson 3.2, the African Learning Model (ALM) is the core data model underpinning the QCP. It defines how qualification information is structured and described. It is designed not only to meet the specific needs of the African context but also with international compatibility in mind, drawing inspiration from and aiming for interoperability with established international models.

For example, the European Learning Model (ELM) offers a comprehensive framework for describing and assessing learning outcomes and qualifications in Europe. The ALM takes a similar approach: it describes credit systems, detailed learning outcomes, and relationships within qualifications frameworks. This helps the ACQF QCP boost student and worker mobility. It also supports the transfer and recognition of credits and qualifications across African educational institutions and with other regions. Ultimately, a well-defined, internationally-aware data model like the ALM promotes transparency, supports quality assurance, and fosters mutual recognition — all vital for achieving greater mobility and cooperation in education and employment across the continent.

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Self-assessment Activity

Diagnosing interoperability challenges

Consider the following scenarios that might occur when working with the ACQF QCP. For each scenario, identify which dimension(s) of interoperability (technical, structural/syntactic, semantic, organisational) are primarily affected and briefly explain why.

1. The national authority in Country Z has successfully mapped its qualifications to the ALM and prepared a well-structured excel file. However, the staff are unsure about the official procedure for submitting this file to the QCP and who within their agency needs to give final approval before transmission.scussed—such as the African Learning Model (ALM), controlled vocabularies like ESCO, and data formats like JSON-LD—are the practical instruments we use to build these layers. They are the blueprints that ensure data is not just exchanged, but truly understood across diverse national contexts. Ultimately, the rigorous application of these interoperability principles is what transforms the QCP from a simple database into a dynamic engine for continental integration. It ensures that a qualification’s value is transparent and trusted, whether viewed by a learner in Luanda, an employer in Nairobi, or a university in Accra.
   
2. National System A attempts to send its qualification data file to the QCP, but the transmission fails with a “connection refused” error.
   
3. The QCP receives a data file from National System B. The file is in JSON format, but the QCP system cannot process it because the fields are named differently from what the ALM specifies, and some mandatory ALM fields are missing.
   
4. The QCP displays “Advanced Diplomas” from Country X and Country Y. Both are listed as “ACQF Level 6.” However, upon closer inspection of their learning outcomes and typical duration, it’s clear that the “Advanced Diploma” from Country X has a significantly higher level of complexity and learning volume than that from Country Y.

Answers (open after trying the scenarios)

Scenario 1 – technical interoperability: A ‘connection refused’ error means the systems cannot even establish a basic connection – the foundational technical layer has failed before any data or meaning is exchanged.

Scenario 2 – structural (syntactic) interoperability: The file format (JSON) is right, but the QCP cannot parse it because the field structure does not match what the ALM expects – a breakdown in structure, not meaning.

Scenario 3 – semantic interoperability: Both qualifications are labelled ‘ACQF Level 6’, but they do not actually stand for the same level of learning – the data is technically present and structurally correct, but its meaning isn’t truly comparable.

Scenario 4 – organisational interoperability: The data itself is ready and correctly structured – what’s missing is agreement on process: who submits it, and who approves it. A breakdown in organisational alignment, not technology or data.

As we conclude this lesson, it is clear that interoperability is the essential thread that weaves together the technical infrastructure and the policy ambitions of the ACQF Qualifications and Credentials Platform. From the foundational ‘handshake’ between systems (technical) to the shared grammar of data (structural), the crucial agreement on meaning (semantic), and the vital framework of human trust and processes (organisational), each layer is indispensable. The standards and protocols discussed – such as the African Learning Model (ALM), controlled vocabularies like ESCO, and data formats like JSON-LD – are the practical instruments we use to build these layers. They are the blueprints that ensure data is not just exchanged but truly understood across diverse national contexts.

Ultimately, the rigorous application of these interoperability principles is what transforms the QCP from a simple database into a dynamic engine for continental integration. It ensures that a qualification’s value is transparent and trusted, whether viewed by a learner in Luanda, an employer in Nairobi, or a university in Accra.

References

ISCED Fields of Education and Training 2013 (ISCED-F 2013) – UN Statistics Division Classifications Registry

JSON-LD 1.1 – W3C Recommendation

QCP Training Unit 5: Structured Data, Data Standards and Interoperability