Lesson 4.2: Data Mapping and Preparation

Description: This lesson offered detailed guidance on mapping national qualification data to the ALM format required by the QCP. It covers field-by-field mapping instructions with practical examples from actual qualifications (e.g., the South African and Mauritian qualifications used during training).

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

• Define data mapping and explain its critical importance for the ACQF QCP.
• Describe the principles of mapping national qualification data to the African Learning Model (ALM).
• Apply field-by-field mapping instructions using practical examples from national qualifications.
• Identify common challenges in data mapping and appropriate solutions.
• Understand the role of metadata in the data mapping process.
• Explain basic ETL (Extract, Transform, Load) concepts relevant to data preparation.

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4.2.1 Understanding data mapping for QCP

What is data mapping and why is it crucial?

Data mapping is the process of establishing clear and precise relationships between data elements from a source data system (such as a country’s national qualification database or register) and their corresponding elements in a target data system (in this case, the ACQF QCP’s African Learning Model – ALM). It involves defining how data from a field in the source system corresponds to a field in the target system, including any transformations needed to ensure compatibility and consistency.

For the ACQF QCP, accurate data mapping is not just a technical exercise; it is absolutely fundamental to its success. The QCP aims to create a transparent and comparable overview of qualifications across Africa. This can only be achieved if the data from diverse national systems, each with its own structure and terminology, is accurately translated and harmonised into the common ALM format. Without meticulous data mapping:

• Comparisons between qualifications from different countries would be unreliable or impossible.
• The recognition of qualifications across borders would be hindered.
• The QCP would fail to provide a trustworthy and coherent view of the African qualifications landscape.

As previously introduced in Lesson 3.2, the African Learning Model (ALM) is the standardised data model that underpins the ACQF QCP. It defines the common set of data fields (properties), structures (classes), and controlled vocabularies that all national qualification data must be mapped to before being integrated into the QCP. A thorough understanding of the ALM is a prerequisite for successful data mapping, please visit Lesson 3.2 in case you did not review this yet.

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Principles of mapping national data to the ALM

Effectively mapping national qualification data to the ALM requires a systematic approach based on several key principles:

1. Understand source and target thoroughly – Successful mapping begins with a deep understanding of both the source data and the target model. Curators must be intimately familiar with their own national qualification data: its structure, the meaning of each field, any inherent nuances, and its quality. Simultaneously, they must develop a comprehensive understanding of the ALM’s fields, precise definitions, data type requirements, relationships between classes, and specified controlled vocabularies. The T3 training materials and the ALM data model documentation portal are essential resources for this.
2. Field-by-field mapping: It is crucial that the mapping considers each of the individual source fields and how it maps to the ALM fields.
3. Concatenating fields: combining information from multiple source fields into a single ALM field.
4. Semantic equivalence: Ensuring that the meaning of the data is preserved during mapping, i.e. mapping is not only matching field names that are similar, as these might have different meanings across the two systems
5. Utilising controlled vocabularies: where the ALM specifies controlled vocabularies (predefined lists of acceptable terms) for certain fields (e.g., qualification type, ACQF level, ESCO occupations), national data must be accurately mapped to these exact terms.
6. Mapping rules: record all decisions, transformations, and assumptions for consistency and auditing. Circulate the mapping rules among the staff working directly on the document.

When it comes to 2. Field-by-field mapping, there will most likely be a mismatch between the existing data at National Level and the ALM.
As a consequence, your data mapping will have to consider both: direct mapping and transformation of data.

Mapping and Transformation of data

Direct Mapping (1:1): the simplest case, where a source field directly corresponds to an ALM field with little to no change (e.g. the title of a qualification).
Example: The registered qualification’s title (in the KNQF): “Bachelor of Science in Crop Protection” can be directly transferred over to the title field in the QCP

Transformation: Often, source data needs to be transformed to fit ALM requirements.

This can include

1. Concatenating fields: combining information from multiple source fields into a single ALM field.
   Example: A national qualifications register might store the minimum entry requirements in several distinct fields to allow for detailed internal reporting. For instance, a BA programme might have:
   • Primary_Requirement_Type: “National Exam Result”
   • Alternative_Requirement_Type: “Equivalent NQF Qualification”
   • Alternative_Requirement_Value: “KNQF Level 6 (national diploma)”
     
     The ALM does not have separate, structured fields for each of these specific entry requirements. This information, however, is crucial for learners. The most appropriate place for it in the ALM might be a descriptive text field, such as a “More Information” note associated with the Learning Opportunity.
2. Splitting fields: dividing information from a single source field into multiple ALM fields (e.g. national qualification listing multiple learning outcomes in a single field)
   Example: To take a general example, many national qualifications registers describe all learning outcomes in a single paragraph or a bulleted list within one large text box.
   However, the ALM data model requires each Learning Outcome to be a separate, individual item, each with its own title. The curator’s task is to carefully read the source paragraph, identify each distinct outcome statement and ‘split’ it into its own Learning Outcome entry in the QCP.
3. Converting data types: changing a date format, converting text to a numeric code from a controlled vocabulary, etc.
   Example: in case of the current example (Bachelor of Science in Crop Protection), the national qualification classification (“Crop and livestock production”) must be converted to the ISCED-F 2013 standard. A curator would look up this term and find its corresponding code, 0811, which represents “Crop and livestock production”. This ensures the qualification is categorised in a way that is consistent across all countries on the platform.
4. Deriving new fields: creating information for an ALM field based on pre-established process applied to one or more source field.
   Example: the source example data doesn’t list related occupations. The curator would use the thematic area to search the ESCO database to find relevant professional roles. For a degree in Crop Protection, this could include ESCO occupations like “Agronomist” or “Crop production adviser”.

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Common Challenges in data mapping

Mapping diverse national qualification data to a common model like the ALM inevitably presents challenges. Anticipating these can help in developing effective solutions.

Semantic differences:

One of the most significant hurdles is the variation in meaning of apparently similar terms across different national education and training systems. For example, the term “credit” can represent different amounts of learning (notional hours) in different countries. Similarly, how “learning outcomes” are defined, structured, and the taxonomies used can vary widely.

Robust ALM definitions and controlled vocabularies: the ALM has very clear, unambiguous definitions for all its fields and provide comprehensive controlled vocabularies for terms that vary (e.g., qualification types, levels, credit system types).

Detailed mapping guidelines: while the QCP team also provide guidelines on mapping and the data structure of QCP, we strongly encourage the national teams to develop a common understanding of how to interpret national concepts and map them to ALM equivalents.

Contextual fields: utilise ALM’s optional “description” or “more information” fields add essential national context when a direct semantic match is imperfect

Structural Differences:

National qualification databases and registers often have very different data structures, field formats, data types, and levels of granularity compared to the ALM. For instance, one national system might store all learning outcomes for a qualification in a single large text field, whereas ALM requires each learning outcome to be a separate, structured entry. Some NQFs might have complex hierarchical relationships between qualifications and their components (modules, unit standards) that ALM may represent differently.

Transformation rules: national curators will need to develop clear transformation rules to convert their data from its source structure to the ALM structure that manual curators could follow.

Templates and tools: to foster the adherence to transformation rules, countries may develop their own templates for the mapping of qualification data to the ALM.

Keeping mapping up-to-date

National qualification systems are not static; new qualifications are added, existing ones are revised or expire, and NQF structures themselves can evolve (e.g. because of period reviews). These changes necessitate updates to the data mappings.

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Practical field-by-field mapping instructions to ALM

This section provides guidance on mapping common national qualification data elements to the African Learning Model (ALM). National curators must always refer to the latest official ALM documentation portal and QCP guidelines for definitive field names, definitions, data types, and controlled vocabularies.

You can visit the official data architecture of QCP on this website: ACQF Qualifications and Credentials Platform (QCP).

The table below present a simplified overview of the core model. For a detailed documentation and an in-depth description of classes and properties follow this link here: data.acqf-qcp.africa/exchange-model/exchange-model.html.

Class	Property	Expected value
Qualification	awarding information	Awarding Opportunity
	education level	ACQF levels
	learning outcome	Learning Outcome
	publisher	Organisation
	thematic area	ISCED-F
	title	String with language
Awarding Opportunity	awarding body or note	Organisation or Note
Learning Opportunity	default language	Language
	provided by	Organisation
	qualification	Qualification
	title	String with language
Learning Outcome	title	String with language
	related skill or note	Skill or Note
Location	spatial code	Location code
Organisation	location	Location
	name	String with language

The process of mapping national qualifications data to the ALM is best approached in a structured, phased manner. This guide outlines a three-phase process:

• Phase 1: Initiation and planning
• Phase 2: Detailed field-by-field execution of mapping
• Phase 3: Validation, implementation and sustained management

It is important to recognise that this is often an iterative process, where insights from later stages may inform refinements to earlier decisions.

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Phase 1: Initiation and strategic planning

This foundational phase sets the stage for a successful mapping project by defining its parameters, understanding the data landscape, and establishing clear rules.

Step 1.1: Defining the scope and planning of the mapping

1. Define Scope: determine the specific set of national qualifications that will be included in the initial mapping effort. This might encompass all qualifications registered on the NQF, qualifications from specific educational sectors (e.g., VET only), or qualifications at particular levels.
2. Establish success criteria: define measurable criteria against which the success of the mapping project will be evaluated. Examples include the percentage of targeted qualifications successfully mapped, achieving specific data accuracy rates post-mapping, or levels of stakeholder satisfaction with the process and outcomes.
3. Secure mandate and resources: If needed, obtain the necessary official mandates from relevant authorities and data owners to proceed with the mapping.

Step 1.2: Comprehensive analysis of data sources and structures

1. Identify data sources: locate and document all existing national systems that hold qualifications data. These may include formal databases, official registers, spreadsheets, or even document-based systems. If relevant, identify the data owners or stewards responsible for each dataset.
2. Document current structures: for each identified source, document its current data structure, including all data elements, their definitions, formats (e.g., text, date, numeric).
3. Assess data readiness: evaluate the current state of the data for its “readiness” to be mapped. This involves identifying whether data is stored in a structured (e.g. JSON-LD or a well-structured, consistently formatted excel file) or unstructured format (e.g. PDFs, doc files), if there are any significant gaps in information, inconsistencies, or quality issues that must be addressed before the mapping to ALM can effectively begin.

Step 1.3: Detailed analysis of the ALM and field specifications.

1. Thoroughly review all available documentation for the ALM system (available at ACQF Qualifications and Credentials Platform (QCP)). This includes its logical and physical data models (which show its structures and relationships), detailed field definitions, specified data types (e.g., string, integer), constraints (e.g., character limits, mandatory/optional status, accepted value lists), and the relationships defined between different data fields.

Step 1.4: Developing data mapping rules, transformation logic and validation criteria

1. Create mapping specification document: based on the detailed analyses conducted in Steps 1.2 and 1.3, develop a comprehensive mapping specification report. This document will serve as the definitive blueprint for the technical mapping process.
2. Field-to-field correspondence: for every data element identified in the national qualifications data sources, pinpoint the corresponding target field(s) in the ALM system.
3. Define transformation rules: specify any transformation logic required for specific data field that needs to be reformatted (e.g. date conversions), re-coded (e.g., national NQF level codes to ACQF levels, usage of ESCO skills) or otherwise manipulated to fit the ALM system’s requirements.
4. Establish validation criteria: formulate clear validation rules and checklist that will be applied during the mapping process and post-migration to ensure data integrity, accuracy, and compliance with ALM standards. This might take the form of drafting a detailed list for checking fields e.g. valid NQF levels, ensuring that learning outcome fields are not empty for any qualification, or verifying that all mandatory ALM fields are populated.

The thoroughness of this initial preparatory phase, particularly in achieving a deep understanding of both the source national data (Step 1.2) and the target African Learning Model (ALM) (Step 1.3), along with the meticulous development of mapping rules (Step 1.4) will ensure the efficiency and accuracy of the subsequent mapping execution. Experience shows that skipping or rushing these analytical and planning stages often leads to significant rework, data errors, project delays, and ultimately, a less reliable ALM system.

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Phase 2: Detailed field-by-field execution of mapping

This phase involves the practical, element-by-element transfer and, where necessary, transformation of data from national sources to the ALM system. This execution is meticulously guided by the mapping rules and specifications developed in Phase 1. As we have explained before, the process of field-by-field mapping is far more than a simple technical data transfer. It is an act of careful translation that demands a profound understanding of both the source context (the nuances of the national qualifications system) and the target context (the ALM’s architecture and its intended use).

Concretely, data mapping is the process of finding the correct and meaningful equivalent for each piece of national qualification information within the ACQF QCP’s African Learning Model (ALM). You will look at a specific data field in your national system (e.g., a field named Qualification Name or NQF Level) and determine its precise corresponding home in the ALM (e.g., the title property within the Qualification class, or the NQF Level field).

this process is far more than a simple technical data transfer. Misinterpreting the meaning of a source data field or applying an incorrect transformation rule can lead to data that is technically present in the ALM system but is semantically incorrect, misleading, or unusable for its intended purpose. National qualifications systems have specific, often deeply embedded, terminologies and structures. Individuals involved in the mapping process need to be, in a sense, “bilingual”, being able to handle both the national and the ACQF “language” confidently.

Below, we are recapping the main aspects to consider when performing the mapping exercise. Crucially, we see this as a conceptually different step than the manual data upload of qualifications through the QCP Curator interface (detailed under Lesson 4.4). Firstly, the mapping exercise should help you decide whether is possible to be imported automatically or not. Secondly, when interacting with the QCP Curator interface and performing the manual upload, you should already have a clear idea of how to translate your national qualification date to the ALM.

Throughout the mapping exercise, we will also introduce two mapping examples of publicly accessible qualification.

1. A qualification registered by the South African Qualifications Authority (SAQA): https://allqs.saqa.org.za/showQualification.php?id=115821
2. A qualification registered by the Mauritius Qualifications Authority (MQA): https://mqa.govmu.org/mqa/wp-content/uploads/2022/04/National-Certificate-Level-5-in-Sales-and-Marketing.pdf

Step 2.1: Mapping core qualification identifiers (core element)

• National data elements: key identifiers include the official qualification title, the national qualification code or ID, related documents (e.g. nationally stored qualification file) and webpages
• ALM target fields: The ALM system will have corresponding fields for storing the qualification title, a placeholder for your national qualification core or ID. Furthermore, it will also allow for the linking of other related documents, as well as the inclusion of a homepage URL.

Mapping Instructions:

• Titles should generally be mapped directly. The ALM can accommodate multiple languages for qualification titles, even at the same time. Thus, it is not a must to translate qualification titles to more widely used languages, although it is recommended to increase the continental comparability of qualifications.
• Ensure that national qualification codes/IDs are unique within the national context. The ALM accepts alphanumeric identifiers.
• Introduce URLs of relevant documentation for the qualification or webpages storing information externally.

Step 2.2: Mapping qualification levels and their equivalence to ACQF level

Given its importance, we discuss the mapping of qualification levels separately.

• National data elements: the assigned NQF level for the qualification, and the ACQF Level if the NQF has been formally referenced to the ACQF.
• ALM target fields: dedicated fields for the ACQF and the NQF levels separately.

Mapping Instructions:

• Map the national NQF level directly
• If the country’s NQF is officially referenced to the ACQF, map the corresponding ACQF level
• If the NQF has not yet been referenced to the ACQF, the field might be left blank. We strongly suggest to also connect the ACQF team of expert to initiate the referencing process.
• The Related occupation and Thematic area fields are using controlled lists i.e. they have pre-defined values to which your qualification details should be mapped. This step is discussed in more detail under Step 2.4 below.

Label	Example 1 – South Africa: Bachelor of Education Honours in Chemistry Education	Example 2- Mauritius: National Certificate in Sales and Marketing	Description	Comment
Title	Bachelor of Education in Chemistry Education in South Africa	National Certificate Level 5 in Sales and Marketing
Reference ID	115821
ACQF Level			An associated level of education within a semantic framework describing education levels.
NQF Level	Level 8	Level 5	An associated level of education within a semantic framework describing education levels.
Thematic area	0114 Teacher training with subject specialisation	0414 Sales and marketing	The thematic area according to the ISCED-F 2013 Classification. It should be provided using the ISCED-F controlled vocabulary.	ISCED-F 2013
Related occupation	Teaching professionals	Sales, marketing and public relations professionals	An occupation or occupational category. If provided, the value should come from a controlled vocabulary. An Occupation or Occupational Category.	Occupations | ESCO
Homepage	https://www.saqa.org.za/	http://mqa.govmu.org/
Other documents	https://allqs.saqa.org.za/showQualification.php?id=115821	https://mqa.govmu.org/mqa/wp-content/uploads/2022/04/National-Certificate-Level-5-in-Sales-and-Marketing.pdf

Step 2.3: Mapping learning outcomes (core element)

• National data elements: these are statements describing what a learner knows, understands, and is able to do upon successful completion of the qualification. This field does not necessarily have to be a learning outcome per se (e.g. many countries operate with exit level outcomes, competency-based frameworks or learning objectives or goals), however, this will need to be transformed into one as ACQF emphasizes the learning outcomes approach.
• ALM target fields:  title field for learning outcomes. At least one learning outcomes has to be entered for a given qualification.

Mapping Instructions:

• If a learning outcome-based is applied in your country: paste each of the learning outcome statements separately from the national source to the ALM target field(s).
• If you do not have learning outcomes specified: apply guidelines, such as those developed by Cedefop, for writing short, clear, and effective learning-outcomes-based descriptions.
• The Related skills field is tackled in Step 2.4, covering how fields utilising controlled values and lists should be completed.

Label	Example 1 – South Africa: Bachelor of Education Honours in Chemistry Education	Example 2- Mauritius: National Certificate in Sales and Marketing	Description	Comment
Title	1) Prepare learners for research knowledge and skills for further postgraduate studies in Masters in education in Chemistry Education or another relevant Degree in education. 2) Consolidate specialised and theoretical knowledge in Chemistry Education. 3) Develop research capacity in the methodology and techniques of Chemistry Education. 4) Provide learners with theoretical engagement and intellectual enhancement in Chemistry Education. 5) Develop a systematic array of current thinking, practice and research methods in Chemistry Education as well as their application to educational settings. 6) Ability to use research projects to conduct and report on research under the supervision of a competent and qualified academic staff.	1) Communicate product information to sales clients 2) Develop and coordinate the sales team 3) Implement personal selling strategies to achieve targeted results 4) Identify, interpret, and apply direct selling techniques and strategies 5) Demonstrate and apply accounting skills for sales operations and activities 6) Structure, develop, and manage sales territories 7) Manage sales operations to achieve objectives 8) Produce and present sales proposals 9) Provide sales administration and support services 10) Produce and coordinate sales promotion programmes 11) Implement a sales plan for product and services 12) Demonstrate and apply knowledge of sales management 13) Demonstrate and apply principles of marketing 14) Create and maintain a safe and supportive working environment 15) Demonstrate and apply professional and ethical conduct 16) Use information technology 17) Develop strategies to establish and maintain positive workplace  relationships 18) Establish social, ethical, legal, and regulatory parameters for public relations activities 19) Lead a group/team to achieve an objective(s) 20) Demonstrate an understanding of the principles of implementing and  managing an e-Commerce website 21) Demonstrate knowledge of email marketing	The title. One value per language is permitted.	Only one value per language. In the UI, each LO will have its own corresponding field. Each LO will have its own classifications of “Further details” and “Related skills”
Further Details			An additional free text note about the resource.	In the UI, each LO will have its own corresponding field.
Related skills	1) chemistry 2) teach chemistry 3) scientific research methodology 4) teach chemistry 5) education science 6) assist scientific research,	1) sales argumentation 2) team building, manage sales teams 3) implement sales strategies, sales argumentation 4) implement sales strategies 5) accounting 6) sales activities 7) set sales goals, sales strategies 8) draw up marketing and sales plan, sales argumentation 9) office administration, sales activities 10) sales strategies 11) implement sales strategies 12) manage sales channels, manage sales team 13) marketing principles 14) maintain a safe, hygienic and secure working environment 15)  follow ethical code of conduct 16) use IT tools 17) building and developing teams 18) advise on public relations 19) lead a team, 20) e-commerce systems, manage website 21) digital marketing techniques, execute email marketing	An additional free text note about the resource.	ESCO Skills types

Step 2.4: Mapping qualifications attributes to controlled lists or vocabularies (skills, competences and occupational links)

[VISUAL]

This step focuses on ensuring that where the ALM system utilises standardised lists, taxonomies, or controlled vocabularies, national data is accurately mapped to these. This is crucial for consistency, interoperability, and enabling comparative analysis over multiple countries in Africa.

As explained earlier, this will require that you familiarise yourself thoroughly with the target controlled vocabulary or classification system (e.g. international classificatory systems such as ISCED-F, ESCO as well as the ACQF-specific typologies).

In the following, we outline each of these fields, for the national mapping exercise, it is important to define a method of how your national classification is translated to these. If your national system already uses or is aligned with the target standard, the mapping may be straightforward. If not, for systematic mapping, developing or using existing national crosswalks (mapping tables) between your national classifications and the international standards is highly recommended. A third option would be to use your expert judgement and find the closest international equivalent or code on a case-by-case basis. However, this option would raise consistency concerns in how the national mapping is carried out.

Below, we present each of the national elements and the targeted controlled fields. We do not add further instructions, as it will be up to you to define how to approach the mapping exercise to these controlled fields.

A. Qualification: related thematic area and occupation

National data elements:

• Information linking the qualification to relevant (national concept) of occupations in the labour market,
• Information linking the qualification to thematic area of training or study.

ALM target fields:

• ESCO Occupations  ,
• ISCED-F 2013  for thematic area of the qualification.

B. Learning outcomes: related skills

• National Data Elements: Information linking the qualification to specific skills or broader competences
• ALM target fields: ESCO skills

C. Accreditation: type of accreditation

• National data elements: domestic accreditation and quality assurance methodologies at institutional and programme levels
• ALM target fields: ACQF-specific list of accreditation typology

D. Credit points: credit point framework

• National data elements: domestic accreditation and quality assurance methodologies at institutional and programme levels
• ALM target fields: ACQF-specific list of credit accumulation and transfer frameworks

Step 2.5: Mapping other qualification attributes

Having mapped qualification information to controlled list, the last step remains to map other attributes that provide a comprehensive picture of the qualification, beyond the core identifiers, levels, and learning outcomes already covered. These attributes add crucial context and detail to the qualification data within the ALM system.

In the examples below, we cover the all the tabs on the Curator interface which were not presented before, including other attributes of qualifications as well as the controlled fields.

Table: Mapping example of Accreditation

Label	Example 1 – South Africa: Bachelor of Education Honours in Chemistry Education	Example 2- Mauritius: National Certificate in Sales and Marketing	Description	Comment
Title	Missing information	Missing information	The title. One value per language is permitted.
Type	Missing information	Missing information	The type of accreditation. It should be provided e.g. using the ACQF Controlled List of Accreditation Types.	The ACQF controlled list for accreditation types is not finalised yet.
Accrediting Organisation	CHE – Council on Higher Education		The Quality Assuring Authority (i.e., assurer).
Accreditee	University of Venda		The organisation whose activities are being accredited.
Expiry date	30/06/2031		The date when the accreditation expires or has expired.

Table: Mapping example of Credit Points tab on the QCP

Label	Example 1 – South Africa: Bachelor of Education Honours in Chemistry Education	Example 2- Mauritius: National Certificate in Sales and Marketing	Description
Points	120	104	The credit points assigned to the learning specification.
Framework	Credit Accumulation and Transfer within the  National Qualifications  Framework	MQA CATS	The framework used to assign the credit points to the learning specification. It could be provided using the ACQF Controlled List of Credit Systems (this taxonomy is not finalised yet).

Table: Mapping example of Awarding opportunities tab on QCP

Label	Example 1 – South Africa: Bachelor of Education Honours in Chemistry Education	Example 2- Mauritius: National Certificate in Sales and Marketing	Description
Awarding Body	University of Venda		The awarding body related to this awarding activity (i.e., the organisation that issues the qualification) Only in cases of co-awarding/co-graduation, where a qualification is issued to an individual by two or more organisations, the cardinality is greater than 1.
More information			An additional free text note about the resource.

Table: Mapping example of Learning opportunities tab on QCP

Label	Example 1 – South Africa: Bachelor of Education Honours in Chemistry Education	Example 2- Mauritius: National Certificate in Sales and Marketing	Description
Title	Bachelor of Education Honours in Chemistry Education		The title. One value per language is permitted.
Provider	University of Venda		The organisation providing or directing the learning opportunity. In the case of, e.g., joint qualifications, there may be several organisations directing the learning opportunity.
Language of Instruction	English		The base language of the learning opportunity to be considered authoritative.

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Phase 3: Validation, implementation and sustained management

This final phase ensures the mapped data is accurate, properly integrated into the ALM and that mechanisms are in place for its ongoing maintenance and utility.

Step 3.1: Conduct pilot mapping exercise

[VISUAL]

• Select pilot subset: choose a representative subset of national qualifications for a pilot mapping exercise. This subset should ideally include a variety of qualification types, levels, and complexities to test the mapping rules thoroughly.
• Execute pilot: perform the end-to-end mapping process for this selected subset.
• Upload qualifications to QCP and evaluate outcomes of the pilot: this includes assessing the quality of the mapped data, the efficiency of the mapping procedures and tools used, the effectiveness and clarity of the mapping rules, and, if a frontend of the ALM system is available for testing, initial user acceptance of how the data appears.
• Refine process: based on the findings and lessons learned from the pilot, refine the mapping rules, transformation logic, data validation procedures, and any supporting documentation or tools. This iterative refinement is key to improving the quality and efficiency of the full-scale mapping.

Step 3.2: Data validation and quality assurance checks

• Perform checks: conduct manual (spot) reviews  for verifying semantic accuracy (e.g., ensuring learning outcomes are meaningful and correctly categorised, checking the appropriateness of ESCO skill mappings).
• Error management: identify, document, and systematically resolve any errors, inconsistencies, ambiguities, or missing data found during validation. This list should be continuously updated and accessible to all manual curators.

 Step 3.3: Full-scale implementation

• Execute full mapping: implement the mapping process for all national qualifications that fall within the defined scope, using the refined rules, procedures, and tools validated during the pilot phase.
• Post-upload validation: after the data has been loaded into the QCP, perform comprehensive post-migration validation checks to ensure data integrity within the QCP environment. This includes verifying record counts, checking for data corruption, and confirming that relationships between data entities are correctly established in ALM.

Step 3.4: Establishing procedures for ongoing updates and governance

• Define update processes: establish clear, documented procedures for regularly updating the qualifications data within the ALM system. This is crucial because national qualifications are dynamic – new qualifications are added, existing ones are revised, and some may be retired or replaced.
• Assign maintenance responsibilities: clearly assign responsibilities to specific individuals or teams for maintaining the accuracy and currency of the data.
• Implement version control and audit trails: develop a version control for qualification records (i.e. establishing when a qualification is to be updated, retired etc.).

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References:
ESCO Occupation Classification: https://esco.ec.europa.eu/en/classification/occupation_main

ESCO Skills Classification: https://esco.ec.europa.eu/en/classification/skill_main

ISCED-F Classification: https://uis.unesco.org/sites/default/files/documents/international-standard-classification-of-education-fields-of-education-and-training-2013-detailed-field-descriptions-2015-en.pdf

Learning Outcomes CEDEFOP: https://www.cedefop.europa.eu/en/publications/4156