Enterprise architecture (EA) is a coherent planning method and a uniform way to describe practices and models for organizations in different stages of the development process. The objective of the EA work is to improve interoperability of activities and services of public administration and private organizations. The main idea of the CompLeap framework architecture can be summarized in this video:
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Table of Contents
Below you can find links to the full framework architecture model for the learner-centered service development and learner plan.
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The learner's pathway can be seen as a value stream which increments learner's competences and, thus, value in some sense. While the value stream is from learner's perspective, the services the learner uses on the path are produced by other actors. The services the learner uses are pinned on the path to tell when the learner is able to use that particular service. By expanding achitecture of the services the framework guides what is needed to implement the services and it is up to a user of the framework architecture to define how the services are actually implemented. To guide in implementation the framework architecture includes an example how this framework is implemented in Finland.
To whom this document is meant for
The document This documentation is meant for decision makers, strategy leaders and developers (education and employment services).
Conventions in the document - Compleap Framework Architecture Meta Model
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The conceptual data model does not tell how the information is actually structured or modeled. From the framework perspective the concepts are recognized as something which is needed to make data flow smoothly among processes.
Analytics Data flow
Data flow model in the Compleap project illustrates how data flows in the Compleap user services. Information about formal education, previous work history is gathered automatically from other systems and databases. Information about non formal and informal education as well as interests comes from the manual user (learner) input. Specific data set is extracted and used as a data combination to visualize competences for the user and at the same time stimulate his reflection on his current competence situation. User can also manually select data combination and filters to get a education recommendation. This is content based recommendation. 10 best results are presented to the user where he can further select his favorites and proceed to learners path visualization where information about education option is visualized for the user again to promote his understanding of the studying and also self-reflection. User is also able to give feedback about competence and learners path visualizations as well as education recommendations.
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Prototype implements only partial solution of above described reference architecture. Analytics data flow diagram below is modified to describe prototype architecture.
User comes to landing page and either logs in or uses system without logging in. If user logs in and has SSN (HETU) then all user related data is brought automatically from various data sources to dataset combination.
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