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Information flow described above leads to five two main services provided: Competence visualization, mapping and education recommendation, education comparison and study guidance.
- Competence visualization mapping - there are certain competences acquired during the study process. These competences are usually described in the national curriculum and are at the core important part of the modern world of work and study. Although they are learnt and specified in documentation many students don't have enough information about them or maybe haven't even heard about them at all. Compleap service provides an opportunity to see clearly what competences in a form of study modules have been gained during the study process in educational institution. Mapping of Finnish national curriculum to ESCO competences is also done in this part of the project.
- Education recommendation - education recommendations are calculated and based on similarity of content between user's profile data and education descriptions.Education comparison - closest Closest matches of between user's profile and education descriptions are presented to the user in ranking. Starting from the closest one to the less close and so on. User can mark some recommendations as favourite favorite and this way generate more suitable suggestion.
- Learner’s path visualization – is created on the basis of users selected education and is mainly comprising of, for e.g. parts of the study (fin. tutkinnon osat) visualized for the user in a sequence leading to the completion of the studies. Study guidance - the
The whole service is seen as a study guidance and support for decision making when choosing suitable vocational education and training. Information about own competences, previous
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studies and interests are gathered in one place and presented in a user-centred way to promote his reflection and guide him to studying possibilities and the world of education in general. Education recommendation is provided not as a solution but as an encouragement for the person to think and reflect on his interest and future educational and work-related goals. User is able to give feedback on the usefulness and accuracy of the profile and education recommendations to further develop these services.
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At the moment, to learner’s path visualization is left out of the scope of the Compleap project. Two main parts of learning analytics In Compleap are currently developed – competence visualizations mapping and education recommendation. More detailed information on these services, desk and user research is available is the following documents: competence visualization, education recommendation, summary of research activities in Compleap. Competence visualizations are currently left out of the scope of the project because of the lack of resources, and the gained competences are provided in the form of the study modules. These are also mapped to ESCO competences.
Timetable
As for now, piloting of competence visualizations mapping and education recommendation services is seen as a two-step process.
2019, Spring (this has to be renewed in accordance with current development of the project)
Piloting of competence visualization
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This part of the piloting requires data flow in the Compeap system. It requires information to be available from specified databases as well as calculated algorithms to be functioning to provide individualized education recommendation based on user profile information. There is a need for this recommendation to be based mostly on the content of the user profile and user behavior (favoring and marking some of the recommendations) and not on the choices made by other users. However, exact mathematical model of the recommender will be left for the developers to create.
Defining the user groups: the KOSKI database, only has data available from 2018 on the upper secondary and vocational education. This would limit our user group for the piloting considerably to the users who only graduated upper secondary school 2018 and those who started vocational education 2018. However, us no real user data will be used, these limitations are not critical. Nevertheless, the results of evaluating recommender systems using historical datasets cannot be compared directly to studies with real users and vice versa as data accuracy of real user preferences is not captured (Jannach, et al. 2011). Thus, it would be important to gather some user feedback and information needed for further development of recommender system. To make user experience as close as possible to having own personal data in the services, mock-up data will be selected to correspond the most to the previously identified user groups (see the table of the user groups). Participants of the piloting will be selected accordingly to represent identified user groups. This will be done to ensure then the participants of the piloting are able to identify with presented data visualizations and education recommendations.
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