EduCOR: An Educational and Career-Oriented Recommendation Ontology

Ilkou, Eleni; Abu-Rasheed, Hasan; Tavakoli, Mohammadreza; Hakimov, Sherzod; Kismihók, Gábor; Auer, Sören; Nejdl, Wolfgang

doi:10.1007/978-3-030-88361-4_32

Cited by 16 publications

(10 citation statements)

References 34 publications

(48 reference statements)

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“…The current line of research is still in an early stage. In our first steps in user modelling in e-learning platforms, we have published EduCOR ontology [11] for personalised recommendations of educational resources. EduCOR consists of different parts and has a focus on user profiling, as can be seen in Figure 2.…”

Section: Resultsmentioning

confidence: 99%

“…WASPEC [3] utilises a semantic framework that models learner profiles and categorises personalisation parameters to learning preferences and accessibility. Our latest publication, EduCOR ontology [11], contributes in that line as it is analysed in Section 6. Another interesting approach is the formulation of the learning session graph to achieve personalisation in informal learning settings in information wikis [15].…”

Section: Personalisation In E-learningmentioning

confidence: 99%

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Personal Knowledge Graphs: Use Cases in e-learning Platforms

Ilkou¹

2022

Preprint

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Personal Knowledge Graphs (PKGs) are introduced by the semantic web community as small-sized user-centric knowledge graphs (KGs). PKGs fill the gap of personalised representation of user data and interests on the top of big, well-established encyclopedic KGs, such as DBpedia. Inspired by the widely recent usage of PKGs in the medical domain to represent patient data, this PhD proposal aims to adopt a similar technique in the educational domain in e-learning platforms by deploying PKGs to represent users and learners. We propose a novel PKG development that relies on ontology and interlinks to Linked Open Data. Hence, adding the dimension of personalisation and explainability in users' featured data while respecting privacy. This research design is developed in two use cases: a collaborative search learning platform and an e-learning platform. Our preliminary results show that e-learning platforms can get benefited from our approach by providing personalised recommendations and more user and group-specific data.

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Section: Resultsmentioning

confidence: 99%

Section: Personalisation In E-learningmentioning

confidence: 99%

Personal Knowledge Graphs: Use Cases in e-learning Platforms

Ilkou¹

2022

Preprint

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“…Educational recommender systems require a proper framework that models learning processes and from which sound recommendations for learning activities and materials can be derived. A categorization of workplace learning goals can help to build such a framework by describing the exact objectives and reasons why learners engage in workplace learning and by enabling transparency and interoperability of recommender systems (Ilkou et al, 2021;Reichow et al, 2022).…”

Section: Educational Recommender Systemsmentioning

confidence: 99%

A Categorization of Workplace Learning Goals for Multi-Stakeholder Recommender Systems: A Systematic Review

Hemmler

Rasch²

2022

TechTrends

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Educational recommender systems offer benefits for workplace learning by tailoring the selection of learning activities to the individual’s learning goals. However, existing systems focus on the learner as the primary stakeholder of learning processes and do not consider the organization’s perspective. We conducted a systematic review to develop a categorization of workplace learning goals for multi-stakeholder recommender systems. Out of an initial set of 13,198 publications exported from databases, a final sample of 34 key publications was identified, according to predefined inclusion criteria. Content analysis and reflective exchange were deployed to synthesize workplace learning goals investigated in the key publications. We identified five categories of workplace learning goals that can be arranged along a dimension from intrinsic (goals set exclusively by the learner) to external (goals set exclusively by the organization). Our categorization provides a common language for multi-stakeholder recommender systems incorporating both the learner’s and the organization’s perspectives.

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“…Based on GeoNames and Wikidata, Reference [9] transformed and integrated geographic data in various formats from different sources to create CKGG, an ontology for geography at the high school level. Reference [10] introduced the EduCOR ontology, an educational, career-oriented ontology that provided the basis for online learning resources representing personalized learning systems. Since the domain ontology constructed by these scholars are relatively large in number of entities and relationships, their work requires multi-person collaboration, using a variety of complex technologies and a large number of tools, which makes these methods inefficient in terms of time.…”

Section: Related Researchmentioning

confidence: 99%

Research on the key technology of course ontology construction

Chen

Chen²

2022

J. Phys.: Conf. Ser.

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Education Information and smart education is the future trend of education. Education ontology is an important tool and means to realize education information and smart education. In the traditional method, the ontology is basically constructed manually, which will consume huge labor and time costs, and the automatically constructed ontology is often of low quality. The previous semi-automatic construction method is also aimed at the ontology of the subject area. Due to the wide range of subject areas and many concepts, the direct construction requires the cooperation of many people and takes several months to complete. Therefore, starting from the construction of curriculum ontology, this paper puts forward an efficient, accurate and low-cost method of constructing course ontology. This method takes “China University MOOC”, “XuetangX” and “cnblog” as data sources, making full use of structured data, semi-structured data and unstructured data to integrate technologies such as web crawler technology, text matching, active learning and deep learning. Applying these methods to the course “Data Structure”, the experimental results show that the scale coefficient of the number of concepts in the course ontology constructed by this method in the standard set is as high as 0.92, and the attribute coverage reaches 1.21, which proves the effectiveness and accuracy of the method.

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EduCOR: An Educational and Career-Oriented Recommendation Ontology

Cited by 16 publications

References 34 publications

Personal Knowledge Graphs: Use Cases in e-learning Platforms

Personal Knowledge Graphs: Use Cases in e-learning Platforms

A Categorization of Workplace Learning Goals for Multi-Stakeholder Recommender Systems: A Systematic Review

Research on the key technology of course ontology construction

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