Semantically enhanced decision support for learning management systems

Gavriushenko, Mariia; Kankaanranta, Marja; Neittaanmäki, Pekka

doi:10.1109/icosc.2015.7050823

Cited by 4 publications

(3 citation statements)

References 26 publications

(26 reference statements)

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“…Ontologies are used for formal specification of concepts and their relations, which characterize a specific knowledge domain. For this system's model proposal, the advantage of using this approach of knowledge representation is a formal structure, which greatly simplifies the processing [41], [42], [29].…”

Section: System's Model Proposal Methods and Architecturementioning

confidence: 99%

Smart Educational Process Based on Personal Learning Capabilities

Gavriushenko¹,

Lindberg²,

Khriyenko³

2017

EDULEARN Proceedings

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Personalized learning is increasingly gaining popularity, especially with the development of information technology and modern educational resources for learning. Each person is individual and has different knowledge background, different kind of memory, different learning speed. Teacher can adapt learning course, learning instructions or learning material according to the majority of learners in class, but that means that learning process is not adapted to the personality of each individual learner. That is why it is important to have smart educational process based on personal learning capabilities. This paper presents a literature survey on different learning systems which detects learning progress and based on that a model of smart educational system which use knowledge engineering and Watson technology is proposed. This system is relevant both for basic education and for adult education.

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Section: System's Model Proposal Methods and Architecturementioning

confidence: 99%

Smart Educational Process Based on Personal Learning Capabilities

Gavriushenko¹,

Lindberg²,

Khriyenko³

2017

EDULEARN Proceedings

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“…The connection and joining between specialists' innovation and information mining and machine taking in originate from the characteristic difficulties, needs, and opportunities faced by the constituent advancements both individually and commonly. New difficulties are showing up with the rise of new processing systems [27], for example, conduct figuring, distributed computing, and social registering [5]. Specialists' mining realizes multifold preferences to multioperators frameworks, information mining, and machine learning [24] and additionally new inferred speculations, instruments, and applications that passed any individual innovation [4].…”

Section: Agent-based Developmentmentioning

confidence: 99%

Multiagent Semantical Annotation Enhancement Model for IoT-Based Energy-Aware Data

Malik

Ahmad

Farhan

et al. 2016

International Journal of Distributed Sensor Networks

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The Internet of Things (IoT) is involved in dealing with physical items, gadgets, vehicles, structures, and different things that are inserted into hardware, programming, sensors, and system availability, which empowers these items to gather and trade information. Improving extraction of sensor-based data for energy awareness and then annotating it and converting it into semantically enabled form for analyzing results with the use of improved tools and applications are the focus of this research. However, as the amount of real time data gets huge, it becomes difficult to track results when needed at once. Reconciliation of heterogeneous information sources into an interlinked data is a standout among the most pertinent difficulties for some learning based systems these days. This paper forms suitable elements by a methodology for adjustment of heterogeneous sensor-based Web assets, where different tools and applications like weather detection for self-observing and self-diagnostics use dispersed human specialists and learning. The proposed general model uses a capability of the Semantic Web innovation and concentrates on the part of a semantic adjustment of existing broadly utilized models of information representation to Resource Description Framework (RDF) based semantically rich arrangement. This work is valuable for sorting out and inquiry of the detecting information in the Internet of Things.

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“…The system should adopt benefits of Semantic Web and linked data technologies, as well as, be facilitated by text mining and natural language processing techniques. There has been many studies proposed for the intelligent study advising utilizing Semantic Web technologies (Henderson and Goodridge, 2015;Gavriushenko et al, 2015;Ranganathan and Brown, 2007;Nguyen et al, 2008;Jovanović et al, 2007). Authors in (Dicheva et al, 2009;Jovanovic et al, 2007) used ontologies and Semantic Web in curriculum development and in institutional support and adaptation.…”

Section: Using Semantic Web In Building Ilssmentioning

confidence: 99%

An Intelligent Learning Support System

Gavriushenko

Khriyenko

Tuhkala

2017

Proceedings of the 9th International Conference on Computer Supported Education

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Abstract:Fast-growing technologies are shaping many aspects of societies. Educational systems, in general, are still rather traditional: learner applies for school or university, chooses the subject, takes the courses, and finally graduates. The problem is that labor markets are constantly changing and the needed professional skills might not match with the curriculum of the educational program. It might be that it is not even possible to learn a combination of desired skills within one educational organization. For example, there are only a few universities that can provide high-quality teaching in several different areas. Therefore, learners may have to study specific modules and units somewhere else, for example, in massive open online courses. A person, who is learning some particular content from outside of the university, could have some knowledge gaps which should be recognized. We argue that it is possible to respond to these challenges with adaptive, intelligent, and personalized learning systems that utilize data analytics, machine learning, and Semantic Web technologies.In this paper, we propose a model for an Intelligent Learning Support System that guides learner during the whole lifecycle using semantic annotation methodology. Semantic annotation of learning materials is done not only on the course level but also at the content level to perform semantic reasoning about the possible learning gaps. Based on this reasoning, the system can recommend extensive learning material.

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Semantically enhanced decision support for learning management systems

Cited by 4 publications

References 26 publications

Smart Educational Process Based on Personal Learning Capabilities

Smart Educational Process Based on Personal Learning Capabilities

Multiagent Semantical Annotation Enhancement Model for IoT-Based Energy-Aware Data

An Intelligent Learning Support System

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