Disclaimer: This is a fictional paper written in a fashion that describes the present as 2043. In this paper, we introduce a Semantic Web-based educational application for K-12 Automatic User Modelling, which attains a 92% accuracy rate with a focus on enhancing personalised learning experiences. The field of automatic user profiling has received considerable attention in recent years, intending to enable personalised content delivery and individualised user experience. In this paper, we present a novel machine learning-based approach for automatic user profiling in the K-12 education domain, which achieves a high accuracy compared to the baselines. Our approach involves the use of a combination of 10 out of 12 learning dimensions over the EduMultiKG tested in 152 students over the course of the academic year 2041-2042.

EduMultiKG attains 92% accuracy in K-12 user profiling! / Ilkou, Eleni; Galletti, Martina; Dobriy, Daniil. - (2023). (Intervento presentato al convegno ESWC 2043 - The next 20 years track, ESWC 2023 tenutosi a Hersonissos, Greece).

EduMultiKG attains 92% accuracy in K-12 user profiling!

Galletti, Martina;
2023

Abstract

Disclaimer: This is a fictional paper written in a fashion that describes the present as 2043. In this paper, we introduce a Semantic Web-based educational application for K-12 Automatic User Modelling, which attains a 92% accuracy rate with a focus on enhancing personalised learning experiences. The field of automatic user profiling has received considerable attention in recent years, intending to enable personalised content delivery and individualised user experience. In this paper, we present a novel machine learning-based approach for automatic user profiling in the K-12 education domain, which achieves a high accuracy compared to the baselines. Our approach involves the use of a combination of 10 out of 12 learning dimensions over the EduMultiKG tested in 152 students over the course of the academic year 2041-2042.
2023
ESWC 2043 - The next 20 years track, ESWC 2023
Intelligent User Modelling, Hybrid AI, Education, Wisdom Web, Speech and Language Therapy
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
EduMultiKG attains 92% accuracy in K-12 user profiling! / Ilkou, Eleni; Galletti, Martina; Dobriy, Daniil. - (2023). (Intervento presentato al convegno ESWC 2043 - The next 20 years track, ESWC 2023 tenutosi a Hersonissos, Greece).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1717831
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