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APPLYING A PERSONALIZED MODEL IN PHD STUDENT LEARNING

Petya Petrova

First published: 2020-09-20https://doi.org/10.5593/sgem2020/2.1/s07.006View metrics

Abstract

With each passing day consumers, including students and PhD students, expect more intelligent and personalized services. The key to providing such services is the concept of a personalized model. The purpose of the current research is to present adequate methods for creating a personalized model in student and PhD students learning. User profile modeling is a challenge because of the high degree of subjectivity and human behavior indetermination. Traditional methods used to create user models are usually not agile enough to capture the inherent uncertainty. Due to the variety and amount of information available to create custom models, machine learning techniques and Data Mining can be used to automatically identify user profiles and interests. However, traditional techniques are not able to capture the inherent uncertainty in modeling human behavior. In this context, soft computing methods play the role of an adequate tool for generating effective personalized models in the education of undergraduate and in particular PhD students. The soft computing methods - fuzzy logic, neural networks, genetic algorithms, fuzzy clustering and neuro-fuzzy systems ? standalone or in combination with other machine learning techniques, could be used for this purpose, due to their appropriate specificity. In this paper the author presents the research results on the suitability of general soft computing methods for creating a personalized model in students and PhD students learning.

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Publication details

Title
APPLYING A PERSONALIZED MODEL IN PHD STUDENT LEARNING
Authors
Petya Petrova
Proceedings
SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings; 20th International Multidisciplinary Scientific GeoConference Proceedings SGEM 2020, Informatics, Geoinformatics and Remote Sensing
Publisher
STEF92 Technology
Year
2020
Pages
41-50
SWS Citekey
Petrova202074150
ISSN
1314-2704
ISBN
978-619-7603-06-4
Language
en
Publication type
Conference Paper
Keywords
Personalized ModelStudent profile modelPhD Student LearningSoftComputing
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