Personalized Students’ Profile Based On Ontology and Rule-based Reasoning

Nafea, Shaimaa M.; Μαγλαράς, Λέανδρος; Siewe, François; Smith, Richard N.; Janicke, Helge

doi:10.4108/eai.2-12-2016.151720

Cited by 14 publications

(7 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…e design of an adaptive e-learning environment should take into account the preferred learning style so as to personalize the profile of each learner based on ontology. e learning styles include being active or reflective, sensing or intuitive, visual or verbal, and sequential or global [14].…”

Section: Literature Reviewmentioning

confidence: 99%

Examining the Effectiveness of Using Adaptive AI-Enabled e-Learning during the Pandemic of COVID-19

Younes

2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

This study aimed to identify the effect of using adaptive AI-enabled e-learning on developing digital content creative design skills among postgraduate students. The research tools included an achievement test and an observation checklist for rating the practical performance. Research results concluded that, regardless of learning styles, the proposed adaptive e-learning environment had a positive effect on developing both cognitive achievement and practical performance of digital content creative design skills. The results also indicated that there is a significant difference at the 0.01 level between the mean scores of the first experimental group’s students using the global learning style-based adaptive e-learning environment and the second experimental group’s students using the sequential learning style-based adaptive AI-enabled e-learning environment in the achievement test and observation checklist after measurement of digital content creative design skills in favor of the second experimental group’s students. The study provided a number of suggestions and recommendations for making the utmost use of various design layouts of adaptive AI-enabled e-learning environments in developing different cognitive and performance aspects of learning as well as taking full advantage of digital content creative design skills mastery in producing a plethora of advanced electronic educational applications in the foreseeable future.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Examining the Effectiveness of Using Adaptive AI-Enabled e-Learning during the Pandemic of COVID-19

Younes

2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…This ontology aims to support the instructor to semantically search for suitable contents during the learning content authoring process in order to reuse this content. Additionally, the authors in [34], proposed a an ontological approach in order to create an personalized student profile based on ontology and rule-based reasoning by analysis of learning patterns through a learning management system, according to the Felder-Silverman learning style model (FSLSM) and Myers-Briggs Type Indicator (MBTI) theory. Furthermore, Rezgui [35], introduces a learner profile represented by an ontological structure for learning networks.…”

Section: Ontological Technology For Learner Modelingmentioning

confidence: 99%

New Approach for Context-dependent Learner Model for Technology Enhanced Learning System

Akharraz¹

2019

IJATCSE

View full text Add to dashboard Cite

Learner modeling still the object of very active research in the technology-enhanced learning system. It makes it possible to represent a complete description of the learner. In this paper, we have proposed a new learner modeling approach which is independent on the learning system and dependent on the learning context. The proposed model involved four information categories: Personal data describing the general information about the learner; Cognitive data representing the learner performances and knowledge; Activity data providing details about learner activities during the learning process. Finally, contextual data presenting a description about learner context such as location, device, accessibility, connectivity, etc. To ensure a better representation of these characteristics, we have proposed an ontology-based learner model in order to benefit of advantages gained from the use of ontological technology, like extensibility, usability, exchanging information, inferring new knowledge by reasoning on an existing one.

show abstract

“…This LMS relies on several ontologies and adaptation rules for knowledge representation, and inference engines for reasoning. Finally, an adaptation model based on the Felder–Silverman model of learning styles implemented in Moodle platform was proposed by Nafea et al , where adaptive learning was also supported by ontologies and inference rules. In this work, personalized student guidance was achieved by collecting initial students' competencies and preferences, and using both semantic rules and reasoning to detect changes in the behavior of students during the learning process.…”

Section: Related Workmentioning

confidence: 99%

A semantic approach to enhance moodle with personalization

Perišić

Milovanović

Kazi

2018

Comp Applic In Engineering

View full text Add to dashboard Cite

The purpose of this paper is to present a framework for an adaptive mechanism implemented in Moodle in order to improve learning outcomes and students' satisfaction with the learning process. The proposed mechanism adapts the learning content within the course according to students' characteristics expressed by their learning style. In our study, student's learning style is dynamically determined by monitoring students’ actions and activities during the learning process, and detecting patterns of behavior that correspond to specific learning style. Semantic web technologies are in the background of the entire adaptive system. In order to examine the effectiveness of the proposed model and students' feedback, an evaluation study was conducted on two groups of students. Students from the control group had access to standard Moodle course, while students from experimental group had access to personalized learning content. The results indicated that students' performance was improved by using the proposed framework, while the student's feedback from regarding its usefulness was positive.

show abstract

Personalized Students’ Profile Based On Ontology and Rule-based Reasoning

Cited by 14 publications

References 55 publications

Examining the Effectiveness of Using Adaptive AI-Enabled e-Learning during the Pandemic of COVID-19

Examining the Effectiveness of Using Adaptive AI-Enabled e-Learning during the Pandemic of COVID-19

New Approach for Context-dependent Learner Model for Technology Enhanced Learning System

A semantic approach to enhance moodle with personalization

Contact Info

Product

Resources

About