Applying dynamic fuzzy petri net to web learning system

Chen, Juei Nan; Huang, Yueh Min; Chu, William C.

doi:10.1080/10494820500382810

Cited by 42 publications

(30 citation statements)

References 11 publications

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“…Adaptive learning has gained attention in recent years, whereby several approaches have appeared, such as adaptive pedagogical paths [23], dynamic learning recommendations [6] and adaptive learning [25].…”

Section: Introductionmentioning

confidence: 99%

Designing adaptive learning itineraries using features modelling and swarm intelligence

Vazquez¹,

Ramírez

Abril

et al. 2011

Neural Comput & Applic

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In this paper, Bayesian network (BN) and ant colony optimization (ACO) techniques are combined in order to find the best path through a graph representing all available itineraries to acquire a professional competence. The combination of these methods allows us to design a dynamic learning path, useful in a rapidly changing world. One of the most important advances in this work, apart from the variable amount of pheromones, is the automatic processing of the learning graph. This processing is carried out by the learning management system and helps towards understanding the learning process as a competence-oriented itinerary instead of a stand-alone course. The amount of pheromones is calculated by taking into account the results acquired in the last completed course in relation to the minimum score required and by feeding this into the learning tree in order to obtain a relative impact on the path taken by the student. A BN is used to predict the probability of success, by taking historical data and student profiles into account. Usually, these profiles are defined beforehand; however, in our approach, some characteristics of these profiles, such as the level of knowledge, are classified automatically through supervised and/or unsupervised learning. By using ACO and BN, a fitness function, responsible for automatically selecting the next course in the learning graph, is defined. This is done by generating a path which maximizes the probability of each user's success on the course. Therefore, the path can change in order to adapt itself to learners' preferences and needs, by taking into account the pedagogical weight of each learning unit and the social behaviour of the system.

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Section: Introductionmentioning

confidence: 99%

Designing adaptive learning itineraries using features modelling and swarm intelligence

Vazquez¹,

Ramírez

Abril

et al. 2011

Neural Comput & Applic

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“…In Chen et al (2005) [43] work, authors propose a Dynamic Fuzzy Petri Net inference engine that monitors "browsing time" and "browsing count" of users' interaction with their system. According to them, whenever the learner spends too much time on a specific section, he/she is very interested in it or confused by it.…”

Section: Learning Behavior -Motivationmentioning

confidence: 99%

“…Chen et al (2006) [43] proposed a system based on modified Item Response Theory which provides learning paths that can be adapted to various levels of difficulty of course materials and various abilities of learners. Meanwhile, the concept continuity of learning pathways is also integrated by analyzing concept relation degrees for all database courseware while applying personalized curriculum sequencing.…”

Section: Knowledge Levelmentioning

confidence: 99%

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Recent Contributions to a Generic Architecture Design that Supports Learning Objects Interoperability

Botsios

Georgiou

2009

International Journal of Distance Education Technologies

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“…As yet, however, no combined effort has been made to extract the common design paradigm from these systems. In [6] dynamic fuzzy Petri nets (DFPN's) model is introduced to increase the flexibility of the tutoring a gent behavior ( software assistant for a single user, who may be an expert in e-learning course). A compete standardized course generation using DFPN's usually needs some auxiliary materials which are determined by the students' behavior are introduced in [7].…”

Section: Introductionmentioning

confidence: 99%

Modeling Intelligent ELearning Systems based on Adaptive Fuzzy Higher Order Petri Nets

Amin¹,

El-Ramly²,

Shebl³

2011

IJCA

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In this paper, we introduce adaptive and intelligent technologies in E-learning course development using Modular Object Oriented Dynamic Learning Environment (Moodle) based on Petri nets as a modeling formalism. Since classical Petri nets and fuzzy Petri nets are not adaptable according to the changes of the new incoming data such as the parameters of Moodle (static course material, interactive course material, activities), we introduce adaptive fuzzy higher order Petri net (AFHOPN) that is dynamically adjust the parameters. AFHOPN helps to describe and analyze the dynamic behavior, production inference of the intelligent E-learning systems and measure the learning rate.

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Applying dynamic fuzzy petri net to web learning system

Cited by 42 publications

References 11 publications

Designing adaptive learning itineraries using features modelling and swarm intelligence

Designing adaptive learning itineraries using features modelling and swarm intelligence

Recent Contributions to a Generic Architecture Design that Supports Learning Objects Interoperability

Modeling Intelligent ELearning Systems based on Adaptive Fuzzy Higher Order Petri Nets

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