Evaluating the integration of fuzzy logic into the student model of a web-based learning environment

Chrysafiadi, Konstantina; Virvou, Maria

doi:10.1016/j.eswa.2012.05.089

Cited by 50 publications

(32 citation statements)

References 33 publications

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“…Chrysafiadi and Virvou [12] have proposed a learner model that represents the learner's knowledge through the overlay model (presented concepts that the learner master with "1" or with the word "known" and those that do not master by "0" or unknown), the fuzzy logic allowed to define and update the level of knowledge of each concept, with each interaction with the e-learning system. Martin and VanLehn [13] have presented OLAE as an assessment tool that collects data from students solving physics problems in college.…”

Section: Genetic Algorithm On Adaptive E-learningmentioning

confidence: 99%

Adaptive e-learning using Genetic Algorithm and Sentiments Analysis in a Big Data System

Madani¹,

Bengourram²,

Erritali³

et al. 2017

ijacsa

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Abstract-In this article we describe our adaptive e-learning system, which allows the learner to take courses adapted to his profile and to the pedagogical objectives set by the teacher, we use for adaptation the genetic algorithms to give the learner the concepts that must learn in an optimal way by seeking the objectives most adapted to his profile. And after a second level of adaptation using one of the social networks of the learner (twitter, facebook, Google + ...), based on his post on one of these social networks we propose two levels of analysis. The first one is to look for the period of activity which gives us an idea about the period when the learner is active and the second consists of making an analysis of the feelings on the publications that are published during the period of activity and related to education. Our work therefore is to adapt the profile of the learner with the pedagogical objectives by using the genetic algorithm and the notions of the research of information by doing this work in a Big Data system, that is to say we parallelize the search problem using Hadoop with Hadoop distributed file system (HDFS) and the MapReduce programming model,and after using information from a social network of the learner, we look for the period of activity of the learner and the feeling (sentiment analysis) related to the publications of the period of activity.

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Section: Genetic Algorithm On Adaptive E-learningmentioning

confidence: 99%

Adaptive e-learning using Genetic Algorithm and Sentiments Analysis in a Big Data System

Madani¹,

Bengourram²,

Erritali³

et al. 2017

ijacsa

View full text Add to dashboard Cite

show abstract

“…The operation of these subsystems is imperceptible by the learners, as StuDiAsE provides personalized educational material and support based on the profile and performance of the learner. The profiling, modelling and evaluation of the learners is being performed by the use of artificial intelligence and, specifically, fuzzy logic [32][33][34]. Using artificial intelligence and exploiting the data logged during the educational process, StuDiAsE is capable of deriving personalized learner profiles.…”

Section: Paper An Advanced Elearning Environment Developed For Enginementioning

confidence: 99%

An Advanced eLearning Environment Developed for Engineering Learners

Σαμαράκου¹,

Fylladitakis²,

Früh³

et al. 2015

Int. J. Emerg. Technol. Learn.

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Abstract-Monitoring and evaluating engineering learners through computer-based laboratory exercises is a difficult task, especially under classroom conditions. A complete diagnosis requires the capability to assess both the competence of the learner to use the scientific software and the understanding of the theoretical principles. This monitoring and evaluation needs to be continuous, unobtrusive and personalized in order to be effective. This study presents the results of the pilot application of an eLearning environment developed specifically with engineering learners in mind. As its name suggests, the Learner Diagnosis, Assistance, and Evaluation System based on Artificial Intelligence (StuDiAsE) is an Open Learning Environment that can perform unattended diagnostic, evaluation and feedback tasks based on both quantitative and qualitative parameters. The base architecture of the system, the user interface and its effect on the performance of postgraduate engineering learners are being presented.

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“…However, these methods are quite limited in term of handling uncertain and imprecise data. Under the paradigm of fuzzy logic, approaches have been proposed such as learner's profile modeling( [14], [15]), evaluation issue ( [16], [17], [18]), learning styles prediction ( [19], [20]), which cover different sides of e-learning systems.…”

Section: Related Workmentioning

confidence: 99%

Personalization of learning situations within a virtual environment for training based on fuzzy clustering

Mohamed¹,

Jakimi²,

Bermi³

2017

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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Advances in networks, computers and multimedia technology have changed traditional methods for learning and skills training. Nowadays, Virtual Environments for Training (VET) have been popular, they can provide an environment where virtual reality can be used to create interactive interfaces and real-time software that can control every response and action made by the user. VET have proven to be advantageous to put learners into varied training situations to acquire knowledge and competencies, especially when these situations are taking place in uncontrolled circumstances, or when they are dangerous, unrealizable, or expensive to establish in reality. However individual learners find it difficult to select suitable learning situations for their particular situation because often, there is no personalized service to response to the user needs. Personalization of learning in a VET is a very important way of improving the effectiveness and the quality of the training, yet it also a complex process that requires consideration of several factors such as learners profiles. The goal is to associate suitable learning situations to each learner based on his profile. However, personalization of learning becomes an issue with the uncertainty and imprecision of data that may contains a learner profile. To address this issue, this study is an attempt to integrate a fuzzy clustering into the process of the personalization of learning.

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Evaluating the integration of fuzzy logic into the student model of a web-based learning environment

Cited by 50 publications

References 33 publications

Adaptive e-learning using Genetic Algorithm and Sentiments Analysis in a Big Data System

Adaptive e-learning using Genetic Algorithm and Sentiments Analysis in a Big Data System

An Advanced eLearning Environment Developed for Engineering Learners

Personalization of learning situations within a virtual environment for training based on fuzzy clustering

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