Research on Personalized E-Learning System Using Fuzzy Set Based Clustering Algorithm

Lu, F.; Li, Xin; Liu, Qingtang; Yang, Zhen; Tan, Guoxin; He, Tingting

doi:10.1007/978-3-540-72588-6_99

Cited by 14 publications

(9 citation statements)

References 3 publications

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“…Rule-based approaches are found to be more appropriate since the other traditional techniques do not address the issue of uncertainty very well. The model proposed in this paper makes use of Fuzzy membership functions which helps in identifying the degree of activeness or reflectiveness and also categorizes the learners in the unknown category of the existing models into active, medium active, medium reflective, and reflective types of learners (Lu et al 2007).…”

Section: Analysis Of Related Workmentioning

confidence: 99%

Fuzzy-logic based learning style prediction in e-learning using web interface information

Deborah¹,

Sathiyaseelan

Audithan

et al. 2015

Sadhana

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The e-learners' excellence can be improved by recommending suitable e-contents available in e-learning servers that are based on investigating their learning styles. The learning styles had to be predicted carefully, because the psychological balance is variable in nature and the e-learners are diversified based on the learning patterns, environment, time and their mood. Moreover, the knowledge about the learners used for learning style prediction is uncertain in nature. This paper identifies Felder-Silverman learning style model as a suitable model for learning style prediction, especially in web environments and proposes to use Fuzzy rules to handle the uncertainty in the learning style predictions. The evaluations have used the Gaussian membership function based fuzzy logic for 120 students and tested for learning of C programming language and it has been observed that the proposed model improved the accuracy in prediction significantly.

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Section: Analysis Of Related Workmentioning

confidence: 99%

Fuzzy-logic based learning style prediction in e-learning using web interface information

Deborah¹,

Sathiyaseelan

Audithan

et al. 2015

Sadhana

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“…The difficulty parameters slowly approach a steady value as the number of learners increases. Another approach to obtain item parameter estimates is allowing subject domain experts to estimate the value of the difficulty parameter (Yao 1991;Linacre 2000;Fernandez 2003;Lu et al 2007). There is some evidence in the measurement literature that test specialists are capable of estimating item difficulties with reasonable accuracy (e.g.…”

Section: Item Difficulty Estimationmentioning

confidence: 99%

Adaptive item‐based learning environments based on the item response theory: possibilities and challenges

Wauters

Desmet

Noortgate

2010

Computer Assisted Learning

100

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The popularity of intelligent tutoring systems (ITSs) is increasing rapidly. In order to make learning environments more efficient, researchers have been exploring the possibility of an automatic adaptation of the learning environment to the learner or the context. One of the possible adaptation techniques is adaptive item sequencing by matching the difficulty of the items to the learner's knowledge level. This is already accomplished to a certain extent in adaptive testing environments, where the test is tailored to the person's ability level by means of the item response theory (IRT). Even though IRT has been a prevalent computerized adaptive test (CAT) approach for decades and applying IRT in item-based ITSs could lead to similar advantages as in CAT (e.g. higher motivation and more efficient learning), research on the application of IRT in such learning environments is highly restricted or absent. The purpose of this paper was to explore the feasibility of applying IRT in adaptive item-based ITSs. Therefore, we discussed the two main challenges associated with IRT application in such learning environments: the challenge of the data set and the challenge of the algorithm. We concluded that applying IRT seems to be a viable solution for adaptive item selection in item-based ITSs provided that some modifications are implemented. Further research should shed more light on the adequacy of the proposed solutions.

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“…In most of ALS, supervised learning has been employed for classification of students [5], [6] and prediction of student performance [7], [8], [9]. Whilst clustering in ALS can be divided into clustering of students [10] or clustering of learning materials [11]. To date, there have very few works reported in combining both classification and clustering techniques especially in learning material selection of ALS.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Learning Model Based On Significant Weight Of Domain Knowledge Concept For Adaptive E-Learning

Idris

Hashim

Samsudin

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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In order to support personalized learning, an adaptive learning system should have a capability to provide each student with a suitable learning material regarding his profile. However, the issue of student varieties in acquiring every Domain Knowledge Concept (DKC), and a range of DKC important variations in a particular learning material produced a complex dependency that causes a difficulty in the learning material selection process. Existing rule-based learning material selection approach requires the definition of a huge set adaptation rules. However, this approach usually results in inaccurate and incorrect selection due to the inconsistent, insufficient and confluence of the defined rules. Consequently, the process of learning material selection is hard to be algorithmized, therefore, intelligent methods are applied to handle the complexity challenges. This research proposes a significance weight approach that represents the complex dependency of learning material selection problem to substitute the rules definition in the selection process. In addition, this research proposes an intelligent learning model that combines unsupervised and supervised machine learning techniques to accurately select the learning material for a particular student adaptively. The unsupervised machine learning technique is vital in obtaining a learning material classification and labelling based on the proposed significance weight. Meanwhile, the supervised machine learning technique, the Multilayer Perceptron Artificial Neural Networks conducts the adaptation process that will assign the student to suitable learning materials regarding his performance upon specific DKC. With 98% achievement of classification accuracies, this model can be considered as highly accurate in selecting a correct and suitable learning material based on student's domain knowledge level.

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Research on Personalized E-Learning System Using Fuzzy Set Based Clustering Algorithm

Cited by 14 publications

References 3 publications

Fuzzy-logic based learning style prediction in e-learning using web interface information

Fuzzy-logic based learning style prediction in e-learning using web interface information

Adaptive item‐based learning environments based on the item response theory: possibilities and challenges

Intelligent Learning Model Based On Significant Weight Of Domain Knowledge Concept For Adaptive E-Learning

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