A systematic review of ontology use in E-Learning recommender system

Rahayu, Nur Wijayaning; Ferdiana, Ridi; Kusumawardani, Sri Suning

doi:10.1016/j.caeai.2022.100047

Cited by 84 publications

(39 citation statements)

References 55 publications

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“…There are various types of methods for recommendation. According to [22], recommendation methods can be classified into content-based [23], [24], collaborative filtering [25], [26], knowledge-based [27], and hybrid recommender systems that combine multiple methods [28], [29]. In the early stages of this field, the focus was on linking learning resources using termbased similarity [30], [31], which has since been replaced by modern text processing approaches, such as topic modelling and concept extraction [32], [33].…”

Section: Related Workmentioning

confidence: 99%

Adaptive Learning Support System Based on Automatic Recommendation of Personalized Review Materials

Okubo

Shiino

Minematsu

et al. 2023

IEEE Trans. Learning Technol.

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Section: Related Workmentioning

confidence: 99%

Adaptive Learning Support System Based on Automatic Recommendation of Personalized Review Materials

Okubo

Shiino

Minematsu

et al. 2023

IEEE Trans. Learning Technol.

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“…In addition, it resolves the cold-start problem (Jeevamol & Renumol, 2021). Numerous ontology-based RSs have been developed with the association of many different recommendation techniques (Rahayu et al, 2022). (Romero et al, 2019;Shishehchi et al, 2012) presented an ontology-based system to recommend suitable materials to learners.…”

Section: Ontology-based Rssmentioning

confidence: 99%

A comparative analysis of different recommender systems for university major and career domain guidance

et al. 2022

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Academic advising is inhibited at most of the high schools to help students identify appropriate academic pathways. The choice of a career domain is significantly influenced by the complexity of life and the volatility of the labor market. Thus, high school students feel confused during the shift period from high school to university, especially with the enormous amounts of data available on the Web. In this paper, an extensive comparative study is conducted to investigate five approaches of recommender systems for university study field and career domain guidance. A novel ontology is constructed to include all the needed information for this purpose. The developed approaches considered user-based and item-based collaborative filtering, demographic-based recommendation, knowledge base supported by case-based reasoning, ontology, as well as different hybridizations of them. A case study on Lebanese high school students is analyzed to evaluate the effectiveness and efficiency of the implemented approaches. The experimental results indicate that the knowledge-based hybrid recommender system, combined with the user-based collaborative filtering and braced with case-based reasoning as well as ontology, generated 98% of similar cases, 95% of them are personalized based on the interests of the high school students. The average usefulness feedback and satisfaction level of the students concerning this proposed hybrid approach reached 95% and 92.5% respectively, which could be a solution to similar problems, regardless of the application domain. Besides, the constructed ontology could be reused in other systems in the educational domain.

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“…The long short term memory (LSTM) architecture has its own advantages but it suffers from the input sequences length limitation. 20 To overcome this complexity, the GRU-RNN with DTT improves the accuracy of long-term text sequence prediction. The drawback is mainly overcome via the deep learning (DTT) technique adopted.…”

Section: Related Workmentioning

confidence: 99%

Personalized ontology and deep training tree‐based optimal gated recurrent unit‐recurrent neural network for prediction of students' behavior

Fernandez

Ramana²,

Shabana³

et al. 2022

Concurrency and Computation

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Summary Digital libraries are now widely used by students all around the world due to the hassle‐free services it offers in the form of digitized study materials, ebooks, multimedia content and so forth. However, to increase user satisfaction and allegiance, a personalized digital library needs to be deployed to offer customized services to the users. To accomplish this objective, this article presents a novel scheme to design personalized digital libraries via the usage of the Protégé editor and machine learning techniques. The Protégé 4.3 tool is used to generate the digital library ontologies and identify the interrelated concepts. Domain knowledge acquisition, ontology organization, ontology elaboration, ensuring the consistency of the information, and ontology validation. The gated recurrent unit‐recurrent neural network (GRU‐RNN) with a deep training tree (DTT) is used to predict the different user behavior styles such as cognitive behavior, learning speed behavior, sedentary behavior, and aggressive behavior. The DTT is mainly incorporated to overcome the vanishing gradient issue associated with the GRU‐RNN and used in the place of gradient‐based optimization. A black widow optimization approach is employed to update the weights of the GRU‐RNN network in order to increase its accuracy. The efficiency of the proposed methodology is analyzed by using different performance metrics such as F‐score, accuracy, loss, precision, and recall scores. The results show that the personalized digital library created can be efficient in terms of usability.

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A systematic review of ontology use in E-Learning recommender system

Cited by 84 publications

References 55 publications

Adaptive Learning Support System Based on Automatic Recommendation of Personalized Review Materials

Adaptive Learning Support System Based on Automatic Recommendation of Personalized Review Materials

A comparative analysis of different recommender systems for university major and career domain guidance

Personalized ontology and deep training tree‐based optimal gated recurrent unit‐recurrent neural network for prediction of students' behavior

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