Smart Learning Objects Retrieval for E-Learning with Contextual Recommendation based on Collaborative Filtering

Tahir, Sidra; Hafeez, Yaser; Abbas, Muhammad Azeem; Nawaz, Asif; Hamid, Bushra

doi:10.1007/s10639-022-10966-0

Cited by 27 publications

(8 citation statements)

References 48 publications

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“…Tahir et al . developed a contextual recommendation system based on collaborative filtering for the smart retrieval of LOs in e-learning (Tahir et al ., 2022). Moreover, Dagiene et al .…”

Section: Related Work For Learning Object Classificationmentioning

confidence: 99%

“…For instance, Falci et al employed a low-complexity heuristic approach to tackle a problem pertaining to the recommendation of LOs (Falci et al, 2020). Tahir et al developed a contextual recommendation system based on collaborative filtering for the smart retrieval of LOs in e-learning (Tahir et al, 2022). Moreover, Dagiene et al…”

Section: Related Work For Learning Object Classificationmentioning

confidence: 99%

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New perspective of learning objects in e-learning system

Amane

Aissaoui

Berrada

2023

IJILT

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PurposeTogether, learning objects (LOs) and e-pedagogical practices have the potential to improve the performance of e-learning systems in several ways. They can make e-learning more personalised and adaptable, providing students with a more engaging and effective learning experience.Design/methodology/approachThe development of LOs and e-pedagogical practices have significantly influenced and changed the performance of e-learning systems. LOs are self-contained, reusable units of instructional content that create instructional materials, such as online courses, tutorials and assessments. They provide a flexible and modular approach to designing and delivering e-learning content, allowing educators to easily customise and adapt their materials to the needs of their students. e-pedagogical practices refer to the use of technology to enhance and support the teaching and learning process. They include strategies such as online collaboration, gamification and adaptive learning to improve student engagement, motivation and achievement.FindingsTo achieve this objective, this study consists of two main phases. First, the authors extract metadata from LOs using latent semantic analysis algorithms, which are considered a strong tool in web-mining exploration techniques. Second, they identify LOs according to a particular form of similarity using fuzzy c-means (FCM) algorithms. To improve classification accuracy, the FCM is used as a clustering algorithm.Originality/valueFinally, in order to assess the effectiveness of LOs with FCM, a series of experimental studies using a real-world dataset are conducted. The results of this study indicate that the proposed approach exceeds the traditional approach and produces good results.

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“…Tahir et al . developed a contextual recommendation system based on collaborative filtering for the smart retrieval of LOs in e-learning (Tahir et al ., 2022). Moreover, Dagiene et al .…”

Section: Related Work For Learning Object Classificationmentioning

confidence: 99%

Section: Related Work For Learning Object Classificationmentioning

confidence: 99%

New perspective of learning objects in e-learning system

Amane

Aissaoui

Berrada

2023

IJILT

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“…In online learning, web-based searching plays a vital role in finding relevant important learning resources for students which are described using various computing and theoretical models. Some of the important steps are listed below: [ 31 , 32 ]: Personalized learning objectives are based on the importance of the learner's requirements. Utilization of search engines for finding required learning resources by searching with the help of different inputs like text, images, and video.…”

Section: Proposed Systemmentioning

confidence: 99%

Smart education system to improve the learning system with CBR based recommendation system using IoT

M.R.M,

Dabade,

et al. 2023

Heliyon

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“…A Variational Auto-Encoder (VAE) is a graphical model based on directed notes with a probabilistic approach, and its posterior is estimated by a neural network, resulting in an architecture similar to Auto-Encoder [16][17][18]. The encoder-decoder structure of the Variational Auto-Encoder allows for learning and training a mapping from highly complex and structured dimensional input and transforms to a latent representation having low dimensions.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Outlier Detection in IOT Using Deep VAE

Gouda

Tahir

Alanazi

et al. 2022

Sensors

Self Cite

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The Internet of Things (IoT) refers to a system of interconnected, internet-connected devices and sensors that allows the collection and dissemination of data. The data provided by these sensors may include outliers or exhibit anomalous behavior as a result of attack activities or device failure, for example. However, the majority of existing outlier detection algorithms rely on labeled data, which is frequently hard to obtain in the IoT domain. More crucially, the IoT’s data volume is continually increasing, necessitating the requirement for predicting and identifying the classes of future data. In this study, we propose an unsupervised technique based on a deep Variational Auto-Encoder (VAE) to detect outliers in IoT data by leveraging the characteristic of the reconstruction ability and the low-dimensional representation of the input data’s latent variables of the VAE. First, the input data are standardized. Then, we employ the VAE to find a reconstructed output representation from the low-dimensional representation of the latent variables of the input data. Finally, the reconstruction error between the original observation and the reconstructed one is used as an outlier score. Our model was trained only using normal data with no labels in an unsupervised manner and evaluated using Statlog (Landsat Satellite) dataset. The unsupervised model achieved promising and comparable results with the state-of-the-art outlier detection schemes with a precision of ≈90% and an F1 score of 79%.

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Smart Learning Objects Retrieval for E-Learning with Contextual Recommendation based on Collaborative Filtering

Cited by 27 publications

References 48 publications

New perspective of learning objects in e-learning system

New perspective of learning objects in e-learning system

Smart education system to improve the learning system with CBR based recommendation system using IoT

Unsupervised Outlier Detection in IOT Using Deep VAE

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