Intelligent query optimization and course recommendation during online lectures in E-learning system

Rafiq, Muhammad Sajid; Jian-she, Xie; Arif, Muhammad; Barra, Paola

doi:10.1007/s12652-020-02834-x

Cited by 25 publications

(21 citation statements)

References 48 publications

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“…As for the mapping function f 1 (ats, dts) in expression (4), since user's times of accessing resources ats and times of downloading resources dts cannot objectively demonstrate user's interest in the resources, the average times of accessing and downloading resources need to be quoted to evaluate the current user's interest degree. Therefore, the function f 1 (ats, dts) can be replaced with expression (5). Please see the specific expression as follows:…”

Section: User Interest and Preferencementioning

confidence: 99%

“…In expression (5), ats represents the times of accessing webpages or learning resources; dts represents the times of downloading resources; ats 0 represents the average times of accessing webpages or learning resources; dts 0 represents the average times of downloading resources; α and β represents weighing parameters. The modes of calculating ats 0 and dts 0 are listed, respectively, as follows:…”

Section: User Interest and Preferencementioning

confidence: 99%

“…Alghofaily and Ding [ 4 ] took into consideration the dataset features in the QoS-based service recommendation process, and then used a meta-learning algorithm to incorporate dataset features in the recommendation process and study the use of different machine learning algorithms. Rafiq et al [ 5 ] introduced the possibility of disaggregating query/question information in E-learning system online lectures or course recommendations, and then proposed a method for improving the classification of action verbs to a more accurate level in E-learning system. Sridharan et al [ 6 ] invented an adaptive learning management system, which can create a customized course for every student based on their level of knowledge, preferred mode of learning, and continuously updating the course based on their learning speed.…”

Section: Introductionmentioning

confidence: 99%

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Analysis and Construction of the User Characteristic Model in the Adaptive Learning System for Personalized Learning

Zhao

Long

2022

Computational Intelligence and Neuroscience

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Adaptive Learning System (ALS) is a supportive environment, which dynamically provides learners with services that can satisfy their demand for personalized learning in accordance with the differentiation of their individual traits. At present, study on ALS is still in the exploratory stage, and there are still many fields that deserve to be studied thoroughly. User characteristic model is the foundation and core of ALS and the key to the implementation of intelligent and personalized recommendation service. Based on this, this paper intends to analyze learners’ characteristics in ALS through several dimensions, such as basic information, interest, preference, cognitive level and learning style, through which learners’ user characteristic model is established. In the end, ALS, which supports the function of personalized recommendation, is implemented based on this model. It is suggested by the result of the simulation experiment that ALS, which is developed through this model, demonstrates a satisfying effect in recommendation, and it can dynamically recommend appropriate learning resources in accordance with learners’ personalized demands through which learners’ quality and efficiency of learning can be effectively enhanced to a certain extent.

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Section: User Interest and Preferencementioning

confidence: 99%

Section: User Interest and Preferencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analysis and Construction of the User Characteristic Model in the Adaptive Learning System for Personalized Learning

Zhao

Long

2022

Computational Intelligence and Neuroscience

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“…Further, the relationship between the products produced by e‐learning platform was determined by the developed recommendation system. Rafiq et al 23 suggested the feasibility of decoherence the question information in the E‐learning system. Question messages reading, parsing and classification were comprised in the information arrangement.…”

Section: Related Workmentioning

confidence: 99%

An efficient framework for the similarity prediction with query recommendation in E‐learning system

Prasad

Muthappa²

2022

Concurrency and Computation

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Summary A novel model of data similarity estimation and clustering method is proposed in this article to retrieve the relevant data with the best matching in big data processing. An advanced model of graph distance pattern (GDP) method with lexical subgroup (LS) system is used to estimate the similarity between the query data and the entire database. With the help of neural network, the relevancy of feature attributes in the database are predicted and matching index is sorted to provide the recommended data for given query data. This was achieved by using the correlated sim‐neural network (CSNN). This is an enhanced model of neural network technology to find the relevancy based on the correlation factor of feature set. The training process of CSNN classifier is carried by estimating the correlation factor of the attributes of dataset. These are forms as the clusters and paged with proper indexing based on the LS parameter of similarity metric. The results obtained by the proposed system for recall, precision, accuracy, error rate, F‐measure, kappa coefficient, specificity, and MCC are 0.98, 0.98, 0.97, 0.03, 0.99, 0.991, 0.986, and 0.984, respectively.

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“…The users can International Journal of Intelligent Engineering and Systems, Vol. 16 obtain the information easily by using the web page recommendation system. A hybrid semantic algorithm used for the web image retrieval based on the ontology classification is proposed supporting to the query of the user.…”

Section: Introductionmentioning

confidence: 99%

Web Page Recommendation System Based on Text and Image Pattern Extraction Classification Model

2023

IJIES

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Classification and analysis are improved factors for the real-time automation system. We need to analyze the attribute level in the area to predict the type of web page that can be predicted. This paper proposed a novel feature prediction model and the classification algorithm to estimate the attribute level of data and its probabilistic image to analyze the type of content that can be predicted on the page. This is to classify the different types of web pages which is better to perform the web page recommendation. For this process, dynamic lexical magnitude pattern (DLMP) system and correlated boltzmann machines (CBM) based classification model are used to analyze the attribute and image of the page area. The dataset consists of collections of attributes and images at various data samples for a page. In the DLMP-PGDT-based feature analysis method; the extract of the attribute and image in various texture patterns are analyzed and framed as the pattern for the given dataset. Then from that, an improved neural network architecture based on the block probability analysis is used to classify the data pattern to predict the class of web page according to the features of the dataset. This classification model assists were to recommend the content. The result analysis presents the comparison result of the proposed work with the web page classification technique like TrAdaBoost algorithm where proposed technique achieved accuracy of about 98%.

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Intelligent query optimization and course recommendation during online lectures in E-learning system

Cited by 25 publications

References 48 publications

Analysis and Construction of the User Characteristic Model in the Adaptive Learning System for Personalized Learning

Analysis and Construction of the User Characteristic Model in the Adaptive Learning System for Personalized Learning

An efficient framework for the similarity prediction with query recommendation in E‐learning system

Web Page Recommendation System Based on Text and Image Pattern Extraction Classification Model

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