Research on personalized recommendation of MOOC resources based on ontology

Li, Yuanmin; Chen, Dexin; Zhan, Zehui

doi:10.1108/itse-10-2021-0190

Cited by 10 publications

(3 citation statements)

References 33 publications

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“…Learners can choose the learning content and grasp the learning rhythm freely according to their own needs. At the same time, it also makes learners' abilities more diversified (Gan & Zhang, 2020;Li et al, 2022;Xu et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Personalized Course Resource Recommendation Algorithm Based on Deep Learning in the Intelligent Question Answering Robot Environment

Sun¹

2023

International Journal of Information Technologies and Systems Approach

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Aiming at the problems of difficult-to-extract effective information and insufficient feature extraction in the existing intelligent question answering robot environment, a personalized course resource recommendation algorithm based on deep learning is proposed. Firstly, the potential preferences of users are obtained through course-related data. Secondly, the authors use one-hot coding and embedding to convert word vectors into low-dimensional, dense real-valued vectors and input them into the CIN-GRU model. Finally, the attention mechanism is used to improve the attention of some words and the accuracy of personalized course recommendation. The experiment shows that when the recommended list is 35, the precision, recall, and F1 value of the proposed personalized course recommendation method are 0.862, 0.851, and 0.857, respectively, which are higher than those of the comparison method. Therefore, the performance of the proposed method in sustainable personalized course resource recommendation is better than that of the comparison method.

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Section: Introductionmentioning

confidence: 99%

Personalized Course Resource Recommendation Algorithm Based on Deep Learning in the Intelligent Question Answering Robot Environment

Sun¹

2023

International Journal of Information Technologies and Systems Approach

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“…However, MOOC, as one of the most important learning resources in the digital age, has faced many problems in its development, and the high dropout and attrition rates in MOOC education are the most criticized (Li et al , 2022). Studies show that the majority of MOOC completion rates range from 0.7% to 52.1%, with a mean of 12.6% (Jordan, 2015).…”

Section: Introductionmentioning

confidence: 99%

An evaluation model based on procedural behaviors for predicting MOOC learning performance: students’ online learning behavior analytics and algorithms construction

Tong

Zhan

2023

ITSE

Self Cite

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Purpose The purpose of this study is to set up an evaluation model to predict massive open online courses (MOOC) learning performance by analyzing MOOC learners’ online learning behaviors, and comparing three algorithms – multiple linear regression (MLR), multilayer perceptron (MLP) and classification and regression tree (CART). Design/methodology/approach Through literature review and analysis of data correlation in the original database, a framework of online learning behavior indicators containing 26 behaviors was constructed. The degree of correlation with the final learning performance was analyzed based on learners’ system interaction behavior, resource interaction behavior, social interaction behavior and independent learning behavior. A total of 12 behaviors highly correlated to learning performance were extracted as major indicators, and the MLR method, MLP method and CART method were used as typical algorithms to evaluate learners’ MOOC learning performance. Findings The behavioral indicator framework constructed in this study can effectively analyze learners’ learning, and the evaluation model constructed using the MLP method (89.91%) and CART method (90.29%) can better achieve the prediction of MOOC learners’ learning performance than using MLR method (83.64%). Originality/value This study explores the patterns and characteristics among different learning behaviors and constructs an effective prediction model for MOOC learners’ learning performance, which can help teachers understand learners’ learning status, locate learners with learning difficulties promptly and provide targeted instructional interventions at the right time to improve teaching quality.

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“…In latest years, MOOC structures such as Coursera, edX, and Scholastic Online have flourished and swiftly attracted a giant variety of learners. The ease with which customers can get entry to a giant variety of magnificent mastering assets in moot classification systems has significantly facilitated expertise sharing and online learning and supplied many possibilities for character newbies to get hold of training [1]. However, customers are regularly pressured via the "information overload" of the exploding quantity of publications on the Mootools platform.…”

Section: Introductionmentioning

confidence: 99%

Design and Application of Online Courses under the Threshold of Smart Innovation Education

Wang¹,

Xiong²,

Zhu³

2023

IJACSA

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With the rapid development of the Internet and the growing demand for education, a new online teaching mode, massive open online courses (MOOC), emerged in 2012. To address the problems of sparse data and poor recommendation effect in online course recommendation, this paper introduces deep learning into course recommendation and proposes an auxiliary information-based neural network model (IUNeu), on the basis of which a collaborative neural network filtering model (FIUNeu) is obtained by improving it. Firstly, the principles and technical details of the deep learning base model are studied in depth to provide technical support for course recommendation models and online learning recommendation systems. In this paper, based on the existing neural matrix decomposition model (NeuMF), we combine user information and course information and consider the interaction relationship between them to improve the accuracy of the model to represent users and courses. The neural network model of auxiliary information (IUNeu) is incorporated into the online learning platform, and the system development is completed with the design of front and back-end separation, realizing the functions of the online learning module, course collection module, course recommendation module, and resource download module. Finally, the experimental results are analyzed: under the same experimental conditions, the test experiments are repeated 10 times, and the RMSE calculation results are averaged. The RMSE value of the neural network collaborative filtering model (FIUNeu) proposed in this paper based on deep learning is 0.85517, which is the best performance and has a high accuracy rate of rating prediction, and is useful for alleviating the data sparsity problem.

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Research on personalized recommendation of MOOC resources based on ontology

Cited by 10 publications

References 33 publications

Personalized Course Resource Recommendation Algorithm Based on Deep Learning in the Intelligent Question Answering Robot Environment

Personalized Course Resource Recommendation Algorithm Based on Deep Learning in the Intelligent Question Answering Robot Environment

An evaluation model based on procedural behaviors for predicting MOOC learning performance: students’ online learning behavior analytics and algorithms construction

Design and Application of Online Courses under the Threshold of Smart Innovation Education

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