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Research on MOOC Teaching Mode in Higher Education Based on Deep Learning

Yuan Tian, Yingjie Sun, Lijing Zhang, Wanqiang Qi, Baiyuan Ding
2022 Computational Intelligence and Neuroscience  
platform, implements a deep neural network algorithm, and recommends related services.  ...  In one step, this article takes MOOC education resources as the research goal.  ...  TOP-N recommen- dation is to recommend the Top-N items that users of the MOOC platform may like.  ... 
doi:10.1155/2022/8031602 pmid:35132317 pmcid:PMC8817835 fatcat:lmyn6iztzfarjal5oldzrwlriu

Automatic Generation of Meta-Path Graph for Concept Recommendation in MOOCs

Jibing Gong, Cheng Wang, Zhiyong Zhao, Xinghao Zhang
2021 Electronics  
We first clarify the concept recommendation in MOOCs as a reinforcement learning problem to offer a personalized and dynamic knowledge concept label list to users.  ...  To deal with the above issues, this paper proposes AGMKRec, a novel reinforced concept recommendation model with a heterogeneous information network.  ...  Graph Convolutional Network (GCN) H (l+1) = σ D − 1 2ÃD − 1 2 H (l) W (l) (1) whereà = A + I ∈ R N×N is the adjacency matrix A of the graph G with added self-connections, D is the degree matrix ofà and  ... 
doi:10.3390/electronics10141671 fatcat:7w7ihedkpzf7rltkpfagutgose

Online Course Recommendation Using Deep Convolutional Neural Network with Negative Sequence Mining

Ming Gao, Yonghan Luo, Xiaonan Hu, Yifan Zhu
2022 Wireless Communications and Mobile Computing  
To solve this problem, this paper proposes a personalized course recommendation model based on convolutional neural network combined with negative sequence pattern mining.  ...  Massive Open Online Course (MOOC) has been criticized for low completion rates, and one of the major reasons is that it fails to offer personalized course recommendations for different users with different  ...  This work was supported in part by the Professional Site Construction Project in Electronic Information (Computer Science) of Beijing Information Science and Technology University under Grant 5112211038  ... 
doi:10.1155/2022/9054149 fatcat:x7uegvms7ber7d7gwhine7pvle

A Systematic Mapping Review on MOOC Recommender Systems

Imranuddin, Ali Shariq Imran, Khan Muhammad, Nosheen Fayyaz, Muhammad Sajjad
2021 IEEE Access  
[157] proposed Elmo model to recommend learning resources. Similarly end to end graph neural networked-based approach was used in Gong et al.  ...  [59] to recommend top-n discussion forums and Yang et al. [103] for a social recommendation.  ... 
doi:10.1109/access.2021.3101039 fatcat:vnhraonfujgstdvcnpcwi6lxxe

A Survey of Online Course Recommendation Techniques

Jinliang Lu
2022 Open Journal of Applied Sciences  
The recommender system has been widely used in various Internet applications due to its high efficiency in filtering information, helping users to quickly find personalized resources from thousands of  ...  In addition, due to its great use value, many new researches have been proposed in the field of recommender systems in recent years, but there are not many works on online course recommendation at present  ...  graph through the graph neural network layer.  ... 
doi:10.4236/ojapps.2022.121010 fatcat:ww7vgve2bvecjfjyzc2iqevcam

Layer-refined Graph Convolutional Networks for Recommendation [article]

Xin Zhou, Donghui Lin, Yong Liu, Chunyan Miao
2022 arXiv   pre-print
Recommendation models utilizing Graph Convolutional Networks (GCNs) have achieved state-of-the-art performance, as they can integrate both the node information and the topological structure of the user-item  ...  In this paper, we first identify a recommendation dilemma of over-smoothing and solution collapsing in current GCN-based models.  ...  To capture the structure information associated with users' behavior data, various graph neural networks-based recommendation methods have also been proposed [24] - [27] .  ... 
arXiv:2207.11088v1 fatcat:xpxz76iisfgv3nmksmkjloedte

Mining Precedence Relations among Lecture Videos in MOOCs via Concept Prerequisite Learning

Kui Xiao, Youheng Bai, Shihui Wang, Zhen Liu
2021 Mathematical Problems in Engineering  
And then, an LSTM-based neural network model was used to measure prerequisite relations among the main concepts.  ...  Moreover, the precedence relations between lecture videos in a MOOC are often not clearly explained.  ...  In our experiments, we also used a Graph embedding (GE) method, PyTorch-BigGraph (PBG) [26] , as a baseline to compare our model with graph learning methods.  ... 
doi:10.1155/2021/7655462 fatcat:pjt3nczlnrf5doy7ahekgw7g5y

Inferring Concept Prerequisite Relations from Online Educational Resources

Sudeshna Roy, Meghana Madhyastha, Sheril Lawrence, Vaibhav Rajan
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
PREREQ is designed using latent representations of concepts obtained from the Pairwise Latent Dirichlet Allocation model, and a neural network based on the Siamese network architecture.  ...  Inferring prerequisite relations between educational concepts is required for modern large-scale online educational technology applications such as personalized recommendations and automatic curriculum  ...  Each document is represented by the concepts contained in it, i.e. d i = {C ∩ W i } where W i is the set of n-grams in document d i , for n ∈ {1, 2, 3}.  ... 
doi:10.1609/aaai.v33i01.33019589 fatcat:4fpkn4rg3bhy5c2daxob46yuji

Inferring Concept Prerequisite Relations from Online Educational Resources [article]

Sudeshna Roy, Meghana Madhyastha, Sheril Lawrence, Vaibhav Rajan
2019 arXiv   pre-print
PREREQ is designed using latent representations of concepts obtained from the Pairwise Latent Dirichlet Allocation model, and a neural network based on the Siamese network architecture.  ...  Inferring prerequisite relations between educational concepts is required for modern large-scale online educational technology applications such as personalized recommendations and automatic curriculum  ...  So, we compare with MOOC-RF using metrics precision, recall and F-score.  ... 
arXiv:1811.12640v2 fatcat:iw3lmc32qzdrhhp3vtaoolcvyy

Enhanced Clustering-based MOOC Recommendations using LinkedIn Profiles (MR-LI)

Fatimah Alruwaili, Dimah Alahmadi
2021 International Journal of Advanced Computer Science and Applications  
In the experiment result, four clusters were provided with the top-N course recommendations. Ultimately, the proposed approach was evaluated, and the F1-score of the approach was .81.  ...  With the rapid development of massive open online courses (MOOCs), the interest of learners in MOOCs has increased significantly. MOOC platforms offer thousands of varied courses with many options.  ...  This graphical model combines probability and statistics with machine learning and neural networks in MOOC environments.  ... 
doi:10.14569/ijacsa.2021.0120818 fatcat:kfrt6htirrecpfksiuoi47zieu

Recommendation system based on heterogeneous feature: A survey

Hui Wang, ZiChun Le, Xuan Gong
2020 IEEE Access  
In 2019, Nzeko et al. [20] proposed a novel graph-based framework GraFC2T2, which enables easy combination and comparison of top-N recommended edge information.  ...  and short-term memory (LSTM), and graph neural network (GNN)-based recommendation methods.  ... 
doi:10.1109/access.2020.3024154 fatcat:clxk77bcr5hdjd3hnxxi6wzlr4

MOOCRep: A Unified Pre-trained Embedding of MOOC Entities [article]

Shalini Pandey, Jaideep Srivastava
2021 arXiv   pre-print
exist in the graph, and 2) domain-oriented objective to effectively incorporate the complexity level of concepts.  ...  This richer information includes the graph relationship between the lectures, concepts, and courses along with the domain knowledge about the complexity of a concept.  ...  Neural network based methods have been proposed to solve this task.  ... 
arXiv:2107.05154v1 fatcat:t7eeeqhtffevhdnbao64o2tbbe

Peer-inspired Student Performance Prediction in Interactive Online Question Pools with Graph Neural Network [article]

Haotian Li, Huan Wei, Yong Wang, Yangqiu Song, Huamin Qu
2020 arXiv   pre-print
In this paper, we propose a novel approach using Graph Neural Networks (GNNs) to achieve better student performance prediction in interactive online question pools.  ...  Existing work on student performance prediction targets at online learning platforms with predefined course curriculum and accurate knowledge labels like MOOC platforms, but they are not able to fully  ...  ACKNOWLEDGMENTS This work is partially sponsored by Innovation and Technology Fund (ITF) with No. ITS/388/17FP. Y. Wang is the corresponding author.  ... 
arXiv:2008.01613v1 fatcat:v7spnglvbfcvnltows7x6wvbbi

Neural Network-Based Collaborative Filtering for Question Sequencing [article]

Lior Sidi, Hadar Klein
2020 arXiv   pre-print
In this paper, we used the Neural Collaborative Filtering (NCF) model to generate question sequencing and compare it to a pair-wise memory-based question sequencing algorithm - EduRank.  ...  Sequencing questions is the art of generating a personalized quiz for a target learner.  ...  Learners can study from their laptop and gain education from top lectures with Massive Open Online Courses (MOOCs).  ... 
arXiv:2004.12212v1 fatcat:zzitx6zl6fevbgpwdur5rj4w7y

Graph-Community-Enabled Personalized Course-Job Recommendations with Cross-Domain Data Integration

Guoqing Zhu, Yan Chen, Shutian Wang
2022 Sustainability  
The current recommendation research has attracted wide attention in the academic and industrial areas.  ...  With millions of students/employees browsing course information and job postings every day, the need for accurate, effective, meaningful, and transparent course and job recommender systems is more evident  ...  Acknowledgments: The authors will thank Xiaozhong Liu for his great help in writing assistance and data providing. Thanks also to IUB for the data support.  ... 
doi:10.3390/su14127439 fatcat:oyve6kay25bepok7b2mrawfxwq
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