Cited By
View all- Zanellati AZingaro SGabbrielli M(2024)Balancing Performance and Explainability in Academic Dropout PredictionIEEE Transactions on Learning Technologies10.1109/TLT.2024.342595917(2140-2153)Online publication date: 1-Jan-2024
KIDNet: A Knowledge-Aware Neural Network Model for Academic Performance Prediction
Authors: 
Tao Tang, Jie Hou, Teng Guo, Xiaomei Bai, Xue Tian, Azadeh Noori HoshyarAuthors Info & Claims
Pages 37 - 44
Abstract
Academic performance prediction and analysis in educational data mining is meaningful for instructors to know the student’s ongoing learning status, and also provide appropriate help to students as early as possible if academic difficulties appear. In this paper, we first collect the basic information of students and courses as features. Then, we propose a novel knowledge extraction framework to obtain course knowledge features to reinforce feature groups. The comparative analyses of the knowledge similarity and average grades of the courses in all terms demonstrate a strong correlation between them. Furthermore, we build the Knowledge Interaction Discovery Network (KIDNet) model, based on factorization machine (FM) and deep neural network (DNN) algorithms. This model uses FM to model lower-order interactions of sparse features and employs DNN to model higher-order interactions of both dense and sparse features. The effectiveness of KIDNet has been validated by conducting experiments based on a real-world dataset.
References
[1]
Mashael Al-Luhaybi, Leila Yousefi, Stephen Swift, Steve Counsell, and Allan Tucker. 2019. Predicting Academic Performance: A Bootstrapping Approach for Learning Dynamic Bayesian Networks. In Artificial Intelligence in Education, Seiji Isotani, Eva Millán, Amy Ogan, Peter Hastings, Bruce McLaren, and Rose Luckin (Eds.). Springer International Publishing, Cham, 26–36.
[2]
Hana Bydžovská. 2016. A Comparative Analysis of Techniques for Predicting Student Performance.International Educational Data Mining Society (2016).
[3]
Qiumei Chen and Minqiang Zhang. 2010. Development of Cognitive Diagnosis Models and Their Application Methods. Advances in Psychological Science 18, 03, Article 522(2010), 522-529 pages.
[4]
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, and Hemal Shah. 2016. Wide and Deep Learning for Recommender Systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems (Boston, MA, USA) (DLRS 2016). Association for Computing Machinery, New York, NY, USA, 7–10. https://doi.org/10.1145/2988450.2988454
[5]
Ali Daud, Naif Radi Aljohani, Rabeeh Ayaz Abbasi, Miltiadis D. Lytras, Farhat Abbas, and Jalal S. Alowibdi. 2017. Predicting Student Performance Using Advanced Learning Analytics. In Proceedings of the 26th International Conference on World Wide Web Companion (Perth, Australia) (WWW ’17 Companion). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 415–421. https://doi.org/10.1145/3041021.3054164
[6]
Francesca Del Bonifro, Maurizio Gabbrielli, Giuseppe Lisanti, and Stefano Pio Zingaro. 2020. Student Dropout Prediction. In Artificial Intelligence in Education, Ig Ibert Bittencourt, Mutlu Cukurova, Kasia Muldner, Rose Luckin, and Eva Millán (Eds.). Springer International Publishing, Cham, 129–140.
[7]
Shumin Deng, Ningyu Zhang, Wen Zhang, Jiaoyan Chen, Jeff Z. Pan, and Huajun Chen. 2019. Knowledge-Driven Stock Trend Prediction and Explanation via Temporal Convolutional Network. In Companion Proceedings of The 2019 World Wide Web Conference (San Francisco, USA) (WWW ’19). Association for Computing Machinery, New York, NY, USA, 678–685. https://doi.org/10.1145/3308560.3317701
[8]
Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011. Deep Sparse Rectifier Neural Networks. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics(Proceedings of Machine Learning Research, Vol. 15), Geoffrey Gordon, David Dunson, and Miroslav Dudík (Eds.). PMLR, Fort Lauderdale, FL, USA, 315–323. https://proceedings.mlr.press/v15/glorot11a.html
[9]
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247(2017).
[10]
Teng Guo, Feng Xia, Shihao Zhen, Xiaomei Bai, Dongyu Zhang, Zitao Liu, and Jiliang Tang. 2020. Graduate Employment Prediction with Bias. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 670–677.
[11]
Yuchin Juan, Yong Zhuang, Wei-Sheng Chin, and Chih-Jen Lin. 2016. Field-aware factorization machines for CTR prediction. In Proceedings of the 10th ACM conference on recommender systems. 43–50.
[12]
Hamid Karimi, Tyler Derr, Jiangtao Huang, and Jiliang Tang. 2020. Online Academic Course Performance Prediction Using Relational Graph Convolutional Neural Network.International Educational Data Mining Society (2020).
[13]
Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980(2014).
[14]
Weijie Liu, Peng Zhou, Zhe Zhao, Zhiruo Wang, Qi Ju, Haotang Deng, and Ping Wang. 2020. K-bert: Enabling language representation with knowledge graph. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 2901–2908.
[15]
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Distributed representations of words and phrases and their compositionality. arXiv preprint arXiv:1310.4546(2013).
[16]
Nasheen Nur, Noseong Park, Mohsen Dorodchi, Wenwen Dou, Mohammad Javad Mahzoon, Xi Niu, and Mary Lou Maher. 2019. Student network analysis: a novel way to predict delayed graduation in higher education. In International Conference on Artificial Intelligence in Education. Springer, 370–382.
[17]
Steffen Rendle, Zeno Gantner, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2011. Fast context-aware recommendations with factorization machines. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. 635–644.
[18]
Sudeshna Roy, Meghana Madhyastha, Sheril Lawrence, and Vaibhav Rajan. 2019. Inferring concept prerequisite relations from online educational resources. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 9589–9594.
[19]
Ying Shan, T Ryan Hoens, Jian Jiao, Haijing Wang, Dong Yu, and JC Mao. 2016. Deep crossing: Web-scale modeling without manually crafted combinatorial features. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 255–262.
[20]
Quan Wang, Zhendong Mao, Bin Wang, and Li Guo. 2017. Knowledge graph embedding: A survey of approaches and applications. IEEE Transactions on Knowledge and Data Engineering 29, 12(2017), 2724–2743.
[21]
Feng Xia, Ke Sun, Shuo Yu, Abdul Aziz, Liangtian Wan, Shirui Pan, and Huan Liu. 2021. Graph Learning: A Survey. IEEE Transactions on Artificial Intelligence 2, 2(2021), 109–127. https://doi.org/10.1109/TAI.2021.3076021
[22]
Feng Xia, Shuo Yu, Chengfei Liu, Jianxin Li, and Ivan Lee. 2021. CHIEF: Clustering with Higher-order Motifs in Big Networks. IEEE Transactions on Network Science and Engineering (2021).
[23]
Huaxiu Yao, Min Nie, Han Su, Hu Xia, and Defu Lian. 2017. Predicting academic performance via semi-supervised learning with constructed campus social network. In International Conference on Database Systems for Advanced Applications. Springer, 597–609.
[24]
Michael V Yudelson, Kenneth R Koedinger, and Geoffrey J Gordon. 2013. Individualized bayesian knowledge tracing models. In International conference on artificial intelligence in education. Springer, 171–180.
[25]
Weinan Zhang, Tianming Du, and Jun Wang. 2016. Deep learning over multi-field categorical data. In European conference on information retrieval. Springer, 45–57.
- KIDNet: A Knowledge-Aware Neural Network Model for Academic Performance Prediction
Recommendations
Evolution of Knowledge Sharing Model in the Development of Supply Chain Management
ICOIP '10: Proceedings of the 2010 International Conference on Optoelectronics and Image Processing - Volume 01There are not only logistics, trade flow and capital flow but also abundant knowledge flow in supply chain. Knowledge complementarity among enterprises is the important basis and prerequisite of supply chain knowledge flowing and knowledge sharing. ...
Knowledge graph embedding model with attention-based high-low level features interaction convolutional network
AbstractKnowledge graphs are sizeable graph-structured knowledge with both abstract and concrete concepts in the form of entities and relations. Recently, convolutional neural networks have achieved outstanding results for more expressive representations ...
Highlights- We propose a knowledge graph embedding model with attention-based high-low level feature interaction convolutional network.
- A criss-cross attention mechanism based on consecutive sparsely-connected graphs is introduced to our model.
A knowledge-enhanced interactive graph convolutional network for aspect-based sentiment analysis
AbstractDeep neural networks, especially graph neural networks, have made great progress in aspect-based sentiment analysis. Knowledge graphs can provide rich auxiliary information for aspect-based sentiment analysis. However, existing models cannot ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
December 2021
541 pages
Copyright © 2021 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 11 April 2022
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
WI-IAT '21
Sponsor:
WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence
December 14 - 17, 2021
VIC, Melbourne, Australia
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 84Total Downloads
- Downloads (Last 12 months)14
- Downloads (Last 6 weeks)3
Reflects downloads up to 12 Nov 2024
Other Metrics
Citations
Cited By
View all- Zanellati AZingaro SGabbrielli M(2024)Balancing Performance and Explainability in Academic Dropout PredictionIEEE Transactions on Learning Technologies10.1109/TLT.2024.342595917(2140-2153)Online publication date: 1-Jan-2024
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format