References
[1]. Parthiban K, Pandey D, Pandey B K. Impact of SARS-CoV-2 in Online Education, Predicting and Contrasting Mental Stress of Young Students: A Machine Learning Approach[J]. Augmented Human Research, 2021, 6(1): 1-7.
[2]. Ruiperez-Valiente J A, Muñoz-Merino P J, Alexandron G, et al. Using machine learning to detect ‘multiple-account’cheating and analyze the influence of student and problem features[J]. IEEE transactions on learning technologies, 2017, 12(1): 112-122.
[3]. Chien H Y, Kwok O M, Yeh Y C, et al. Identifying At-Risk Online Learners by Psychological Variables Using Machine Learning Techniques[J]. Online Learning, 2020, 24(4): 131-146.
[4]. Stimpson A J, Cummings M L. Assessing intervention timing in computer-based education using machine learning algorithms[J]. IEEE Access, 2014, 2: 78-87.
[5]. Duzhin F, Gustafsson A. Machine learning-based app for self-evaluation of teacher-specific instructional style and tools[J]. Education Sciences, 2018, 8(1): 7.
[6]. Waheed H, Hassan S U, Aljohani N R, et al. Predicting academic performance of students from VLE big data using deep learning models[J]. Computers in Human behavior, 2020, 104: 106189.
[7]. Tsai S C, Chen C H, Shiao Y T, et al. Precision education with statistical learning and deep learning: a case study in Taiwan[J]. International Journal of Educational Technology in Higher Education, 2020, 17(1): 1-13.
[8]. Hussain S, Muhsion Z F, Salal Y K, et al. Prediction Model on Student Performance based on Internal Assessment using Deep Learning[J]. iJET, 2019, 14(8): 4-22.
[9]. Li C, Zhou H. Enhancing the efficiency of massive online learning by integrating intelligent analysis into MOOCs with an application to education of sustainability[J]. Sustainability, 2018, 10(2): 468.
[10]. Sosinskaya S, Dorofeev R, Rogacheva S, et al. Generating Data on Individual Learning Paths for Classification Using Deep Learning Networks[C]//8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020). Atlantis Press, 2020: 369-374.
[11]. Dias S B, Hadjileontiadou S J, Diniz J, et al. DeepLMS: a deep learning predictive model for supporting online learning in the Covid-19 era[J]. Scientific reports, 2020, 10(1): 1-17.
[12]. Kim B H, Vizitei E, Ganapathi V. GritNet: Student performance prediction with deep learning[J]. arXiv preprint arXiv:1804.07405, 2018.
[13]. Lee K, Chung J, Cha Y, et al. Machine learning approaches for learning analytics: Collaborative filtering or regression with experts[C]//NIPS Workshop, Dec. 2016: 1-11.
[14]. Tang J, Zhou X, Zheng J. Design of Intelligent classroom facial recognition based on Deep Learning[C]//Journal of Physics: Conference Series. IOP Publishing, 2019, 1168(2): 022043.
[15]. He Y, Li T. A lightweight CNN model and its application in intelligent practical teaching evaluation[C]//MATEC Web of Conferences. EDP Sciences, 2020, 309: 05016.
[16]. Qin, J. and He, Z.S., 2005, August. A SVM face recognition method based on Gabor-featured key points. In Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on (Vol. 8, pp. 5144-5149). IEEE.
[17]. Random Forest's homepage (by Leo Breiman and Adele Cutler)
[18]. Peng C Y J, Lee K L, Ingersoll G M. An introduction to logistic regression analysis and reporting[J]. The journal of educational research, 2002, 96(1): 3-14.
[19]. Maulud D, Abdulazeez A M. A Review on Linear Regression Comprehensive in Machine Learning[J]. Journal of Applied Science and Technology Trends, 2020, 1(4): 140-147.
Cite this article
Li,J. (2023). A Systematic Study on Intelligent Learning Techniques for Online Education. Applied and Computational Engineering,2,322-330.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).
References
[1]. Parthiban K, Pandey D, Pandey B K. Impact of SARS-CoV-2 in Online Education, Predicting and Contrasting Mental Stress of Young Students: A Machine Learning Approach[J]. Augmented Human Research, 2021, 6(1): 1-7.
[2]. Ruiperez-Valiente J A, Muñoz-Merino P J, Alexandron G, et al. Using machine learning to detect ‘multiple-account’cheating and analyze the influence of student and problem features[J]. IEEE transactions on learning technologies, 2017, 12(1): 112-122.
[3]. Chien H Y, Kwok O M, Yeh Y C, et al. Identifying At-Risk Online Learners by Psychological Variables Using Machine Learning Techniques[J]. Online Learning, 2020, 24(4): 131-146.
[4]. Stimpson A J, Cummings M L. Assessing intervention timing in computer-based education using machine learning algorithms[J]. IEEE Access, 2014, 2: 78-87.
[5]. Duzhin F, Gustafsson A. Machine learning-based app for self-evaluation of teacher-specific instructional style and tools[J]. Education Sciences, 2018, 8(1): 7.
[6]. Waheed H, Hassan S U, Aljohani N R, et al. Predicting academic performance of students from VLE big data using deep learning models[J]. Computers in Human behavior, 2020, 104: 106189.
[7]. Tsai S C, Chen C H, Shiao Y T, et al. Precision education with statistical learning and deep learning: a case study in Taiwan[J]. International Journal of Educational Technology in Higher Education, 2020, 17(1): 1-13.
[8]. Hussain S, Muhsion Z F, Salal Y K, et al. Prediction Model on Student Performance based on Internal Assessment using Deep Learning[J]. iJET, 2019, 14(8): 4-22.
[9]. Li C, Zhou H. Enhancing the efficiency of massive online learning by integrating intelligent analysis into MOOCs with an application to education of sustainability[J]. Sustainability, 2018, 10(2): 468.
[10]. Sosinskaya S, Dorofeev R, Rogacheva S, et al. Generating Data on Individual Learning Paths for Classification Using Deep Learning Networks[C]//8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020). Atlantis Press, 2020: 369-374.
[11]. Dias S B, Hadjileontiadou S J, Diniz J, et al. DeepLMS: a deep learning predictive model for supporting online learning in the Covid-19 era[J]. Scientific reports, 2020, 10(1): 1-17.
[12]. Kim B H, Vizitei E, Ganapathi V. GritNet: Student performance prediction with deep learning[J]. arXiv preprint arXiv:1804.07405, 2018.
[13]. Lee K, Chung J, Cha Y, et al. Machine learning approaches for learning analytics: Collaborative filtering or regression with experts[C]//NIPS Workshop, Dec. 2016: 1-11.
[14]. Tang J, Zhou X, Zheng J. Design of Intelligent classroom facial recognition based on Deep Learning[C]//Journal of Physics: Conference Series. IOP Publishing, 2019, 1168(2): 022043.
[15]. He Y, Li T. A lightweight CNN model and its application in intelligent practical teaching evaluation[C]//MATEC Web of Conferences. EDP Sciences, 2020, 309: 05016.
[16]. Qin, J. and He, Z.S., 2005, August. A SVM face recognition method based on Gabor-featured key points. In Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on (Vol. 8, pp. 5144-5149). IEEE.
[17]. Random Forest's homepage (by Leo Breiman and Adele Cutler)
[18]. Peng C Y J, Lee K L, Ingersoll G M. An introduction to logistic regression analysis and reporting[J]. The journal of educational research, 2002, 96(1): 3-14.
[19]. Maulud D, Abdulazeez A M. A Review on Linear Regression Comprehensive in Machine Learning[J]. Journal of Applied Science and Technology Trends, 2020, 1(4): 140-147.