The Impact of Machine Learning and Emerging Technologies on E-Learning User Experience
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Abstract
This study aims to determine the impacts of Machine Learning and Information Technology on e-Learning. Information technology has significantly impacted every aspect of human endeavours, including education. This research examined how machine learning can enhance users' experiences in distance learning. The dataset for the model was extracted from Kaggle, and training, testing, and evaluation were performed. The accuracy reached 60% due to the bias in the data distribution. Finally, the deployment of the model to monitor students' emotions in real-time, offering personalised content recommendations based on the detected emotional states, would enhance the user experience and facilitate a supportive learning environment by addressing negative emotions.
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Acharya, M. S., Armaan, A., & Antony, A. S. (2019). A comparison of regression models for prediction of graduate admissions," in 2019 International Conference on Computational Intelligence in Data Science (ICCIDS) pp. 1-5.
Alex, S., & Vishwanathan, S.V.N. (2010). Introduction to Machine Learning. Published by the press syndicate of the University of Cambridge, Cambridge, United Kingdom. Copyright ⓒ Cambridge University Press 2008. ISBN: 0- 521-82583-0. Available at KTH website: https://www.kth.se/social/upload/53a14887f276540ebc81ae c3/online.pdf Retrieved from
Amazon Web Services (2023). “Machine learning in Education” retrieved online at https://aws.amazon.com/education/ml-in-education. on 2/11/2023.
Brownlee, J. (2016). Master machine learning algorithms. Discover how they work and implement them from scratch. © Copyright 2016 Jason Brownlee.
Brownlee, J. (2020). "Machine Learning Mastery," 4 Types of Classification Tasks in Machine Learning,. [Online]. Available: https://machinelearningmastery.com/types-of-classification-in-machinelearning/.
Chengliang W., Xiaojiao C., Teng Y., Yidan L., & Yuhui J., (2024). Education reform and change driven by digital technology: a bibliometric study from a global perspective. Humanities and social sciences communications.
Chiu, J. P. C. & Nichols, E. (2016). Named entity recognition with bidirectional LSTM-CNNs. TACL, 4:357–370.
Dewan, M.A.A., Murshed, M., & Lin, F. (2019). Engagement detection in online learning: A review. Smart Learn. Environ.
Freeman, S. (2014). “Active learning increases student performance in science, engineering, and mathematics.” Proceedings of the National Academy of Scientists, 111(23), pp. 8410–841 Implementation In Nigeria: Case Study National Open University Of Nigeria (Noun).
Gupta, P., Saxena, N., Sharma, M., and Tripathi, J. (2018). Deep neural network for human face recognition. International Journal of Engineering and Manufacturing (IJEM), 8(1), 63-71
Han, J. Pei, J., & Yin, Y. (2000) Mining frequent patterns without candidate generation. In: ACM Sigmod Record, ACM. ;29: 1–12.
Hazarika, D., Poria, S., Zadeh, A., Cambria, E., Morency, L. P., & Zimmermann, R. (2018). Conversational memory network for emotion recognition in dyadic dialogue videos. In Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting (Vol. 2018, p. 2122). NIH Public Access.
Hyun-il, L. (2019). A Linear Regression Approach to Modeling Software Characteristics for Classifying Similar Software, in 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), pp. 942-943.
Jung, H., Lee, S., Park, S., Kim, B., Kim, J., Lee, I., & Ahn, C. (2015). Development of deep learning-based facial expression recognition system. In 2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV) (pp. 1-4). IEEE.
Jurafsky, D. & Martin, J.H. (2024). Speech and language processingFramework. Human IT: Journal for Information Technology Studies as a Human Science, 13(3),.89-114.
Jurafsky, D. & Martin, J.H. (2024). Speech and language processingFramework. Human IT: Journal for Information Technology Studies as a Human Science, 13(3), pp.89-114.
Keerthi, S.S., S.K. Shevade, Bhattacharyya, C. & Radha K. M.(2021) . Improvements to platt’s smo algorithm for svm classifer design. Neural Comput.;13(3), 637–49.
Kotsiantis, S. B. (2017). Supervised Machine Learning: A Review of Classification Techniques. Pp.249–268.Retrieved from IJS website: http://wen.ijs.si/ojs2.4.3/index.php/informatica/article/download/148/140.
Liu, Z., Zhang, W., Sun, J., Cheng, H. N., Peng, X., & Liu, S. (2016). Emotion and associated topic detection for course comments in a MOOC platform. In 2016 International Conference on Educational Innovation through Technology (EITT) (pp. 15-19). IEEE.
Maulud, D. H., and Abdulazeez, A. M. (2020). A review on linear regression comprehensive in machine learning. Journal of Applied Science and Technology Trends, 01(4). 140 –147.
Mengchi, L., & Dongmei, Y. (2022).Towards intelligent E-learning systems. under exclusive licence to Springer Science, Business Media, LLC, part of Springer Nature.
Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2012). Basics of device learning. Cambridge, MA: MIT Press.
Ojajuni, O., Ayeni, F., Akodu, O., Ekanoye, F., Adewole, S., Ayo, T., Misra, S., Mbarika, V. (2021). Predicting Student Academic Performance Using Machine Learning . Springer Nature Switzerland AG. O. Gervasi et al. (Eds.): ICCSA 2021, LNCS 12957, 481–491.
Osisanwo, F. Y., Akinsola, J. T., Hinmikaiye, J. O., Olakanmi, O., & Akinjobi, J. (2017). "Supervised Machine Learning Algorithms: Classification and Comparison, International Journal of Computer Trends and Technology (IJCTT), 48(3), 128-138.
Rattan, P., Penrice, D.D., & Simonetto, D.A. (2022). Artificial Intelligence and Machine Learning: What You Always Wanted to Know but Were Afraid to Ask. Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota Gastro Hep Advances 2022;1: 70–78
Rogers, Y., Sharp, H., & Preece, J. (2011). Interaction design: Beyond human-computer interaction (3rd ed.). John Wiley & Sons, Ltd.
Roopa, H., & Asha T., (2019). A linear model based on principal component analysis for disease prediction, IEEE Access, 7, 105314-105318.
Ruiz-Garcia, A., Elshaw, M., Altahhan, A., & Palade, V. (2018). A hybrid deep learning neural approach for emotion recognition from facial expressions for socially assistive robots. Neural Computing and Applications, 29(7), 359-373.
Sarker, I. H. (2019). Context-aware rule learning from smartphone data: survey, challenges and future directions. J Big Data. ;6(1):1–25.
Shalev-Shwartz, S., & Ben-David, D.S. (2014). Understanding Machine Learning: From Theory to Algorithms.
Sharma, S. (2024). Benefits or concerns of AI: A multistakeholder responsibility. 157.
Sinaga, K. P. & Yang, M. (2023). Unsupervised K-Means Clustering Algorithm Department of Applied Mathematics, Chung Yuan Christian University, Chung-Li 32023, Taiwan
Smith, L. (2021) “Balancing automation and human interaction in adaptive learning,” International Journal of Computer and Data Science, 12(2), 89–104.
Taiwo, O. A. (2010). Types of Machine Learning Algorithms, New Advances in Machine Learning, Yagang Zhang (Ed.), ISBN: 978-953-307-034-6, InTech, University of Portsmouth United Kingdom. Pp–31. Available at InTech open website: http://www.intechopen.com/books/new-advances-inmachine-learning/types-of-machine-learning-algorithms
Taye, M.M. (2023). Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions. Data Science and Artificial Intelligence, Philadelphia University, Amman 19392, Jordan. DOI: 10.3390/computers12050091
Tom, M.M. (2020). The Discipline of Machine Learning. School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA CMU-ML-06-108.
Vassilis S.M., Juergen H. (2010). “Where do machine learning and human-computer interaction meet” SSN: 0883-9514 (Print) 1087-6545 (Online) Journal homepage: https://www.tandfonline.com/loi/uaai20
Zarsky T (2016). The trouble with algorithmic decisions: An analytic road map to examine efficiency and fairness in automated and opaque decision making. Science, Technology & Human Values 41(1): 118–132.
Zhang, J., Yin, Z., Chen, P., & Nichele, S. (2020). Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review. Information Fusion, 59,