Digital Learning For Kids
Volume 5 - Issue 11, November 2022 Edition
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Author(s)
Sharmilan Sureshwaran, Nilakshana Rajeswaran, Kithusshand Raveendran, Sriram Rajeswaran
Keywords
Text To Speech (TTS), Machine Learning(ML),Image Processing, Web Scraping.
Abstract
The current educational system changed to use the digital learning system due to the Covid-19 pandemic situation. There is a huge need for the Digital Learning platforms that could use by the students and kids for the educational purpose. The currently available digital learning platforms needs to enhance, ensure the safety for kids, and can provide variety of services for the kids using the newly available technologies like Text to speech (TTS), Machine Learning (ML), Image Processing, and Web scraping Technologies. The research paper is proposing a new system that could help to the kids to the learn the things very easily via watching the videos that is generated automatically using the new technologies.
References
[1] “Unesco covid-19 educational disruption and response,” https://en.unesco.org/covid19/educationresponse.
[2] M. Najar, Alberto, G. Sanzana, A. Grace, Hasan, Amer, C. Romani,
J. Cristobal, Azevedo, J. P. W. De, Akmal, and Maryam, “Remote learning during covid-19 : Lessons from today, principles for tomorrow,” 2022.
[3] Zaharah, Indrayanto, C. Dhien Nourwahidah, A. Saehudin, H. Hasan, and Kamarusdiana, “The effectiveness of information technology as a learning media towards teaching role (case study for student due to pandemic covid-19),” in 2020 8th International Conference on Cyber and IT Service Management (CITSM), 2020, pp. 1–6.
[4] A. Tick, “Research on the digital learning and e-learning behaviour and habits of the early z generation,” in 2018 IEEE 22nd International Con- ference on Intelligent Engineering Systems (INES), 2018, pp. 000 033– 000 038.
[5] L. Alfaro, C. Rivera, J. Luna-Urquizo, E. Castan˜eda, J. Zuniga-Cueva, and M. Rivera-Chavez, “New trends in e-technologies and e-learning,” in 2021 IEEE World Conference on Engineering Education (EDUNINE), 2021, pp. 1–6.
[6] P. Jayawardhana, A. Aponso, N. Krishnarajah, and A. Rathnayake, “An intelligent approach of text-to-speech synthesizers for english and sinhala languages,” in 2019 IEEE 2nd International Conference on Information and Computer Technologies (ICICT), 2019, pp. 229–234.
[7] Z. Yin, “An overview of speech synthesis technology,” in 2018 Eighth International Conference on Instrumentation Measurement, Computer, Communication and Control (IMCCC), 2018, pp. 522–526.
[8] M.-J. Hwang, R. Yamamoto, E. Song, and J.-M. Kim, “Tts-by-tts: Tts- driven data augmentation for fast and high-quality speech synthesis,” in ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, pp. 6598–6602.
[9] L. Mathew and V. R. Bindu, “A review of natural language processing techniques for sentiment analysis using pre-trained models,” in 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), 2020, pp. 340–345.
[10] T. P. Nagarhalli, V. Vaze, and N. K. Rana, “Impact of machine learning in natural language processing: A review,” in 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), 2021, pp. 1529–1534.
[11] P. K?osowski, “Deep learning for natural language processing and lan- guage modelling,” in 2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), 2018, pp. 223–228.
[12] L. Safae, B. E. Habib, and T. Abderrahim, “A review of machine learning algorithms for web page classification,” in 2018 IEEE 5th International Congress on Information Science and Technology (CiSt), 2018, pp. 220– 226.
[13] H. Li, Z. Zhang, and Y. Xu, “Web page classification method based on semantics and structure,” in 2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD), 2019, pp. 238–243.
[14] J. Kumar, A. Santhanavijayan, B. Janet, B. Rajendran, and B. Bindhu- madhava, “Phishing website classification and detection using machine learning,” in 2020 International Conference on Computer Communica- tion and Informatics (ICCCI), 2020, pp. 1–6.
[15] S. Lunn, J. Zhu, and M. Ross, “Utilizing web scraping and natural language processing to better inform pedagogical practice,” in 2020 IEEE Frontiers in Education Conference (FIE), 2020, pp. 1–9.
[16] V. Singrodia, A. Mitra, and S. Paul, “A review on web scrapping and its applications,” in 2019 International Conference on Computer Communication and Informatics (ICCCI), 2019, pp. 1–6.
[17] D. M. Thomas and S. Mathur, “Data analysis by web scraping using python,” in 2019 3rd International conference on Electronics, Commu- nication and Aerospace Technology (ICECA), 2019, pp. 450–454.