Abstract
In this paper, we propose a Machine Learning-based approach to validate suggested learning materials. Learning material validation is an essential part of the learning process, ensuring that learners have access to relevant and accurate information. However, the process of manual validation can be time-consuming and may not be scalable. Traditional learning contents are often only updated or changed in the yearly course revisions. This can be presented with some challenges, especially to courses on emerging subjects and catering to diversified learners, which includes the ability to provide adaptive and updated learning contents to the learners, and the opportunity to continually incorporate feedback. We present a solution and framework that utilizes machine learning algorithms to validate learning materials in an open learning content creation platform. Our approach involves pre-processing the data using Natural Language Processing techniques, creating vectors using TF-IDF and training a Machine Learning model to classify the subject of the learning material. We then calculate the similarity with existing materials for the given course to make sure there is not an existing mate-rial with same content and the new material will add new value. Using an augmented TF-IDF score, we check if the suggested learning materials satisfies the key phrases for the course. We evaluate our approach by comparing the Machine-Learning based approach to manual validation. Not only does the machine-learning based approach reduce the time and effort needed for validation, but it also achieves high accuracy in detecting duplicates and similarity matches.
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References
Ako-Nai, F., de la Cal Marin, E., Tan, Q.: Open learning content creation platform empowered by artificial intelligence and blockchain. In: EDULEARN21 Proceedings, vol. 1, pp. 2504–2513 (2021). https://doi.org/10.21125/EDULEARN.2021.0546
Ako-Nai, F., de la Cal Marin, E., Tan, Q.: Artificial intelligence decision and validation powered smart contract for open learning content creation. In: Prieto, J., Partida, A., Leitão, P., Pinto, A. (eds.) Blockchain and Applications. BLOCKCHAIN 2021. Lecture Notes in Networks and Systems. https://doi.org/10.1007/978-3-030-86162-9_37/COVER
F. Ako-Nai, Q. Tan, and E. A. de la Cal Marin, ‘Employing Blockchain Technology in Instructional Design and Learning Content Creation’, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11937 LNCS, pp. 581–588, 2019 https://doi.org/10.1007/978-3-030-35343-8_61
Al Asaad, B., Erascu, M.: A tool for fake news detection. In: 2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), pp. 379–386 (2018). https://doi.org/10.1109/SYNASC.2018.00064
Bhowmik, P., Sohrawordi, M., Ehsan Ali, M., Hasan, M.N., Roy, P.K.: Analysis of social media data to classify and detect frequent issues using machine learning approach. In: 2020 2nd International Conference on Advanced Information and Communication Technology (ICAICT), pp. 394–399. IEEE (2020). https://doi.org/10.1109/ICAICT51780.2020.9333452
Amalia, A., Gunawan, D., Lydia, M.S., Wesley: The identification of negative content in websites by using machine learning approaches. In: 2019 5th International Conference on Computing Engineering and Design (ICCED), pp. 1–6 (2019). https://doi.org/10.1109/ICCED46541.2019.9161105
Al Asaad, B., Erascu, M.: A tool for fake news detection. In: 2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), pp. 379–386. IEEE (2018). https://doi.org/10.1109/SYNASC.2018.00064
Shinde, S., Joeg, P., Vanjale, S.: Web document classification using support vector machine. In: 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC), pp. 688–691 (2017). https://doi.org/10.1109/CTCEEC.2017.8455102
Khan, A., Baharudin, B., Lee, L.H., Khan, K.: A review of machine learning algorithms for text-documents classification. J. Adv. Inform. Technol. 1(1), 4–20 (2010)
Cong, X., Li, L.: UGC quality evaluation based on meta-learning and content feature analysis. In: Proceedings of 2016 5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016, pp. 495–499 (2017). https://doi.org/10.1109/ICNIDC.2016.7974624
Mazari, A.C., Djeffal, A.: Deep learning-based sentiment analysis of algerian dialect during Hirak 2019. In: 2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH), pp. 233–236. IEEE (2021)https://doi.org/10.1109/IHSH51661.2021.9378753
Wang, X., Ning, H.: TF-IDF keyword extraction method combining context and semantic classification. In: ACM International Conference Proceeding Series (2020).https://doi.org/10.1145/3414274.3414492
Wang, J., Xu, W., Yan, W., Li, C.: Text similarity calculation method based on hybrid model of LDA and TF-IDF. In: ACM International Conference Proceeding Series, pp. 1–8 (2019). https://doi.org/10.1145/3374587.3374590
Liu, C.Z., Sheng, Y.X., Wei, Z.Q., Yang, Y.Q.: Research of text classification based on improved TF-IDF algorithm. In: 2018 IEEE International Conference of Intelligent Robotic and Control Engineering, IRCE 2018, pp. 69–73 (2018)https://doi.org/10.1109/IRCE.2018.8492945
Agrawal, R., Arunachalam, A.S.: K-nearest neighbor for uncertain data related papers terrorist group prediction using data classification SDIWC organization analysis of distance measures using K-Nearest neighbor algorithm on KDD dataset K-Nearest neighbor for uncertain data. Int. J. Comput. Appl. 105(11), 975–8887 (2014)
Ghosh, S., Desarkar, M.S.: Class specific TF-IDF boosting for short-text classification: application to short-texts generated during disasters. In: The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018, pp. 1629–1637 (2018)https://doi.org/10.1145/3184558.3191621
Salton, G., Yu, C.T.: On the construction of effective vocabularies for information retrieval. ACM SIGIR Forum 9(3), 48–60 (1973). https://doi.org/10.1145/951761.951766
Acknowledgment
The research has been funded by the Spanish Ministry of Economics and Industry, grant PID2020-112726RB-I00, by the Spanish Research Agency (AEI, Spain) under grant agreement RED2018-102312-T (IA-Biomed), and by the Ministry of Science and Innovation under CERVERA Excellence Network project CER-20211003 (IBERUS) and Missions Science and Innovation project MIG-20211008 (INMERBOT). Also, by Principado de Asturias, grant SV-PA-21-AYUD/2021/50994. By European Union’s Horizon 2020 research and innovation programme (project DIH4CPS) under the Grant Agreement no 872548. And by CDTI (Centro para el Desarrollo Tecnológico Industrial) under projects CER-20211003 and CER-20211022 and by ICE (Junta de Castilla y León) under project CCTT3/20/BU/0002.
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Ako-Nai, F., de la Cal Marin, E., Tan, Q. (2023). A Machine-Learning Based Approach to Validating Learning Materials. In: García Bringas, P., et al. International Joint Conference 16th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2023) 14th International Conference on EUropean Transnational Education (ICEUTE 2023). CISIS ICEUTE 2023 2023. Lecture Notes in Networks and Systems, vol 748. Springer, Cham. https://doi.org/10.1007/978-3-031-42519-6_29
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