Educational data mining and learning analytics: text generators usage effect on students’ grades
Abstract
Today, various types of data are constantly growing, so they can be used for different purposes. In this investigation, educational data has been analyzed to determine the influence of assessment on student knowledge. The newly collected dataset has been prepared and statistically analyzed. The dataset consists of open-question answers collected on one study subject during the midterm exam at Vilnius Gediminas Technical University. The results of the statistical analysis have shown that by using the text generators, students obtained higher grades by paraphrasing the answers to the questions in good quality. Furthermore, research has shown which types of questions are more difficult for students to answer without additional material and using text generation tools. It can be useful for lecturers planning course assessment tasks.
Keyword : educational data mining, learning analytics, statistical analysis, Lithuanian texts, open-questions dataset
How to Cite
Pliuskuvienė, B., Radvilaitė, U., Juodagalvytė, R., Ramanauskaitė, S., & Stefanovič, P. (2024). Educational data mining and learning analytics: text generators usage effect on students’ grades. New Trends in Computer Sciences, 2(1), 19–30. https://doi.org/10.3846/ntcs.2024.21318
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Bouaine, C., Benabbou, F., & Sadgali, I. (2023). Word embedding for high performance cross-language plagiarism detection techniques. International Journal of Interactive Mobile Technologies, 17(10). https://doi.org/10.3991/ijim.v17i10.38891
Chiu, T. K. (2024). Future research recommendations for transforming higher education with generative AI. Computers and Education: Artificial Intelligence, 6, Article 100197. https://doi.org/10.1016/j.caeai.2023.100197
Fanni, S. C., Febi, M., Aghakhanyan, G., & Neri, E. (2023). Natural language processing. In Introduction to Artificial Intelligence (pp. 87–99). Springer International Publishing. https://doi.org/10.1007/978-3-031-25928-9_5
Ferreira‐Mello, R., André, M., Pinheiro, A., Costa, E., & Romero, C. (2019). Text mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(6), Article e1332. https://doi.org/10.1002/widm.1332
Gong, Y., Liu, G., Xue, Y., Li, R., & Meng, L. (2023). A survey on dataset quality in machine learning. Information and Software Technology, 162, Article 107268. https://doi.org/10.1016/j.infsof.2023.107268
Gupta, M. K., & Chandra, P. (2020). A comprehensive survey of data mining. International Journal of Information Technology, 12(4), 1243–1257. https://doi.org/10.1007/s41870-020-00427-7
Hasan, R., Palaniappan, S., Mahmood, S., Abbas, A., Sarker, K. U., & Sattar, M. U. (2020). Predicting student performance in higher educational institutions using video learning analytics and data mining techniques. Applied Sciences, 10(11), Article 3894. https://doi.org/10.3390/app10113894
Hoq, M., Shi, Y., Leinonen, J., Babalola, D., Lynch, C., Price, T., & Akram, B. (2024). Detecting ChatGPT-generated code submissions in a CS1 course using machine learning models. In SIGCSE 2024: Proceedings of the 55th ACM Technical Symposium on Computer Science Education (pp. 526–532). https://doi.org/10.1145/3626252.3630826
Jarrah, A. M., Wardat, Y., & Fidalgo, P. (2023). Using ChatGPT in academic writing is (not) a form of plagiarism: What does the literature say. Online Journal of Communication and Media Technologies, 13(4), Article e202346. https://doi.org/10.30935/ojcmt/13572
Kaggle. (n.d.). Students and chatGPT answers in Lithuanian. Retrieved April 6, 2024, from https://www.kaggle.com/datasets/pavelstefanovi/students-and-ChatGPT-answers-in-lithuanian/
Kapočiūtė-Dzikienė, J., & Salimbajevs, A. (2022). Comparison of deep learning approaches for Lithuanian sentiment analysis. Baltic Journal of Modern Computing, 10(3), 283–294. https://doi.org/10.22364/bjmc.2022.10.3.02
Khaled, F., & Al-Tamimi, M. S. H. (2021). Plagiarism detection methods and tools: An overview. Iraqi Journal of Science, 62(8), 2771–2783. https://doi.org/10.24996/ijs.2021.62.8.30
Khan, A., & Ghosh, S. K. (2021). Student performance analysis and prediction in classroom learning: A review of educational data mining studies. Education and Information Technologies, 26(1), 205–240. https://doi.org/10.1007/s10639-020-10230-3
Márquez, L., Henríquez, V., Chevreux, H., Scheihing, E., & Guerra, J. (2024). Adoption of learning analytics in higher education institutions: A systematic literature review. British Journal of Educational Technology, 55(2), 439–459. https://doi.org/10.1111/bjet.13385
Maulud, D. H., Zeebaree, S. R., Jacksi, K., Sadeeq, M. A. M., & Sharif, K. H. (2021). State of art for semantic analysis of natural language processing. Qubahan Academic Journal, 1(2), 21–28. https://doi.org/10.48161/qaj.v1n2a44
Mercha, E. M., & Benbrahim, H. (2023). Machine learning and deep learning for sentiment analysis across languages: A survey. Neurocomputing, 531, 195–216. https://doi.org/10.1016/j.neucom.2023.02.015
Namoun, A., & Alshanqiti, A. (2020). Predicting student performance using data mining and learning analytics techniques: A systematic literature review. Applied Sciences, 11(1), Article 237. https://doi.org/10.3390/app11010237
Nismi Mol, E. A., & Santosh Kumar, M. B. (2023). Review on knowledge extraction from text and scope in agriculture domain. Artificial Intelligence Review, 56(5), 4403–4445. https://doi.org/10.1007/s10462-022-10239-9
Palanivinayagam, A., El-Bayeh, C. Z., & Damaševičius, R. (2023). Twenty years of machine-learning-based text classification: A systematic review. Algorithms, 16(5), Article 236. https://doi.org/10.3390/a16050236
Rao, Y. S. N., & Chen, C. J. (2024). Bibliometric insights into data mining in education research: A decade in review. Contemporary Educational Technology, 16(2), Article ep502. https://doi.org/10.30935/cedtech/14333
Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), Article e1355. https://doi.org/10.1002/widm.1355
Shaik, T., Tao, X., Dann, C., Xie, H., Li, Y., & Galligan, L. (2023). Sentiment analysis and opinion mining on educational data: A survey. Natural Language Processing Journal, 2, Article 100003. https://doi.org/10.1016/j.nlp.2022.100003
Stefanovič, P., Pliuskuvienė, B., Radvilaitė, U., & Ramanauskaitė, S. (2024). Machine learning model for ChatGPT usage detection in students’ answers to open-ended questions: Case of Lithuanian language. Education and Information Technologies. https://doi.org/10.1007/s10639-024-12589-z
Štrimaitis, R., Stefanovič, P., Ramanauskaitė, S., & Slotkienė, A. (2022). A combined approach for multi-label text data classification. Computational Intelligence and Neuroscience, 2022, Article 3369703. https://doi.org/10.1155/2022/3369703
Tsai, Y. S., Rates, D., Moreno-Marcos, P. M., Muñoz-Merino, P. J., Jivet, I., Scheffel, M., Drachsler, H., Delgado Kloos, C., & Gašević, D. (2020). Learning analytics in European higher education – Trends and barriers. Computers & Education, 155, Article 103933. https://doi.org/10.1016/j.compedu.2020.103933
Yağcı, M. (2022). Educational data mining: Prediction of students’ academic performance using machine learning algorithms. Smart Learning Environments, 9(1), Article 11. https://doi.org/10.1186/s40561-022-00192-z