Keywords: distance learning, ranking chatbot, natural language dialogue, embedding, soft testing, sentence similarity measure
Application of natural language dialogue in distance learning
UDC 004.912
DOI: 10.26102/2310-6018/2024.45.2.042
The relevance of the study is due to the low level of use of dialogue in natural language in distance learning. The creation of such tools based on artificial intelligence will make the process of distance learning more accessible and attractive. The article proposes to build a dialogue based on standard questions for the content of the distance learning course. The answer is selected based on the similarity of the user's question to the standard. It is recommended to use the structural units of the distance learning course as a set of answers, and the corresponding headings as standard questions. The training dialogue data is remembered and used to expand the list of standard questions and train the system. To control learning, a measure of the similarity of the student’s answers to test questions and the correct answer options is used. To generate test questions, you can use distance learning dictionaries and test tasks. It is proposed to determine the measure of similarity of two texts using the cosine of the embeddings of the closest terms. Data from comparing texts using the proposed methodology confirm its ability to correctly assess the similarity of texts and justify its use for organizing dialogue in natural language in distance learning.
1. Drandrov D.A., Drandrov G.L. Pros and cons of distance learning. Sovremennye problemy nauki i obrazovaniya = Modern problems of science and education. 2022;(3). (In Russ.). https://doi.org/10.17513/spno.31756
2. Kiuru K.V., Popova E.E., Makovetskaya Ju.G. New technologies of distance learning in the system of higher and additional professional education. Problemy sovremennogo pedagogicheskogo obrazovaniya = Problems of modern pedagogical education. 2022;(75-3):196–199. (In Russ.).
3. Ibna Riza A.N., Hidayah I., Santosa P.I. Use of Chatbots in E-Learning Context: A Systematic Review. In: 2023 IEEE World AI IoT Congress (AIIoT), 07-10 June 2023, Seattle, WA, USA. IEEE; 2023. P. 0819–0824. https://doi.org/10.1109/AIIoT58121.2023.10174319
4. Kumar J.A. Educational chatbots for project-based learning: investigating learning outcomes for a team-based design course. International Journal of Educational Technology in Higher Education. 2021;18(1). https://doi.org/10.1186/s41239-021-00302-w
5. Labadze L., Grigolia M., Machaidze L. Role of AI chatbots in education: systematic literature review. International Journal of Educational Technology in Higher Education. 2023;20(1). https://doi.org/10.1186/s41239-023-00426-1
6. Goryachkin B.S., Galichii D.A., Tsapii V.S., Burashnikov V.V., Krutov T.Yu. Effektivnost' ispol'zovaniya chat-botov v obrazovatel'nom protsesse. E-Scio. 2021;(4):529–551. (In Russ.).
7. Rozhkin P.A., Nekhaev I.N., Markin K.A. Designing AI teacher assistant on online-course based on word2vec technology. International Journal of Advanced Studies. 2018;8(1):106–128. (In Russ.). https://doi.org/10.12731/2227-930X-2018-1-106-128
8. Bengfort B., Bilbro R., Ojeda T. Applied Text Analysis with Python. Enabling Language-Aware Data Products with Machine Learning. Saint Petersburg: Piter; 2019. 368 p. (In Russ.).
9. Parhomenko P.A., Grigorev A.A., Astrakhantsev N.A. A survey and an experimental comparison of methods for text clustering: application to scientific articles. Trudy Instituta sistemnogo programmirovaniya RAN = Proceedings of the Institute for System Programming of the RAS. 2017;29(2):161–200. (In Russ.). https://doi.org/10.15514/ISPRAS-2017-29(2)-6
10. Yuferev V.I., Razin N.A. Word-embedding Based Text Vectorization Using Clustering. Modelirovanie i analiz informatsionnykh sistem = Modeling and Analysis of Information Systems. 2021;28(3):292–311. (In Russ.). https://doi.org/10.18255/1818-1015-2021-3-292-311
11. Mikolov T., Chen K., Corrado G., Dean J. Efficient Estimation of Word Representations in Vector Space. URL: https://arxiv.org/abs/1301.3781 [Accessed 24th March 2024].
12. Ataeva O.M., Serebriakov V.A., Tuchkova N.P. On the synonym search model. Elektronnye biblioteki = Russian Digital Libraries Journal. 2021;24(6):1006–1022. (In Russ.).
13. Morev I.A. Obrazovatel'nye informatsionnye tekhnologii. Chast' 2. Pedagogicheskie izmereniya. Vladivostok: Izdatel'stvo Dal'nevostochnogo universiteta; 2004. 174 p. (In Russ.).
14. Codd E.F. et al. RENDEZVOUS Version 1: An Experimental English Language Query Formulation System for Casual Users of Relational Data Bases. IBM Research Report. 1978;RJ2144.
Keywords: distance learning, ranking chatbot, natural language dialogue, embedding, soft testing, sentence similarity measure
For citation: Bratischenko V.V. Application of natural language dialogue in distance learning. Modeling, Optimization and Information Technology. 2024;12(2). URL: https://moitvivt.ru/ru/journal/pdf?id=1589 DOI: 10.26102/2310-6018/2024.45.2.042 (In Russ).
Received 29.05.2024
Revised 30.05.2024
Accepted 05.06.2024
Published 30.06.2024