Abstract
Teaching necessitates a method of determining if learners are gaining the desired knowledge and skills. We believe that chatbot technology would be an excellent solution to this problem. Using our approach, the chatbot will assess the student by using questions and answers recorded in a question bank. Four approaches were taken to assess the students’ answers against the model answer, using models such as Word2Vec, all-mpnet-base-v2, and Sense2Vec models with Word Mover’s Distance algorithm or Cosine Similarity. After evaluating these approaches, the best performing approach was when we used the Sense2Vec model with Cosine Similarity which gave the most accurate similarity score range for correct and incorrect answers.
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References
Alharbi, K., Cristea, A.I., Shi, L., Tymms, P., Brown, C.: Agent-based classroom environment simulation: the effect of disruptive schoolchildren’s behaviour versus teacher control over neighbours. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds.) AIED 2021. LNCS (LNAI), vol. 12749, pp. 48–53. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-78270-2_8
Sandu, N., Gide, E.: Adoption of AI-chatbots to enhance student learning experience in higher education in India. In: 2019 18th International Conference on Information Technology Based Higher Education and Training (ITHET). IEEE, Magdeburg (2019)
Catania, F., Spitale, M., Cosentino, G., Garzotto, F.: Conversational agents to promote children’s verbal communication skills. In: Følstad, A., et al. (eds.) CONVERSATIONS 2020. LNCS, vol. 12604, pp. 158–172. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68288-0_11
Virvou, M., Tsiriga, V.: Web passive voice tutor: an intelligent computer assisted language learning system over the WWW. In: Proceedings IEEE International Conference on Advanced Learning Technologies. IEEE, Madison (2001)
Sitikhu, P., Pahi, K., Thapa, P., Shakya, S.: A comparison of semantic similarity methods for maximum human interpretability. In: 2019 Artificial Intelligence for Transforming Business and Society (AITB). IEEE, Kathmandu (2019)
Olowolayemo, A., Nawi, S., Mantoro, T.: Short answer scoring in English grammar using text similarity measurement. In: 2018 International Conference on Computing, Engineering, and Design (ICCED). IEEE, Bangkok (2018)
Wallace, E., Wang, Y., Li, S., Singh, S., Gardner, M.: Do NLP models know numbers? Probing numeracy in embeddings. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (2019)
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Weerasinghe, E., Kotuwegedara, T., Amarasena, R., Jayasinghe, P., Manathunga, K. (2022). Dynamic Conversational Chatbot for Assessing Primary Students. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham. https://doi.org/10.1007/978-3-031-11647-6_89
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DOI: https://doi.org/10.1007/978-3-031-11647-6_89
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