Abstract
Providing personalized support to students during courses is essential to facilitate them in their desired learning goals and reduce the dropout rate. Although teachers can play an effective role in providing personalized support, achieving individual-level assistance for massive courses becomes challenging. To overcome this challenge, this paper proposes a transformer-based approach that first models students’ knowledge of various course concepts based on their performance in various assessed tasks. Afterwards, the students’ concept-wise knowledge level derived from the models is combined with the available course material, leading to the generation of personalized concept-wise exercises by employing fine-tuned Text-to-Text Transfer Transformer (T5) architecture. These generated exercises help students to improve their knowledge about different course concepts. The proposed approach has been evaluated with various university courses to determine its quality, utility and effects on students’ academic performance. The evaluation results revealed that teachers and students were satisfied with the quality of the generated exercises, and these were found to be helpful for students to improve their concept-wise understanding. Furthermore, the generated exercises positively impacted students’ academic performance.
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References
Barria-Pineda, J., Brusilovsky, P.: Explaining educational recommendations through a concept-level knowledge visualization. In: Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion, pp. 103–104 (2019)
Rivers, M.L., Dunlosky, J., Joynes, R.: The contribution of classroom exams to formative evaluation of concept-level knowledge. Contemp. Educ. Psychol. 59, 101806 (2019)
Andrade, H., Brookhart, S.M.: The role of classroom assessment in supporting self-regulated learning. In: Laveault, D., Allal, L. (eds.) Assessment for Learning: Meeting the Challenge of Implementation. TEPA, vol. 4, pp. 293–309. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39211-0_17
Winstone, N., Carless, D.: Designing Effective Feedback Processes in Higher Education: A Learning-focused Approach. Routledge (2019)
Kizilcec, R.F., Pérez-Sanagustín, M., Maldonado, J.J.: Recommending self-regulated learning strategies does not improve performance in a MOOC. In: Proceedings of the Third (2016) ACM Conference on Learning@ Scale, pp. 101–104 (2016)
Huang, Y., He, L.: Automatic generation of short answer questions for reading comprehension assessment. Nat. Lang. Eng. 22(3), 457–489 (2016)
Du, X., Shao, J., Cardie, C.: Learning to ask: neural question generation for reading comprehension. arXiv preprint arXiv:1705.00106 (2017)
Mazidi, K., Tarau, P.: Infusing NLU into automatic question generation. In: Proceedings of the 9th International Natural Language Generation Conference, pp. 51–60 (2016)
Huang, Y., He, L.: Automatic generation of short answer questions for reading comprehension assessment. Nat. Lang. Eng. 22(3), 457–489 (2016)
Matsumoto, Y., Prasad R.: In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers (2016)
Kumar, G., Banchs, R.E., Fernando D’Haro, L.: Revup: automatic gap-fill question generation from educational texts. In: Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications, pp. 154–161 (2015)
Du, X., Cardie, C.: Identifying where to focus in reading comprehension for neural question generation. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 2067–2073 (2017)
Wang, Z., Lan, A.S., Nie, W., Waters, A.E., Grimaldi, P.J., Baraniuk, R.G.: QG-net: a data-driven question generation model for educational content. In: Proceedings of the Fifth Annual ACM Conference on Learning at Scale, pp. 1–10 (2018)
Wang, Z., Lan, A.S., Nie, W., Waters, A.E., Grimaldi, P.J., Baraniuk, R.G.: QG-net: a data-driven question generation model for educational content. In: Proceedings of the Fifth Annual ACM Conference on Learning at Scale, pp. 1–10 (2018)
Kurdi, G., Leo, J., Parsia, B., Sattler, U., Al-Emari, S.: A systematic review of automatic question generation for educational purposes. Int. J. Artif. Intell. Educ. 30(1), 121–204 (2020)
Haladyna, T.M., Rodriguez, M.C.: Developing and Validating Test Items. Routledge (2013)
Liang, C., Yang, X., Dave, N., Wham, D., Pursel, B., Giles, C.L.: Distractor generation for multiple choice questions using learning to rank. In: Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, pp. 284–290 (2018)
Jiang, S., Lee, J.S.Y.: Distractor generation for Chinese fill-in-the-blank items. In: Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pp. 143–148 (2017)
Miller, G.A.: Wordnet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)
Mendes, P.N., Jakob, M., García-Silva, A., Bizer, C.: DBpedia spotlight: shedding light on the web of documents. In: Proceedings of the 7th International Conference on Semantic Systems, pp. 1–8 (2011)
Xue, L., et al.: mT5: a massively multilingual pre-trained text-to-text transformer. arXiv preprint arXiv:2010.11934 (2020)
Rondeau, M.-A., Hazen, T.J.: Systematic error analysis of the stanford question answering dataset. In: Proceedings of the Workshop on Machine Reading for Question Answering, pp. 12–20 (2018)
Lai, G., Xie, Q., Liu, H., Yang, Y., Hovy, E.: Race: large-scale reading comprehension dataset from examinations. arXiv preprint arXiv:1704.04683 (2017)
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Afzaal, M., Nouri, J., Aayesha, A. (2023). A Transformer-Based Approach for the Automatic Generation of Concept-Wise Exercises to Provide Personalized Learning Support to Students. In: Viberg, O., Jivet, I., Muñoz-Merino, P., Perifanou, M., Papathoma, T. (eds) Responsive and Sustainable Educational Futures. EC-TEL 2023. Lecture Notes in Computer Science, vol 14200. Springer, Cham. https://doi.org/10.1007/978-3-031-42682-7_2
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