Semantic-based enhanced logistic regression model with dual mode classification for accurate MCQs generation
*Vijaya Raju MadriCorresponding authorvijayaraju2122@gmail.comDepartment of Computer Science and EngineeringKandula Srinivasa Reddy Memorial College of EngineeringAffiliated to Jawaharlal Nehru Technological UniversityAnanthapuramu, Andhra Pradesh, 516003, IndiaView full profile → , Sreenivasulu Meruvameruva7@gmail.comDepartment of Computer Science and EngineeringKandula Srinivasa Reddy Memorial College of EngineeringAffiliated to Jawaharlal Nehru Technological UniversityAnanthapuramu, Andhra Pradesh, 516003, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 13 Nov 2024
- Published Online:
- 17 Mar 2025
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1913
- Pages:
- 281–289
Abstract
Keywords
Subject Classifications
References
[1] M. Liu, V. Rus and L. Liu, “Automatic Chinese Multiple Choice Question Generation Using Mixed Similarity Strategy,” in IEEE Transactions on Learning Technologies, vol. 11, no. 2, pp. 193-202, 1 April-June (2018), doi: 10.1109/TLT.2017.2679009.
[2] S. K. Hadifar, J. Bitew, C. Deleu, and T. Demeester, “EduQG: A multi-format multiple-choice dataset for the educational domain,” IEEE Access, vol. 11, pp. 20885–20896 (2023), doi: 10.1109/ACCESS.2023.3248790.
[3] D. R. CH and S. K. Saha, “Generation of Multiple-Choice Questions from Textbook Contents of School-Level Subjects,” in IEEE Transactions on Learning Technologies, vol. 16, no. 1, pp. 40-52, 1 Feb. (2023), doi: 10.1109/TLT.2022.3224232.
[4] J. Xie, N. Peng, Y. Cai, T. Wang and Q. Huang, “Diverse Distractor Generation for Constructing High-Quality Multiple-Choice Questions,” in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 30, pp. 280-291 (2022), doi: 10.1109/TASLP.2021.3138706.
[5] M. K. Gupta, P. Dadheech, A. Kumar, S. R. Dogiwal, R. C. Poonia, L. Raja, and D. P. Bhatt, “Detection and localization for watermarking technique using LSB encryption for DICOM Image,” J. Discrete Math. Sci. Cryptogr., vol. 25, no. 1, pp. 193–204, 2022.
[6] M. Larrañaga, I. Aldabe, A. Arruarte, J. A. Elorriaga and M. Maritxalar, “A Qualitative Case Study on the Validation of Automatically Generated Multiple-Choice Questions from Science Textbooks,” in IEEE Transactions on Learning Technologies, vol. 15, no. 3, pp. 338-349, 1 June (2022), doi: 10.1109/TLT.2022.3171589.
[7] M. Aydoğan and A. Karci, “Improving the accuracy using pre-trained word embeddings on deep neural networks for turkish text classification”, Phys. A Stat. Mech. Appl., vol. 541, pp. 123288 (2020).
[8] G. Saini, V. Bhatnagar, S. Seema, L. Raja, S. Sharma, and R. C. Poonia, “Structural equation-based model to investigate the moderating effect of fear of COVID using partial least square method,” J. Interdiscip. Math., vol. 25, no. 3, pp. 703–720, 2022.
[9] Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. R. Salakhutdinov and Q. V. Le, “Xlnet: Generalized autoregressive pretraining for language understanding”, Proc. Adv. Neural Inf. Process. Syst., pp. 5753-5763 (2019).
[10] Y. Yang, D. Cer, A. Ahmad, M. Guo, J. Law, N. Constant, and R. Kurzweil, “Multilingual universal sentence encoder for semantic retrieval,” arXiv:1907.04307, 2019. [Online]. Available: http://arxiv.org/abs/1907.04307.
[11] T. Thongtan and T. Phienthrakul, “Sentiment classification using document embeddings trained with cosine similarity”, Proc. 57th Annu. Meeting Assoc. Comput. Linguistics Student Res. Workshop, pp. 407-414 (2019).




