Abstract
In today’s generation of MOOCs, videos are fundamental to the student learning experience. Due to the prominence of video content in MOOCs, production staff and instructional designers invest significant time and resources in creating these videos. With instructional videos, they want to increase student involvement. Without formative assessment, however, actual engagement is hard to quantify. Nonetheless, a large number of students necessitates a larger pool of questions; to address this issue, we considered mixing machine-learning methods with automatic natural language processing in order to expand the number of questions evaluated and ensure their validity. To accomplish this, we implemented a methodology that generates questions automatically from video transcripts. Following each course comes an evaluation issue, which is typically a multiple-choice question designed to measure a student's comprehension of the video's material. machine-generated questions performed comparably to human-generated questions when it came to judging skill and resemblance. Additionally, the findings indicate that the majority of the questions generated improve e-assessment when the new technology is applied.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Amidei, J., Piwek, P., Willis, A.: Evaluation methodologies in automatic question generation 2013–2018. In: Proceedings of the 11th International Conference on Natural Language Generation, pp. 307–317 (2018). http://oro.open.ac.uk/57517/
Bachiri, Y., Mouncif, H.: Applicable strategy to choose and deploy a MOOC platform with multilingual AQG feature. In: 2020 21st International Arab Conference on Information Technology (ACIT), pp. 1–6 (2020). https://doi.org/10.1109/ACIT50332.2020.9300051
Baker, A.: Active learning with interactive videos: creating student-guided learning materials. J. Libr. Inf. Serv. Dist. Learn. 10(3–4), 79–87 (2016). https://doi.org/10.1080/1533290X.2016.1206776
Dhawaleswar Rao, C.H., Saha, S.K.: Automatic multiple choice question generation from text: a survey. IEEE Trans. Learn. Technol. 13(1), 14–25 (2020). https://doi.org/10.1109/TLT.2018.2889100
Chicco, D., Jurman, G.: The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21(1), 6 (2020). https://doi.org/10.1186/s12864-019-6413-7
Das, B., Majumder, M., Phadikar, S., Sekh, A.A.: Automatic question generation and answer assessment: a survey. Res. Pract. Technol. Enhanc. Learn. 16(1), 1–15 (2021). https://doi.org/10.1186/s41039-021-00151-1
Divate, M., Salgaonkar, A.: Automatic question generation approaches and evaluation techniques. Curr. Sci. 113(9), 1683–1691 (2017)
Dong, L., et al.: Unified language model pre-training for natural language understanding and generation. Adv. Neural Inf. Process. Syst. 32. https://proceedings.neurips.cc/paper/2019/hash/c20bb2d9a50d5ac1f713f8b34d9aac5a-Abstract.html
Filippidou, F., Moussiades, L.: Α benchmarking of IBM, Google and Wit automatic speech recognition systems. In: Maglogiannis, I., Iliadis, L., Pimenidis, E. (eds.) AIAI 2020. IAICT, vol. 583, pp. 73–82. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49161-1_7
Goto, T., Kojiri, T., Watanabe, T., Iwata, T., Yamada, T.: Automatic generation system of multiple-choice cloze questions and its evaluation. Knowl. Manag. E-Learn. 2(3), 210–224 (2010)
Gupta, A., Chugh, D., Anjum, Katarya, R.: Automated news summarization using transformers (2021). arXiv:2108.01064. http://arxiv.org/abs/2108.01064
Hermann, K.M., et al.: Teaching machines to read and comprehend. Adv. Neural Inf. Process. Syst. 28. https://proceedings.neurips.cc/paper/2015/hash/afdec7005cc9f14302cd0474fd0f3c96-Abstract.html
Hoshino, A., Nakagawa, H.: A real-time multiple-choice question generation for language testing: a preliminary study. In: Proceedings of the Second Workshop on Building Educational Applications Using NLP, pp. 17–20 (2005)
Klašnja-Milićević, A., Vesin, B., Ivanović, M., Budimac, Z.: E-Learning personalization based on hybrid recommendation strategy and learning style identification. Comput. Educ. 56(3), 885–899 (2011). https://doi.org/10.1016/j.compedu.2010.11.001
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 (2019). https://doi.org/10.1007/s40593-019-00186-y
Lawson, T.J., Bodle, J.H., Houlette, M.A., Haubner, R.R.: Guiding questions enhance student learning from educational videos. Teach. Psychol. 33(1), 31–33 (2006). https://doi.org/10.1207/s15328023top3301_7
Le, N.-T., Kojiri, T., Pinkwart, N.: Automatic question generation for educational applications–the state of art. In: Advanced Computational Methods for Knowledge Engineering, pp. 325–338. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-00293-4
Lewis, M., et al.: BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv:1910.13461 [Cs, Stat] http://arxiv.org/abs/1910.13461
Liu, D., Lin, C.: Sherlock: a semi-automatic quiz generation system using linked data. In: International Semantic Web Conference (Posters & Demos), pp. 9–12 (2014)
Moos, D.C., Bonde, C.: Flipping the classroom: embedding self-regulated learning prompts in videos. Technol. Knowl. Learn. 21(2), 225–242 (2015). https://doi.org/10.1007/s10758-015-9269-1
Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv:1910.10683 [Cs, Stat]. http://arxiv.org/abs/1910.10683
Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P.: SQuAD: 100,000+ Questions for Machine Comprehension of Text. arXiv:1606.05250 [Cs]. http://arxiv.org/abs/1606.05250
Rey, G.Á., et al. : Semi-automatic generation of quizzes and learning artifacts from linked data. In: Linked Learning 2012: 2nd International Workshop on Learning and Education with the Web of Data, at the World Wide Web Conference 2012 (WWW 2012), Lyon, France (2012). http://lile2012.linkededucation.org/
Trask, A., Michalak, P., Liu, J.: sense2vec—A Fast and Accurate Method for Word Sense Disambiguation In Neural Word Embeddings (2015). arXiv:1511.06388 [Cs]. http://arxiv.org/abs/1511.06388
Wolf, T., et al. : Hugging Face’s transformers: state-of-the-art natural language processing (2020). arXiv:1910.03771 [Cs]. http://arxiv.org/abs/1910.03771
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Bachiri, Ya., Mouncif, H. (2022). Increasing Student Engagement in Lessons and Assessing MOOC Participants Through Artificial Intelligence. In: Fakir, M., Baslam, M., El Ayachi, R. (eds) Business Intelligence. CBI 2022. Lecture Notes in Business Information Processing, vol 449. Springer, Cham. https://doi.org/10.1007/978-3-031-06458-6_11
Download citation
DOI: https://doi.org/10.1007/978-3-031-06458-6_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-06457-9
Online ISBN: 978-3-031-06458-6
eBook Packages: Computer ScienceComputer Science (R0)