Abstract
In this paper, we present a tool that interleaves lengthy lecture videos with questionnaires at optimal moments. This is done to keep students’ attention by making the video interactive. The student will be presented with MCQ type questions based on the topic covered so far in the video, at regular intervals. The questions are generated based on the transcript of the video lecture using machine learning and natural language processing techniques. In order to have continuity and proper flow of teaching, a LDA-based (Latent Dirichlet Allocation) model has been proposed to insert those generated questions at appropriate points called logical points.
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Attention spans, consumer Insights, Microsoft. http://dl.motamem.org/microsoft-attention-spans-research-report.pdf. Accessed 21 Nov 2019
Hake, R.R.: Interactive-engagement versus traditional methods: a six thousand-student survey of mechanics test data for introductory physics courses. Am. J. Phys. 66, 64 (1998)
Hannun, A., Case, C., Casper, J., et al.: DeepSpeech: scaling up end-to-end speech recognition. In: ArXiv e-prints (2014)
Blei, D.M., Ng, A.Y., Michael, J.I., Lafferty, J.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Heilman, M.: Automatic factual question generation from text, 195. Carnegie Mellon University (2011)
Aldabe, I., Maritxalar, M.: Automatic distractor generation for domain specific texts. In: Loftsson, H., Rögnvaldsson, E., Helgadóttir, S. (eds.) NLP 2010. LNCS (LNAI), vol. 6233, pp. 27–38. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14770-8_5
Crossno, P.J., Wilson, A.T., Shead, T.M., Dunlavy, D.M.: TopicView: visually comparing topic models of text collections. In: 23rd International Conference on Tools with Artificial Intelligence, Boca Raton, FL, pp. 936–943. IEEE (2011)
SQuAD: 100,000+ questions for machine comprehension of text. https://rajpurkar.github.io/SQuAD-explorer. Accessed 21 Nov 2019
Module 1: Recap of C (Lecture 01), NPTEL. https://nptel.ac.in/courses/106105151. Accessed 21 Nov 2019
Introduction to literary history (Week 1), NPTEL. https://nptel.ac.in/courses/109/106/109106124. Accessed 21 Nov 2019
Pennington, J., Socher, R., Manning, R.D.: GloVe: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)
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Skanda, V.C., Jayaram, R., Bukitagar, V.C., Kumar, N.S. (2020). Automatic Questionnaire and Interactive Session Generation from Videos. In: Chandrabose, A., Furbach, U., Ghosh, A., Kumar M., A. (eds) Computational Intelligence in Data Science. ICCIDS 2020. IFIP Advances in Information and Communication Technology, vol 578. Springer, Cham. https://doi.org/10.1007/978-3-030-63467-4_16
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DOI: https://doi.org/10.1007/978-3-030-63467-4_16
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