Abstract
In this chapter, question generation techniques were briefly reviewed, and a question-paragraph mapping task was identified. Then, we described our method to solve the mapping task and the preliminary results. In specific, given a set of questions generated from a semantic network and a list of paragraphs, the mapping task was to map the related paragraphs to each question. To conduct a first step evaluation, two undergraduate students were recruited to make connections between 53 paragraphs and 54 questions. The two students first submitted their works separately and discuss to resolve their conflicts together. The mutual agreement work was treated as the gold standards. By comparing the machine-generated mapping to the human-generated mapping, it showed that the machine-generated mapping (F measure: 0.68) performed as good as human-generated ones (F measure: 0.67/0.66). It implied that the mapping technique could be potentially used to give students the recommendation for further learning materials.
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Acknowledgement
The work was supported in part by the National Natural Science Foundation of China [Grant Number 61807004].
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Zhang, L. (2020). Mapping Machine-Generated Questions to Their Related Paragraphs in the Textbook. In: Pinkwart, N., Liu, S. (eds) Artificial Intelligence Supported Educational Technologies. Advances in Analytics for Learning and Teaching. Springer, Cham. https://doi.org/10.1007/978-3-030-41099-5_14
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