Generating Student Progress Reports based on Keywords

Shumpei Kobashi, Tsunenori Mine

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a method that automatically generates a student learning status report based on keywords given by instructors at cram schools to reduce their burden on writing the report. For selecting sentences to generate the report, we propose two methods: Seq2Seq-based and Information Retrieval (IR)-based methods. The Seq2Seq-based method uses a Seq2Seq model to generate sentences using keywords given by the instructors. The IR-based method uses OkapiBM25 to select sentences from those written by the instructors based on the keywords. We conducted extensive experiments to evaluate the two methods on a test set of 197,493 sentences. The experimental results show that the Seq2Seq method generates more suitable sentences as the report than the IR-based method. Adding the attention mechanism to the Seq2Seq method further improved the performance of the Seq2Seq method. Considering the above experimental results, we discussed the generation of the lecturer report by keywords.

Original languageEnglish
Title of host publication29th International Conference on Computers in Education Conference, ICCE 2021 - Proceedings
EditorsMaria Mercedes T. Rodrigo, Sridhar Iyer, Antonija Mitrovic, Hercy N. H. Cheng, Dan Kohen-Vacs, Camillia Matuk, Agnieszka Palalas, Ramkumar Rajenran, Kazuhisa Seta, Jingyun Wang
PublisherAsia-Pacific Society for Computers in Education
Pages75-80
Number of pages6
ISBN (Electronic)9789869721479
Publication statusPublished - Nov 22 2021
Event29th International Conference on Computers in Education Conference, ICCE 2021 - Virtual, Online
Duration: Nov 22 2021Nov 26 2021

Publication series

Name29th International Conference on Computers in Education Conference, ICCE 2021 - Proceedings
Volume1

Conference

Conference29th International Conference on Computers in Education Conference, ICCE 2021
CityVirtual, Online
Period11/22/2111/26/21

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Education

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