Exploring the Relationship Between Learning Achievement and Discussion Records in Remote Maker Activities

Yu Cheng Chien, Pei Yu Cheng, Lin Tao Csui, Yeongwook Yang, Danial Hooshyar, Yueh Min Huang

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

Abstract

Maker’s popularity worldwide has led to numerous studies on maker education to enhance learner competitiveness. One important research topic in educational research is how to effectively measure learning performance. Given the rapid development of science and technology, it is now possible to use learner discussion data to measure learning outcomes. We propose using natural language processing (NLP) technology to analyze learner discussion records by using speech-to-text technology to convert audio files into text files and using NLP technology exploring the resultant discussion texts. Experimental results reveal significant relationships between learner discussion and learning effectiveness, participation, and teamwork. This analysis also shows that high-achieving learners often discuss programming-related keywords. The proposed method can be used to analyze learner discussions and thus to measure their learning achievements.

Original languageEnglish
Title of host publicationInnovative Technologies and Learning - 5th International Conference, ICITL 2022, Proceedings
EditorsYueh-Min Huang, Shu-Chen Cheng, João Barroso, Frode Eika Sandnes
PublisherSpringer Science and Business Media Deutschland GmbH
Pages43-51
Number of pages9
ISBN (Print)9783031152726
DOIs
Publication statusPublished - 2022
Event5th International Conference on Innovative Technologies and Learning, ICITL 2022 - Virtual, Online
Duration: 2022 Aug 292022 Aug 31

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13449 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Conference on Innovative Technologies and Learning, ICITL 2022
CityVirtual, Online
Period22-08-2922-08-31

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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