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.
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Chien, YC., Cheng, PY., Csui, LT., Yang, Y., Hooshyar, D., Huang, YM. (2022). Exploring the Relationship Between Learning Achievement and Discussion Records in Remote Maker Activities. In: Huang, YM., Cheng, SC., Barroso, J., Sandnes, F.E. (eds) Innovative Technologies and Learning. ICITL 2022. Lecture Notes in Computer Science, vol 13449. Springer, Cham. https://doi.org/10.1007/978-3-031-15273-3_5
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