Abstract
The number of online lectures has increased against the backdrop of the COVID-19 pandemic. With the increase in online lectures, more methods of evaluating their quality and improving lecture styles are being developed. We proposed a method of estimating online lecture quality using the SD-F0 values of students’ response utterances. First, we confirmed the effectiveness of the SD-F0 values of students’ response utterances in estimating students’ understanding of lectures. Through identification experiments using an online lecture video database, the precision rate of “Understanding” was found to be 80.6%. This suggests that when the SD-F0 value was high, with a high probability, the student understood the lecture content. Next, we analyzed the relationship between the SD-F0 values of the students’ utterances and lecture quality. We confirmed that during the first thirty minutes of a lecture, when the SD-F0 value was high, the lecture was considered high-quality. If the SD-F0 values of the 10% or 20% utterances in a lecture exceeded a boundary set by the SVM (the boundary between high- and low-quality lectures), the lecture could be regarded as high-quality. The identification rate was greater than 80%.
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Acknowledgment
I would like to express my gratitude to Mr. Shozo Kamiya, Mr. Kotaro Ando, and Mr. Yasuhiro Nose of IBY, Inc., for providing the online lecture video database and useful advice for this study. This work was also supported by JSPS KAKENHI (grant number 19K04934).
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Takata, T., Wakita, Y. (2022). Estimation of Online Lecture Quality Using Fundamental Frequency Characteristics ExTracted from Student Utterances. In: Zaphiris, P., Ioannou, A. (eds) Learning and Collaboration Technologies. Designing the Learner and Teacher Experience. HCII 2022. Lecture Notes in Computer Science, vol 13328. Springer, Cham. https://doi.org/10.1007/978-3-031-05657-4_22
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DOI: https://doi.org/10.1007/978-3-031-05657-4_22
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