TY - GEN
T1 - How Does Analysis of Handwritten Notes Provide Better Insights for Learning Behavior?
AU - Li, Boyi
AU - Minematsu, Tsubasa
AU - Taniguchi, Yuta
AU - Okubo, Fumiya
AU - Shimada, Atsushi
N1 - Funding Information:
This work was supported by JST AIP Grant Number JPMJCR19U1, and JSPS KAKENHI Grand Number JP18H04125, Japan.
Publisher Copyright:
© 2022 ACM.
PY - 2022/3/21
Y1 - 2022/3/21
N2 - Handwritten notes are one important component of students' learning process, which is used to record what they have learned in class or tease out knowledge after class for reflection and further strengthen the learning effect. It also helps a lot during review. We hope to divide handwritten notes (Japanese) into different parts, such as text, mathematical expressions, charts, etc., and quantify them to evaluate the condition of the notes and compare them among students. At the same time, data on students' learning behaviors in the course are collected through the online education platform, such as the use time of textbook and attendance, as well as the scores of the online quiz and course grade. In this paper, the analysis of the relationship between the segmentation results of handwritten notes and learning behavior are reported, as well as the research on automatic page segmentation based on deep learning.
AB - Handwritten notes are one important component of students' learning process, which is used to record what they have learned in class or tease out knowledge after class for reflection and further strengthen the learning effect. It also helps a lot during review. We hope to divide handwritten notes (Japanese) into different parts, such as text, mathematical expressions, charts, etc., and quantify them to evaluate the condition of the notes and compare them among students. At the same time, data on students' learning behaviors in the course are collected through the online education platform, such as the use time of textbook and attendance, as well as the scores of the online quiz and course grade. In this paper, the analysis of the relationship between the segmentation results of handwritten notes and learning behavior are reported, as well as the research on automatic page segmentation based on deep learning.
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U2 - 10.1145/3506860.3506915
DO - 10.1145/3506860.3506915
M3 - Conference contribution
AN - SCOPUS:85126210107
T3 - ACM International Conference Proceeding Series
SP - 549
EP - 555
BT - LAK 2022 - Conference Proceedings
PB - Association for Computing Machinery
T2 - 12th International Conference on Learning Analytics and Knowledge: Learning Analytics for Transition, Disruption and Social Change, LAK 2022
Y2 - 21 March 2022 through 25 March 2022
ER -