TY - GEN
T1 - Visualization and Analysis for Supporting Teachers Using Clickstream Data and Eye Movement Data
AU - Minematsu, Tsubasa
AU - Shimada, Atsushi
AU - Taniguchi, Rin ichiro
N1 - Funding Information:
supported by JSPS KAKENHI Grant Number
Funding Information:
This work was supported by JSPS KAKENHI Grant Number JP19K20421.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Recently, various educational data such as clickstream data and eye movement data have been collected from students using e-learning systems. Learning analytics-based approaches also have been proposed such as student performance prediction and a monitoring system of student learning behaviors for supporting teachers. In this paper, we introduce our recent work as instances of the use of clickstream data and eye movement data. In our work, the clickstream data is used for representing student learning behaviors, and the eye movement data is used for estimating page areas where the student found difficulty. Besides, we discuss advantages and disadvantages depending on the types of educational data. To discuss them, we investigate a combination of highlights added on pages by students and eye movement data in page difficulty estimation. In the investigation, we evaluate the similarity between positions of highlights and page areas where the student found difficulty generated from eye movements. It is shown that areas in the difficult pages correspond to the highlights in this evaluation. Finally, we discuss how to combine the highlights and eye movement data.
AB - Recently, various educational data such as clickstream data and eye movement data have been collected from students using e-learning systems. Learning analytics-based approaches also have been proposed such as student performance prediction and a monitoring system of student learning behaviors for supporting teachers. In this paper, we introduce our recent work as instances of the use of clickstream data and eye movement data. In our work, the clickstream data is used for representing student learning behaviors, and the eye movement data is used for estimating page areas where the student found difficulty. Besides, we discuss advantages and disadvantages depending on the types of educational data. To discuss them, we investigate a combination of highlights added on pages by students and eye movement data in page difficulty estimation. In the investigation, we evaluate the similarity between positions of highlights and page areas where the student found difficulty generated from eye movements. It is shown that areas in the difficult pages correspond to the highlights in this evaluation. Finally, we discuss how to combine the highlights and eye movement data.
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U2 - 10.1007/978-3-030-50344-4_42
DO - 10.1007/978-3-030-50344-4_42
M3 - Conference contribution
AN - SCOPUS:85088749401
SN - 9783030503437
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 581
EP - 592
BT - Distributed, Ambient and Pervasive Interactions - 8th International Conference, DAPI 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Proceedings
A2 - Streitz, Norbert
A2 - Konomi, Shin’ichi
PB - Springer
T2 - 8th International Conference on Distributed, Ambient and Pervasive Interactions, DAPI 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020
Y2 - 19 July 2020 through 24 July 2020
ER -