Utilization of bio-signals to understand the physiological states of e-learners and improve the learning contents

C.M. Althaff Irfan; Shusaku Nomura; Takaoi Yamagishi; Yoshimasa Kurosawa; Kuniaki Yajima; Katsuko T. Nakahira; Nobuyuki Ogawa; Yoshimi Fukumura

doi:10.1587/transinf.E94.D.1235

Utilization of bio-signals to understand the physiological states of e-learners and improve the learning contents

C.M. Althaff Irfan, Shusaku Nomura, Takaoi Yamagishi, Yoshimasa Kurosawa, Kuniaki Yajima, Katsuko T. Nakahira, Nobuyuki Ogawa, Yoshimi Fukumura

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

Abstract

This paper presents a new dimension in e-learning by collecting and analyzing physiological data during real-world e-learning sessions. Two different content materials, namely Interactive (IM) and Noninteractive (N-IM), were utilized to determine the physiological state of e-learners. Electrocardiogram (ECG) and Skin Conductance Level (SCL) were recorded continuously while learners experienced IM and N-IM for about 25 minutes each. Data from 18 students were collected for analysis. As a result significant difference between IM and N-IM was observed in SCL (p < .01) meanwhile there were no significance in other indices such as heart rate and its variability, and skin conductance response (SCR). This study suggests a new path in understanding e-learners' physiological state with regard to different e-learning materials; the results of this study suggest a clear distinction in physiological states in the context of different learning materials.

Original language	English
Pages (from-to)	1235-1242
Number of pages	8
Journal	IEICE Transactions on Information and Systems
Volume	E94.D
Issue number	6
DOIs	https://doi.org/10.1587/transinf.E94.D.1235
Publication status	Published - 1 Jun 2011

Keywords

e-learning
ECG
heart rate variability
skin conductance level
skin conductance response

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10.1587/transinf.E94.D.1235

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abstract = "This paper presents a new dimension in e-learning by collecting and analyzing physiological data during real-world e-learning sessions. Two different content materials, namely Interactive (IM) and Noninteractive (N-IM), were utilized to determine the physiological state of e-learners. Electrocardiogram (ECG) and Skin Conductance Level (SCL) were recorded continuously while learners experienced IM and N-IM for about 25 minutes each. Data from 18 students were collected for analysis. As a result significant difference between IM and N-IM was observed in SCL (p < .01) meanwhile there were no significance in other indices such as heart rate and its variability, and skin conductance response (SCR). This study suggests a new path in understanding e-learners' physiological state with regard to different e-learning materials; the results of this study suggest a clear distinction in physiological states in the context of different learning materials.",

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author = "Irfan, {C.M. Althaff} and Shusaku Nomura and Takaoi Yamagishi and Yoshimasa Kurosawa and Kuniaki Yajima and Nakahira, {Katsuko T.} and Nobuyuki Ogawa and Yoshimi Fukumura",

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AU - Irfan, C.M. Althaff

AU - Nomura, Shusaku

AU - Yamagishi, Takaoi

AU - Kurosawa, Yoshimasa

AU - Yajima, Kuniaki

AU - Nakahira, Katsuko T.

AU - Ogawa, Nobuyuki

AU - Fukumura, Yoshimi

PY - 2011/6/1

Y1 - 2011/6/1

N2 - This paper presents a new dimension in e-learning by collecting and analyzing physiological data during real-world e-learning sessions. Two different content materials, namely Interactive (IM) and Noninteractive (N-IM), were utilized to determine the physiological state of e-learners. Electrocardiogram (ECG) and Skin Conductance Level (SCL) were recorded continuously while learners experienced IM and N-IM for about 25 minutes each. Data from 18 students were collected for analysis. As a result significant difference between IM and N-IM was observed in SCL (p < .01) meanwhile there were no significance in other indices such as heart rate and its variability, and skin conductance response (SCR). This study suggests a new path in understanding e-learners' physiological state with regard to different e-learning materials; the results of this study suggest a clear distinction in physiological states in the context of different learning materials.

AB - This paper presents a new dimension in e-learning by collecting and analyzing physiological data during real-world e-learning sessions. Two different content materials, namely Interactive (IM) and Noninteractive (N-IM), were utilized to determine the physiological state of e-learners. Electrocardiogram (ECG) and Skin Conductance Level (SCL) were recorded continuously while learners experienced IM and N-IM for about 25 minutes each. Data from 18 students were collected for analysis. As a result significant difference between IM and N-IM was observed in SCL (p < .01) meanwhile there were no significance in other indices such as heart rate and its variability, and skin conductance response (SCR). This study suggests a new path in understanding e-learners' physiological state with regard to different e-learning materials; the results of this study suggest a clear distinction in physiological states in the context of different learning materials.

KW - e-learning

KW - ECG

KW - heart rate variability

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KW - skin conductance response

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Utilization of bio-signals to understand the physiological states of e-learners and improve the learning contents

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