Regression Analyses between Physiological Indexes and Level of

Understanding with VAS of a Listening Task

Masaki Omata

and Kazuya Nakazawa

Interdisciplinary Graduate School, University of Yamanashi, Kofu, Japan

Department of Computer Science and Engineering, University of Yamanashi, Kofu, Japan

Keywords: Level of Understanding, Physiological Index, Regression Analysis and Visual Analog Scale.

Abstract: This paper describes logistic regression analyses and multiple regression analyses to explain relationships

between physiological signals and subjective self-reported levels of understanding of a second language

listening task by using a visual analog scale (VAS) of 999 degrees. Mean contribution ratio of the logistic

regression expressions was 0.72, and mean contribution ratio of the multiple regression expressions was 0.70.

Power value of theta band of brain waves has a certain tendency to change according to the level of

understanding. Accuracy of the regression expressions using VAS was the same or more than that of the four-

level scale as our previous work.

1 INTRODUCTION

It is crucial to measure level of understanding as a

metric of learning achievement, and this metric is

used for self-reflective learning and also to develop

heuristic guidelines to improve instruction methods

and select appropriate materials. Although

conventional quizzes and questionnaires are common

methods for measuring the level of understanding,

there are two major problems with such methods.

First, these methods can impose the burden of

answering a question even when the learner does not

require this process because they already possess an

effective level of understanding. Second, the methods

interrupt learner’s activity even when the learner

wants to continue concentrating on that activity.

Several studies have reported that changes in

physiological signals reflect changes in the levels of

understanding of a learning task. For a verbal task,

some results indicate that the power values of alpha

and beta brain waves change relative to differences of

in the difficulty level of the text in a reading task, e.g.,

brain blood flow increases when reading text in a

secondary language compared to reading in the

primary language, skin conductance response

increases with greater English proficiency, and the

power value of alpha waves during reading English

content words is reduced compared to reading

functional words.

We have previously proposed using physiological

signals to validate the relationships between the

signals and a learner’s level of understanding, and we

have developed multiple regression expressions to

estimate understanding level (Omata, 2018). Using

such signals allows us to observe the state of a learner

without imposing a burden and interrupting activities;

thus, the estimation can represent a solution to the

aforementioned problems. Our multiple regression

expressions could estimate the level of individual

understanding while reading each second-language

sentence as at least 55% and at maximum 81% on a

four-level ordinal scale.

In this paper, we propose using a logistic

regression model to achieve a higher-accuracy

regression method. In addition, we propose the use of

a visual analog scale (VAS) as a rating scale for high-

resolution estimation. Previously, we reported that

the contribution rate of a logistic regression model

was greater than that of a multiple regression model

when estimating emotion from physiological signals

in a subject drawing a picture (Omata, 2014).

Watanabe et al. and Yamada et al. have reported that

a VAS method was more effective than Likert-scale

evaluations (Watanabe 2015, Yamada 2014).

This paper describes an experiment conducted to

examine the relationships between physiological

signals and self-reported levels of understanding of a

second-language listening task using a VAS. In

addition, this paper compares a logistic regression

Omata, M. and Nakazawa, K.

Regression Analyses between Physiological Indexes and Level of Understanding with VAS of a Listening Task.

DOI: 10.5220/0006892200210029

In Proceedings of the 5th International Conference on Physiological Computing Systems (PhyCS 2018), pages 21-29

ISBN: 978-989-758-329-2

model to a multiple regression model relative to

contribution ratio to further explain these

relationships.

The contributions of this paper are summarized as

follows.

 We demonstrate that physiological signals,

particularly the power of theta brain waves, are

related to levels of understanding in a listening

task.

 VAS is more effective to record a detailed self-

reported level of understanding than a four-

level scale.

 We demonstrate that the accuracy of a logistic

regression model is similar to that of a multiple

regression model relative to regression

analyses of such relationships.

2 RELATED WORK

This section surveys previous work that studied

relationship between person’s responses and their

physiological signals during a verbal task or e-

learning.

Concerning signals of central nervous system

(CNS), Mellem et al. focused on the role of

oscillatory EEG dynamics in retrieving open class

vs. closed class words (Mellem, 2012). Specifically,

they investigated the robustness of the theta and

alpha effects in different contexts and in a different

population and performed time–frequency analysis

based on the context or class of the word. As the

results, they observed larger power decreases in the

alpha waves for the open class compared to closed

class words and did not observe differences in theta

power between these conditions. Safi et al. studied

to validate a protocol using near-infrared

spectroscopy (fNIRS) for the assessment of overt

reading of irregular words and nonwords with a full

coverage of the cerebral regions (Safi, 2012). The

results reported that total hemoglobin

concentrations were significantly higher than

baseline for both irregular word and nonword

reading. Oishi et al. compared brain blood flow

during a listening task for first language with that for

second language (Oishi, 2008). The results reported

that the hemoglobin concentration of the second

language significantly increased than that of the first

language. They interpreted that the second language

needed attentional capacity more than the first

language.

Concerning signals of peripheral nervous system

(PNS), Harris investigated the phenomenon

psychophysiologically, 32 Turkish–English

bilinguals rated a variety of stimuli for pleasantness

in Turkish (L1) and English (L2) by analyzing skin

conductance via fingertip electrodes (Harris, 2003).

The results show that the participants demonstrated

greater autonomic arousal to taboo words and

childhood reprimands in their L1 compared to their

L2. Nomura et al. focused on the skin temperature

changes and Electrocardiogram (ECG) changes of

students engaged in two e-learning exercises:

interactive or non-interactive (Nomura, 2012). As

the result, the skin temperature showed significant

decline when subjects were engaged in the

interactive exercise. In addition, high frequency

(0.15-0.40 Hz) values of ECG dropped just after the

start of both exercises and remained low.

We conducted multiple regression analyses to

investigate a trend of the relations between several

physiological signals and level of understanding of

second language reading of a whole text (Omata,

2016). The contribution ratio was 0.82. After that,

we developed individual adaptive estimation

expressions to estimate a learner’s level of

understanding (four-level scale) of reading each

second-language sentence by analyzing the learner’s

physiological signals (Omata, 2018). The mean

determination coefficients of the expressions was

0.69 ranged from 0.55 to 0.81.

3 PHYSIOLOGICAL SIGNALS

AND INDEXES

Tables 1 and 2 list physiological signals and indexes

calculated from each kind of signals, which were

analyzed in the experiment. This section introduces

physiological signals obtained from participants

from two parts: central nervous system (CNS) and

peripheral nervous system (PNS); and also explains

the indexes and their pre-processing before

analyzing them.

We used the ProComp Infiniti (Thought

Technology Ltd.) for the measurement which is an

eight channel, biofeedback and neurofeedback

system for real-time data acquisition. The channels

can be used with some combination of sensors

described below.

PhyCS 2018 - 5th International Conference on Physiological Computing Systems

Table 1: Physiological signals and indexes of CNS.

Type of

signal

Calculated index

Method of

calculating

EEG

Power of theta waves

(4 Hz - 7 Hz)

Spectral analysis

of brain waves

on a forehead

Power of alpha1 waves

(7 Hz - 8 Hz)

Power of alpha2 waves

(9 Hz - 11 Hz)

Power of alpha3 waves

(12 Hz - 13 Hz)

Power of beta waves

(14 Hz - 30 Hz)

HEG

HEG ratio based on

baseline

Absorptive

power of

oxygenated

haemoglobin on

a frontal lobe

Table 2: Physiological signals and indexes of PNS.

Type of

signal

Calculated index

Method of

calculating

BVP

Power of HF waves

Spectral analysis of

BVPs on a left

thumb

LF/HF ratio

Pulse rate

(beats / minute)

HEG

SC ratio based on

baseline

Electrical

conductance

between left index

and middle fingers

RESP Breaths / minute

Expansion and

contraction motion

of abdomen

3.1 CNS Signals

3.1.1 Electroencephalogram (EEG)

EEG signals are detected on the scalp as neuronal

electric fluctuations in the brain and are classified by

their frequency: theta (4–7 Hz), alpha (8–13 Hz), and

beta (14–30 Hz). Alpha waves are frequently detected

when a person is relaxed, beta waves are commonly

detected when a person is concentrating, and theta

waves are detected when a person is feeling drowsy.

Alpha waves are further classified into alpha1 (7–8

Hz), alpha2 (9–11 Hz), alpha3 (12–13 Hz). We

recorded these data using an EEG-Z sensor (Thought

Technology Ltd.), classified the frequencies and

calculated the power of each frequency band (see

Table 1). Figure 1a shows such electrodes on a

participant’s forehead (F3 of the international 10–20

system).

(a) (b)

(e)

Figure 1: Sensors and electrodes for measuring

physiological signals: (a) electrodes for EEG, (b) a HEG

headband, (c) a BVP sensor, (d) a SC sensor and (e) a RESP

sensor.

3.1.2 Hemoencephalography (HEG)

The HEG ratio is the ratio between oxygen-rich

hemoglobin and oxygen-starved hemoglobin in

cerebral blood flow (CBF), which correlates with

blood flow dynamics and cellular metabolism in

localized parts of the brain cortex. The level of

oxygenated hemoglobin increases during brain

activation due to the phenomenon of neurovascular

coupling. Therefore, we subcutaneously recorded the

ratio at 2 cm on the forehead by measuring the

absorptive power of near-infrared and red light of the

CBF because of the differences of the absorptive

power of oxygen-rich and oxygen-starved

hemoglobin. The HEG ratio is calculated as follows

 = 200





(1)

where RED is the absorptive powers of oxygenated

hemoglobin and reduced hemoglobin during visible

red light irradiation, and IR is their absorptive powers

during near-infrared light irradiation. We used a near-

infrared headband (MediTECH Electronic GmbH;

see Figure1b) on the forehead (Fp2 of the

international 10–20 system) to measure HEG ratio.

Regression Analyses between Physiological Indexes and Level of Understanding with VAS of a Listening Task

3.2 PNS Signals

3.2.1 Blood Volume Pulse (BVP)

BVP, also called photoplethysmography, is a relative

measure of heart rate and inter-beat interval and an

index of the coarctation and angiectasis of peripheral

blood vessels. The frequency characteristics of such

waves reflect the tone of sympathetic and

parasympathetic nerves. The high-frequency (HF,

0.15–0.4 Hz) component reflects the tone of

sympathetic nerves, while the low-frequency (LF,

0.04–0.15 Hz) component reflects the tone of both

types of nerves. We used a BVP sensor (Thought

Technology Ltd.; see Figure 1c), which bounces

infrared light against a skin surface and measures the

amount of reflected light.

3.2.2 Skin Conductance (SC)

A hand’s SC is a measure of the skin’s ability to

conduct electricity due to the eccrine sweat between

two fingers. SC represents changes in the sympathetic

nervous system. The value of the sensor increases

when the subject is in an excitatory state or a stressful

situation. We used an SC sensor (Thought

Technology Ltd.; see Figure 1d) that measures SC

between two fingers in micro-Siemens.

3.2.3 Respiration (RESP)

Respiratory movement consists of inspiration and

expiration. The RESP rate is an index of emotion with

an increase indicating tension and a decrease

indicating relaxation. We used a RESP sensor

(Thought Technology Ltd.; see Figure 1e) that is

sensitive to stretching. When strapped around a

participant’s abdomen, it converts the respiratory

movement into an electric signal. We also calculate

the RESP rate from the data.

3.3 Pre-processing

Note that pre-processing was required for eliminating

individual differences among participants, thus the

data of physiological indexes were standardized using

both experimental data and neutral data prior to

performing experimental tasks. The indexes of EEG,

HEG ratios and SC were calculated from the mean

and standard deviation of the same type of data by

following the equation:

=

−



(2)

where x is the raw data calculated from the signals

obtained from a sensor, and μ and σ are the mean and

standard deviation of the same kind of data,

respectively, recorded before performing an

experimental task in the neutral state as a baseline for

each participant. The RESP and BVP indexes were

standardized using the relative ratios obtained from

the experimental and neutral data.

4 EXPERIMENT

This section describes a verification experiment to

model the relationships between physiological

indexes and participants’ subjective self-reported

levels of understanding when listening to English

speech, which was a second language for the

participants.

4.1 Objective

The main objective of the experiment was to build a

regression expression to explain a learner’s level of

understanding more accurately than our previous

model by verifying the relationships between

understanding levels and physiological indexes. In

addition, a second objective was to evaluate the effect

of using a VAS as a rating scale by comparing it to

the four-level ordinal scale used in our previous work.

Therefore, our hypotheses are as follows.

H1: There is a significant relationship between the

level of understanding of a second-language

listening task and the listener’s physiological

indexes.

H2: The accuracy of a logistic regression model is

significantly greater than that of a multiple

regression model relative to a contribution ratio

comparison.

H3: A high-resolution VAS is more useful than a

low-resolution four-level scale for regression

analyses.

4.2 Experimental Setup

Figure 2 shows the experimental equipment and a

participant wearing the sensors used to measure

physiological signals during a task. A laptop with a

17.3-inch display was used to measure and record

data, and two speakers were placed approximately 30

cm from the participant. The participant’s left hand

was kept on a table and restrained from moving due

to sensors attached to the fingers.

PhyCS 2018 - 5th International Conference on Physiological Computing Systems

A slider (Phidgets Inc. Slider 60; Figure 3) for the

VAS was placed near the participant’s right hand. The

participant reported their subjective self-reported

level of understanding by moving the knob of the

slider along the sulcus. The resolution of the slider

was 999 degrees (0 to 998) within a 60 mm sulcus.

The degrees represent “Not at all understood” (0) side

to “fully understood” (998). The middle position

represents “neutral” degrees and was the default

position before moving. The cardboard around the

equipment was used to unify the participant’s sight by

obscuring visual information in order to allow the

participants to concentrate on the listening task.

Figure 2: Experimental equipment and a participant

wearing sensors.

Figure 3: Slider to report level of understanding.

4.3 Task

The task was to listen to English speech and report the

level of understanding of the content. In each trial, the

participant first clicked the “play” button on the

laptop’s screen. Then, they listened to English speech

played from the speakers. They then reported their

level of understanding of the content by moving the

slider knob and clicking the “record” button to fix and

record the answer. Finally, they moved the knob back

to the default start position prior to the next trial. The

participant then repeated this process with another

speech.

4.4 Stimuli

The speeches used for the listening task were

excerpted from the Grade 1, Grade Pre-1, Grade 2,

Grade Pre-2, Grade 3, and Grade 4 EIKEN tests

(Eiken Foundation of Japan, online), which is one of

the most widely used English-language testing

programs in Japan. The speeches ranged in duration

from 15 to 20 s and represented the above six

difficulty grades. Each difficulty grade included ten

speeches.

4.5 Participant

Ten male college students aged 21 to 23 participated

in this experiment. All participants were native

Japanese speakers who had studied English for over a

decade. Their TOEIC (IIBC, online) listening scores

ranged from 235 to 385 (mean score: 281.5) out of

495. Each participant was assigned a distinguishing

ID character from A to J to facilitate explaining the

results and analyses.

4.6 Procedure

A block of each difficulty grade comprised two parts:

(1) recording physiological signals during rest and (2)

performing a task with ten listening trials for each

grade.

In the rest part, all physiological signals of a

participant were measured and recorded for 60 s

while the participant rested, opened their eyes, and

thought of nothing.

Then, in the task performance part, the participant

listened to the speeches (ten voices) of each grade and

self-reported a subjective level of understanding for

each trial.

All participants conducted six blocks for all six

grades in a within-subject design. Therefore, each

participant listened to 60 speeches. Note that the order

of the blocks for each participant was

counterbalanced; thus, the order of grades differed for

each participant.

In addition, we obtained informed consent from

all participants prior to conducting the experiment.

4.7 Results

Figure 4 shows results of the self-reported level of

understanding of each participant for Grade 4, i.e., the

easiest grade, and Figure 5 shows the results of the

self-reported level of understanding of each

participant for Grade 1, i.e., the most difficult grade.

Fully understood

Start position

Not at all understood

Regression Analyses between Physiological Indexes and Level of Understanding with VAS of a Listening Task

Although the detailed results of other grades are

omitted in this paper, we observed that, overall, more

difficult grades resulted in lower understanding

levels. In addition, because the levels of

understanding were fully distributed, and several

values were 999 degrees on the slider, the self-

reported values and physiological indexes were

useful for analyses.

Figure 4: Distribution of self-reported levels of

understanding of each participant of Grade 4.

Figure 5: Distribution of self-reported levels of under-

standing of each participant of Grade 1.

4.8 Analyses

This section describes statistical analyses conducted

to test our hypotheses (Section 4.1).

4.8.1 Correlation Analyses

Tables 3 and 4 show the correlation coefficients

between the self-reported level of understanding

values of each participant and the physiological index

values of CNS (Table 3) and PNS (Table 4). Overall,

the sign of the coefficients of the power values of each

band of brain waves was negative. We observed that

the power values of theta band brain waves correlate

inversely with the self-reported values. The absolute

values of the coefficients of participants I and J of the

theta band were greater than 0.75.

Table 3: Correlation coefficients between level of

understanding and CNS indexes of each participant.

ID Power

of 

Power

of 



Power

of 



Power

of 



Power

of 

HEG

A -0.45 -0.38 -0.25 -0.24 -0.29 0.33

B -0.52 -0.51 -0.51 -0.51 -0.55 -0.35

C -0.57 -0.31 -0.29 -0.24 -0.28 -0.27

D -0.58 -0.58 -0.59 -0.59 -0.61 -0.12

E -0.35 -0.24 -0.41 -0.26 -0.44 0.22

F -0.56 -0.33 -0.42 -0.43 -0.45 0.02

G -0.37 -0.51 -0.47 -0.49 -0.49 -0.29

H -0.44 -0.44 -0.36 -0.40 -0.45 -0.02

I -0.77 -0.52 -0.50 -0.52 -0.59 0.25

J -0.75 -0.71 -0.59 -0.51 -0.63 0.19

Table 4: Correlation coefficients between level of

understanding and PNS indexes of each participant.

ID Power

of HF

LF/HF Pulse

rate

SC ratio Breaths

/ minute

A -0.14 -0.29 -0.03 0.26 -0.25

B -0.40 0.17 -0.09 0.31 0.11

C -0.31 -0.11 -0.46 0.10 -0.21

D -0.46 0.30 0.13 -0.66 0.21

E 0.08 -0.34 -0.58 0.10 -0.10

F -0.62 0.39 0.56 0.25 -0.33

G -0.39 0.19 0.19 -0.61 0.30

H 0.02 -0.07 -0.20 -0.10 -0.27

I -0.31 -0.02 0.22 0.41 0.22

J -0.15 0.30 -0.17 -0.08 -0.25

Figure 6 illustrates the relationships between the

power values of the theta waves of participant J and

his self-reported levels of understanding as a high

correlation example. As can be seen, a higher level of

understanding, which means the level moves toward

“fully understood,” is correlated with lower theta

wave power. Figure 7 illustrates the power value of

the theta waves of each trial for each grade of

participant J. The graph shows that the power value

of theta waves varies relative to the difficulty of the

grade from easy (Grade 4) to difficult (Grade 1).

Figure 6: Correlation between level of understanding and

power of theta of participant J.

200

400

600

800

1000

ABCDEFGH I J

Self-reprted level of

understanding

Participant's ID

200

400

600

800

1000

ABCDEFGH I J

Self-reprted level of

understanding

Participant's ID

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

1,0

0 200 400 600 800 1000

Power value of theta waves

Level of understanding

PhyCS 2018 - 5th International Conference on Physiological Computing Systems

Figure 7: Level of power of theta waves of each grade of

participant J.

4.8.2 Regression Analyses using Two Models

This section compares the accuracy of a logistic

regression model to that of a multiple regression

model to explain the relationships between the values

of physiological indexes and the self-reported levels

of understanding using the same recorded data.

Here, the objective variable of both regression

methods is the self-reported level of understanding.

However, it was necessary to convert the values from

ratio scales to ordinal scale in the logistic regression;

therefore, the values were divided equally by five and

ranked from first to fifth because the number of

samples in each division was small in greater than

five equal divisions. On the other hand, in the

multiple regression, the values of the self-reported

levels were used as the values of the objective

variable without conversion.

To avoid multicollinearity among the indexes, the

explanatory variables of both regression methods are

the principal components obtained by principal

component analyses of the values of the physiological

indexes.

The overall contribution ratio of the logistic

regression expression obtained using all participant

data was 0.34, and that of the multiple regression

expression using the same data was 0.35. Both ratios

are less than 0.5 and less accurate to explain the

relationships (Donna, online).

Therefore, we built individual logistic regression

and multiple regression expressions to explain the

relationships of each participant using only that

participant’s data. Table 5 shows the contribution

ratios of the individual logistic regression and

multiple regression expressions of each participant.

As can be seen, all ratios are greater than 0.5, the

mean ratio of the logistic regression expressions is

0.72, and the mean ratio of the multiple regression

expressions is 0.70. These results indicate that the

regression expressions can reasonably explain the

relationships between the participant’s levels of

understanding and the physiological indexes

individually, and that there is no significant

difference between logistic and multiple regression.

Table 5: Contribution ratios of two regression models for

each participant.

ID Contribution ratio of

logistic regression

Contribution ratio of

multiple regression

A 0.68 0.68

B 0.74 0.68

C 0.79 0.75

D 0.70 0.68

E 0.58 0.53

F 0.73 0.72

G 0.78 0.70

H 0.61 0.67

I 0.85 0.86

J 0.76 0.71

4.8.3 Analyses of Accuracy of Estimation

We compared the estimated levels of understanding

using an individual multiple regression expression

with the self-reported levels of understanding for each

participant. The data for the comparison were

classified into three difficulty grades, i.e., low

(Grades 4 and 3), middle (Grades 2 and Pre-2), and

high (Grades 1 and Pre-1) for the analyses. Figure 8

shows the variance of the correlation coefficients for

all individual participants of each classified grade.

Note that values were normalized to derive the

multiple regression expressions. The graph shows

that the coefficients of the high grade converge within

a high range and that the mean coefficients of the high

grade are greater than that of the other grades.

Figure 8: Correlation coefficients of three classified grades.

-0,6

-0,4

-0,2

0,0

0,2

0,4

0,6

0,8

1,0

12345678910

Power of theta waves

Listening task trial

Grade 1 Grade Pre-1

Grade 2 Grade Pre-2

Grade 3 Grade 4

0,0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1,0

High Middle Low

Correlation coefficients between

estimate value and reported value

Difficulty of listening task

Regression Analyses between Physiological Indexes and Level of Understanding with VAS of a Listening Task

Figures 9 and 10 show the estimated values by the

multiple regression expression and the actual self-

reported values for Grade 4 (Figure 9), which is the

easiest, and Grade 1 (Figure 10), the most difficult,

for participant G, whose correlation coefficient is

close to the mean of all participants. The graphs show

that the estimated values are approximately close to

the actual self-reported values.

We also compared the estimated levels of

understanding using an individual logistic regression

expression with the self-reported levels of each

participant. Here, the mean accuracy rate of the

estimation of all individuals was 0.76, and the

standard deviation of the rates among the individuals

was 0.10.

Figure 9: Comparison of estimate values with self-reported

value of Grade 4 of participant G.

Figure 10: Comparison of estimate values with self-

reported value of Grade 1 of participant G.

4.8.4 Regression Analyses using Brain

Waves

We attempted to conduct regression analyses with

both models using only brain wave indexes to

consider whether the number of physiological signals

should be reduced relative to implementation cost.

These analyses were limited to brain waves because

the correlation coefficients of the brain wave indexes

were greater than those of the other signals, and the

signs of the coefficients of all participants were the

same (Section 4.8.1).

Table 6 shows the contribution ratios of the

individual logistic regression and multiple regression

expressions using only the values of the brain wave

indexes for each participant. As can be seen, the mean

ratio of logistic regression is 0.53, and the mean ratio

of multiple regression is 0.53. Both mean values are

less than those of the expressions using all

physiological indexes (Section 4.8.2).

Table 6: Contribution ratios of two regression models using

only brain wave indexes.

ID Contribution ratio of

logistic regression

Contribution ratio of

multiple regression

A 0.43 0.44

B 0.70 0.62

C 0.72 0.69

D 0.58 0.43

E 0.31 0.30

F 0.46 0.48

G 0.33 0.41

H 0.39 0.46

I 0.76 0.78

J 0.59 0.64

5 DISCUSSIONS

Relative to H1, we verified a significant relationship

between a listener’s self-reported level of

understanding in the listening task and the listener’s

physiological signals at an individual level. However,

the contribution ratios of the logistic and multiple

regression expressions using all participant data

collectively were low; thus, we consider that it is

difficult to develop a unified regression model to

express the relationships of all learners. We surmise

that this difficulty is due to the fact that kind or

tendency of physiological signals that change relative

to a difficulty level of understanding varies among

individuals, and that each individual has a particular

kind or tendency of the physiological changes when

he/she is finding something difficult. On the other

hand, we suggest that the power value of the theta

brain waves has a certain tendency to change

according to the level of understanding of all

listeners.

Relative to H2, we found no significant difference

between the accuracies of logistic and multiple

regressions when modelling the relationships

between the self-reported level of understanding and

the physiological indexes. Therefore, we consider it

necessary to use another statistical regression model,

to combine another type of data such as English score,

or to include other physiological signals such as an

0,0

0,2

0,4

0,6

0,8

1,0

1,2

12345678910

Normarized level of

understanding

Listening task trial

Actual self-reported value Estimate value

-4,0

-3,0

-2,0

-1,0

0,0

1,0

12345678910

Normarized level of

understanding

Listening task trial

Actual self-reported value Estimate value

PhyCS 2018 - 5th International Conference on Physiological Computing Systems

electromyogram to measure movement of facial

muscles, in order to increase accuracy.

Relative to H3, we have verified that the accuracy

of the 999-degree scale was greater than or equal to

that of the four-level scale in our previous work,

where the mean accuracy was 0.69 (Omata, 2018).

Therefore, we propose that it is possible to estimate

the level of understanding at sufficient resolution

using a VAS.

6 CONCLUSIONS

We conclude that our logistic regression expressions

can explain the relationships between an individual

level of understanding of listening a second language

and individual physiological indexes with 0.72

contribution ratio, and that our multiple regression

expressions can also explain the relationships with

0.70 contribution ratio. The multiple regression

expressions can explain the relationships with

convergent higher correlation coefficients when the

speech voices are difficult for the listener.

Additionally, we suggest that VAS is useful to

answer a level of understanding and estimate it at high

resolution.

We, therefore, propose that it is possible to

develop an e-learning analysis system that

automatically estimates a detailed level of

understanding from learner’s physiological signals

and an e-learning content recommendation system

that automatically provides a flexible and adaptable

learning material based on the estimation for an

individual learner.

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Regression Analyses between Physiological Indexes and Level of Understanding with VAS of a Listening Task