Antonenko, P.D.,
and Niederhauser, D.S. (2010)
The influence of
cognitive load and learning in a hypertext environment
Computers in Human
Behavior
Participants
Researchers have suggested that gender, handedness,
and age can differentially affect brain wave activity (Andreassi,
2007; Fisch, 1999). Further,
EEG patterns can be influenced by brain disorders, or
medications to treat brain disorder conditions (Andreassi, 2007).
Thus, the subject pool was limited to right-handed, 18–23 year-old females with
no known brain disorders.
Materials
descriptions of 4 different learning theories (500 words + or
– 5) . 7 hypertext links at each node (90 + or – 5 words). identical
grammatical and syntactical structures were used across all texts (e.g.,
sentence length, number of clauses, stylistic devices). Also checked for
readability and matched conceptual difficulty.
The hypertext presentation system was designed using
guidelines
suggested by current web usability research (Nielsen,
2006).
Nodes were presented as a single frame using Georgia
serif font,
with black lettering on a white background and no
tracing images
or watermarks. Font size was set to 100 percent of the
default
browser font size, which translated to approximately
16 point font
on the monitor used for the study.
EEG data
Electroencephalogram. EEG data were acquired using a
Biopac
MP30 connected to a Macintosh G4 MiniMac computer.
Electrode
placement was confined to one set of electrodes over
the
pre-frontal cortex (F7) and one-over the parietal lobe
(P3) in the
left hemisphere of the right-handed subjects. EEG data
were collected
at a sampling rate of 500 Hz as each subject read each
of
the four hypertexts. Electrode placement followed the
Modified
Combinatorial Nomenclature expanded 10–20 system, as proposed
by the American Clinical Neurophysiology Society (Jasper,
1958).
The EEG software recorded brain wave rhythms as
separate channels,
allowing identification of the following wave
components: (a)
raw EEG signal, (b) alpha rhythm, (c) beta rhythm, and
(d) theta
rhythm.
Event-Related Desynchronization percentage (ERD%) for
alpha,
beta, and theta rhythms were used as online measures
of brain
activity (Pfurtscheller & Lopes de
Silva, 1999). Increased cognitive
load is associated with higher brain wave desynchronization
for alpha
and beta rhythms, and higher brain wave
synchronization for
the theta rhythm, when subjects move from a relaxed,
eyes-closed
state (baseline) to an eyes-open, active-reading state
(Basar, 2004;
Klimesch, 2005). Consequently,
ERD%, which compares brain wave
power in the test condition with the brain wave power
in the baseline
condition, is represented by a positive number for the
subjects’
alpha and beta rhythms (reflecting wave
desynchronization), and a
negative number for the theta rhythm (reflecting
synchronization).
Thus, larger desynchronization percent values for
alpha and beta
waves, and larger synchronization percent values for
theta waves
indicate increased cognitive load.
The following formula was used to compute ERD% (Pfurtscheller
& Lopes de Silva, 1999):
ERD% =baseline interval band power-test interval band
power
baseline interval band power x 100
Band power values of the subject’s alpha, beta, and
theta brain
waves were estimated with the Biopac
psychophysiometrical system
software. The Area function was used to calculate
alpha, beta,
and theta wave power. This function computes the total
area of
waveform under a straight line drawn between the
endpoints.
Thus, this measure takes into account both wave
frequency (Hz)
and amplitude (microV). Area under the curve for
alpha, beta, and theta
brain wave rhythms was computed for a 20-s segment of
the baseline
condition, which was obtained before a given subject
started
reading each of the four texts. This yielded a unique
baseline interval
band power of brain activity for the time period
immediately
preceding reading each text that was used in the ERD%
formula.
Area was then computed for the test interval band
power. Using
the marker placed on the EEG recording as a referent,
a 20-s test interval was established
which included the 10 s spent reading the
primary passage immediately before selection of given
link to a subordinate
node, and the 10 s spent reading immediately after
clicking
the link to the subordinate node. Test interval band
power was calculated
using this 20-s interval because subjects read and
processed
lead information before they clicked on the link to
open the subordinate
node in the lead-augmented hypertext condition. ERD%
values
were computed for alpha, beta, and theta brain wave
rhythms
on each of the seven links within each of the four
hypertexts.
Average ERD% value was then computed for each of the
three
brain wave rhythms under each of the two experimental
conditions.
For example, the ERD% values for the 20-s intervals
for the
alpha rhythm were averaged for each subject, yielding
one alpha
ERD% value for each text. The alpha ERD% values for
the two nolead
texts were then averaged, as were the alpha ERD%
values for
the two lead-augmented texts. This yielded one grand
alpha
ERD% value for the lead-augmented hypertext condition,
and a second
alpha ERD% value for the no-lead condition. This
procedure
was then used to compute grand averages for lead and
no-lead
conditions for beta and theta rhythms. Since cognitive
load is reflected
by more negative ERD% for the theta rhythm (Klimesch,
2005), and more positive ERD% for alpha and
beta rhythms, we
used absolute values for measures of theta waves to
help make
interpretation of results more intuitive. Grand
average ERD% values
for alpha, beta, and theta rhythms served as dependent
measures.
Procedure
Each sat at a table with an adjustable-height high-back chair with
armrests.
Subjects faced a blank white wall, which extended beyond
peripheral vision on both sides. A full-size 110-key keyboard, wireless
mouse, and 17-inch LCD monitor were on the table in front of
the subject. The same computer and monitor were used for all
subjects.
The monitor surface was approximately 50 cm from the subject, and was
set to 1280 by 854 pixels (the highest available resolution) and maximum level
of brightness. Luminescence was measured at eye-level and at a distance of
approximately 50 cm from the computer screen using a Tenma Digital Lux meter.
Luminosity of hypertext displayed on the LCD monitor was equal across text
conditions.
With the subject comfortably seated at the computer, the researcher
read a scripted verbal overview of treatment procedures to begin the
session. He then attached disposable vinyl electrodes (Ag/AgCl) to two recording
sites on the subject’s skull: (a) pre-frontal lobe (F7) to collect data on
the power of beta and theta waves and (b) parietal lobe (P3) to collect data on
the power of alpha waves. Measurements were referenced to the left mastoid
with
the earlobe serving as ground. Electrode impedance was below
10 kX. The EEG signal passed through an Infinite Impulse Response
(IIR) bandpass filter to remove unintended artifacts of movement,
allowing us to retain only the frequency components that were
of interest in the present study: theta (4–7 Hz), alpha (8–13 Hz),
and beta (14–30 Hz). Two more sets of electrodes were attached
to collect the subject’s electrooculogram and electromyogram of
the dominant (right) hand, which was used to filter artifacts associated
with eye movement, blinking, hand movement and mouse clicking. Subjects
were instructed to minimize unnecessary movement
during the hypertext reading and browsing task.
After all electrodes were placed and the EEG equipment was
activated, the subject sat in a relaxed state with her eyes closed until
an extended alpha pattern was noted. At that point the 20-s baseline
brain wave rhythm sample was recorded and the subject was instructed to open
her eyes (blocking the alpha rhythm) and read the first experimental hypertext.
When the subject had finished reading she said ‘‘done,” completed a self-report
of mental effort measure, and closed her eyes and relaxed until her brainwave patterns
returned to the baseline condition (extended alpha).
Returning to baseline helped circumvent carryover effects from one
treatment to the next.
The researcher brought up the next experimental text while the
subject was returning to baseline. After establishing and recording
the next baseline brain wave sample, the subject was instructed to open
her eyes and read the second text, complete the mental effort
scale, and close her eyes until she returned to the baseline condition.
This procedure was repeated for the remaining two texts. The sequence of
presenting texts to subjects was counterbalanced. When a subject had finished
reading the fourth hypertext, she was instructed to close her eyes to return to
an alpha state, which provided an end point for the brain wave
recording.
Analysis
A 2 x 5 repeated measures MANOVA was conducted to determine
the effect of leads on learners’ cognitive load. Presence of
leads (Lead vs. No-lead) served as a within-subject factor, and
the five measures of cognitive load: (a) reading time; (b) self-reported
mental effort; and Event-Related Desynchronization percentages
of (c) alpha; (d) beta, and (e) theta brain wave rhythms were used as
dependent measures.
Results
The MANOVA for cognitive load measures was significant
(F(5,12) = 58.94, p < 0.01), prompting further
analysis through a series
of univariate repeated measure ANOVAs. For each of the
ensuing
ANOVAs, presence of leads served as the independent
variable,
with each of the cognitive load measures serving as a
dependent
measure. A main effect was found for reading time (F(1,16)
= 5.55,
p < 0.05, MSE = 427.63). Subjects spent more time
reading in the
lead condition than in the no-lead condition (X =
587.23,
SD = 116.06 and X = 571.83, SD = 126.20 s, respectively).
Main effects
were also found for alpha, beta, and theta ERD%
(F(1,16) = 103.47, p < 0.01, MSE = 7.12; F(1,16) =
35.71, p < 0.01,
MSE = 15.34; F(1,16) = 252.56, p < 0.01, MSE =
1.03, respectively). Table
1 shows that mean alpha, beta, and absolute
value of theta
ERD% in the no-lead condition was higher than in the
lead condition.
These findings reveal lower cognitive load in the Lead
condition.
No other results reached significance (p > 0.08).
Discussion
The aim of this study was to determine the influence of leads on
cognitive load and learning in hypertext. The study produced several
important findings. First, EEG-based cognitive load measures
showed that subjects’ brain wave activity was less intense when
they were accessing hypertext nodes via leads. Conversely, the
self-report of mental effort measure did not detect significant
differences
in cognitive load between the two experimental conditions,
and subjects tended to spend more time reading lead augmented
hypertext. Second, use of leads appeared to produce a
positive effect on learning outcomes relative to domain and structural
knowledge acquisition.
The discrepancy between the results of EEG-based measures of
cognitive load, self-report of mental effort, ie EEG refelects what goes on in real time whereas self
report is retrospective
