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Validating the Use of B-Alert Live in Measuring Cognitive Load in Engineering Problem-Solving

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Evaluation of cognitive functioning via physiological measures is a growing area of research in Engineering Education due to its potential implications for enhancing student performance. This paper focuses on the measurement of cognitive functioning via electroencephalography (EEG) and self-report measures, and their relationship with performance. Researchers evaluated the B-Alert X10 EEG system's reliability in measuring cognitive load, and thus indirectly evaluated its potential to measure both cognitive flexibility and cognitive efficiency in future research. Sophomore and senior undergraduate engineering students solved five engineering problems of increasing complexity while connected to the EEG. As a secondary measure, participants also completed the NASA Task Load Index, a multidimensional self-report assessment tool. The average cognitive load experienced by all participants increased as they attempted to solve problems of increasing difficulty, and sophomores experienced greater cognitive load than seniors. These findings further support electroencephalography as a valid measure of cognitive load.
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2018 ASEE Southeastern Section Conference
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Validating the use of B-Alert Live Electroencephalography in
Measuring Cognitive Load with the NASA Task Load Index
Charles Cowan1, Justyn Girdner1, Blaize Majdic1, Elise Barrella2, Robin
Anderson1, and Mary Katherine Watson3
1James Madison University, 3The Citadel, The Military College of South Carolina, and 2Wake Forest University
Abstract
Evaluation of cognitive functioning via physiological measures is a growing area of
research in Engineering Education due to its potential implications for enhancing student
performance. This paper focuses on the measurement of cognitive functioning via
electroencephalography (EEG) and self-report measures, and their relationship with
performance. Researchers evaluated the B-Alert X10 EEG system’s reliability in measuring
cognitive load, and thus indirectly evaluated its potential to measure both cognitive flexibility
and cognitive efficiency in future research. Sophomore and senior undergraduate engineering
students solved five engineering problems of increasing complexity while connected to the EEG.
As a secondary measure, participants also completed the NASA Task Load Index, a
multidimensional self-report assessment tool. The average cognitive load experienced by all
participants increased as they attempted to solve problems of increasing difficulty, and
sophomores experienced greater cognitive load than seniors. These findings further support
electroencephalography as a valid measure of cognitive load.
Keywords
B-Alert, EEG, NASA TLX, cognitive load, problem solving
Introduction
The brain is one of the most important, and most studied, organs in the human body. It is capable
of quickly processing information, making decisions to aid in survival, and controls a large
portion of the biological systems required for survival. The human brain constantly evaluates an
individual’s surroundings, looking towards the future and generalizing about our environment. It
develops strategies to enhance our opportunities and minimize dangerous encounters1.
In addition to maintaining biological homeostasis, the brain also spends a great deal of time
performing problem solving. Problem-solving engages a learner with a wide variety of cognitive
components, including but not limited to concepts, rules, information networking, memory, and
knowledge assessment2. This wide range of cognitive components can be attributed to the large
variation among problem types. To show a comparison across the several types of problems,
Jonassen collected hundreds of problems, analyzed their various attributes, and categorized them
into 11 main groups: logical, algorithmic, story, rule-using, decision making, troubleshooting,
diagnosis solution, strategic performance, case analysis, design, and dilemma3. These problem
types vary in degrees of structuredness and complexity. Well-structured problems present all
elements of the problem to the solver and have solutions that are both distinct and
comprehensible, where the relationship between decision choices and problem states is known4.
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Ill-structured problems possess elements that are unknown, contain a variety of solutions
(including no solution at all), and have multiple criteria for evaluating solutions5. Complexity on
the other hand, while slightly overlapping with structuredness, has a different meaning. The
complexity of a problem is defined by the number of issues, functions, or variables that are
needed to solve a problem. Complexity is also impacted by the degree of connectivity among
these properties, as well as their stability over time6. The degree to how well or ill structured a
problem is, as well as it’s degree of complexity, impacts how an individual solves the problem,
as well as how difficult the individual perceives the problem to be.
Individuals vary in their cognitive styles and controls, which represents patterns of thinking that
control the ways that individuals process and reason about information7. Therefore, a problem
that may be difficult to solve for one individual may be considered easy to another. Nevertheless,
problem solving is a very complex process, which is why it is a highly valued skill, and has a
strong emphasis in the fields of mathematics, science, and engineering8. Ultimately, we are
studying three measures of cognitive load, cognitive flexibility, and cognitive efficiency.
Cognitive load is the amount of mental effort exerted by the working memory at a given time8.
Cognitive flexibility is the ability to mentally switch between different concepts and thus to think
about multiple concepts simultaneously. Cognitive efficiency is an individual’s ability to use his
or her mental resources to solve problems. The study in this paper focuses specifically on
cognitive load.
Cognitive Load Theory and Measurement
Educational research literature is increasingly using cognitive load theory to understand how
individuals learn, and to seek ways to create more effective learning styles. Cognitive load theory
(CLT) is concerned with techniques for using working memory in ways that facilitate the
changes in long term memory associated with schema construction and automation9. CLT is
based on the established psychological principles of a long-term memory with a virtually
unlimited capacity for storing information, and of a working memory with a limited capacity in
processing information10. CLT research directly contributes to the design of instructional
methods that effectively maximize the use of our limited cognitive processing capacity in
acquiring knowledge and applying skills10.
The load that performing a specific task imposes on the learner’s cognitive system can be
represented by a multidimensional construct referred to as cognitive load12. Cognitive load
includes the concepts of mental load, mental effort, and performance13. Mental effort, compared
to mental load and performance measures, is considered more directly related to cognitive load11.
It is measured while participants are working on a task; and is the feature of cognitive load that
refers to the cognitive capacity that is allocated to accommodate the demands imposed by the
tasks being performed10. Mental load is task-related, and it serves as an indicator of the cognitive
capacity needed to process the complexity of a task. Using an electroencephalogram (EEG),
cognitive load can be measured based on the ratio of theta waves to alpha waves. A greater ratio
is indicative of a higher cognitive load.
The research team chose to use the B-Alert X10 EEG for several reasons. The B-Alert X10 is a
9-sensor EEG that provides the option of recording electrocardiographic (ECG) data, or more
plainly put, it can monitor participants’ heartrates. The device is low-cost, portable, and wireless
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device. The B-Alert X10 also interacts with the B-Alert Live software suite, which utilizes
thoroughly validated algorithms to measure and record cognitive load in real time14. This
algorithm uses a linear discriminate function (LDF) to generate a value between 0 and 1,
representing the likelihood that the individual is experiencing cognitive load at a specific 1-
second epoch. This variable typically has a low range (usually between .6 and .8), but it usually
has a low variance as well, granting it statistical power. In previous research, researchers have
analyzed this LDF-based variable through visual analysis and comparison, as well as through
statistical significance testing15.
As a secondary measure of cognitive load, researchers employed the NASA-TLX (Task Load
Index) self-report assessment tool. The NASA-TLX is a multidimensional assessment tool that
was developed by NASA in the late 1980s for gathering information about the magnitude and
sources of workload related factors16. The NASA-TLX carefully and specifically defines six
dimensions of workload: mental demand, physical demand, temporal demand, performance,
effort, and frustration. The researchers chose the NASA-TLX over other self-report measures as
it was well-established due to its age, commonly cited as a valid metric, and due to the tool’s
growing prominence as a secondary measure during EEG studies17. This measure not only
provided a secondary measure of cognitive load to compare to the team’s EEG results, but also
allowed the project greater confidence in the gradual rise in difficulty of its procedure’s
problems.
Developing the Problem Set
Five problems were selected to serve as the problem set the participants would solve. The first
problem selected was an algorithmic problem, designed to be very structured and low in
complexity. The second problem selected was a rule using problem, designed to be moderately
structured but still low in complexity. The third problem selected was a story (word) problem,
designed to be slightly less structured than the previous problem with the same level of
complexity. The fourth problem selected was a more complex rule-using problem, in which the
participant had to use concepts that were covered in both problem two and problem three. This
problem was designed to be less structured than the others while also being moderately complex.
The fifth problem selected was a troubleshooting problem, designed to be the most complicated
and least structured of all the problems. Each of these problems were selected so that they would
progressively increase in complexity and decrease in structuredness. The goal of this selection
was to increase the cognitive load experienced while solving each problem. For more
information about these five problems, please see the appendix.
The B-Alert Live System
The B-Alert X10® collects data from nine EEG sensor sites (Fz, F3, F4, Cz, C3, C4, POz, P3
and P4), two reference electrodes, and two electrocardiogram (ECG) lead sites. The device
interacts with a data analysis software package from Advanced Brain Monitoring (ABM) called
B-Alert Live®. This software package allows the researcher to easily analyze data via ABM’s
algorithms for assessing cognitive state metrics (which assesses engagement level) and cognitive
workload metrics (which assess a participant’s mental effort). The software package also allows
the researcher to monitor distraction via an algorithm derived from the cognitive state metrics,
and it monitors heart rate via ECG.
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B-Alert Live systems feature automatic signal decontamination measures, including measures for
electromyography (EMG), electrooculography (EOG), spikes, saturations, and excursions. The
software measures engagement via a four-class quadratic DFA derived for each participant
during the metric benchmarking task. This four-class model is constructed using absolute and
relative power spectra variables from Fz-POz and Cz-POz. The EEG measure of mental
workload was established via data from C3-C4, Cz-PO, F3-Cz, Fz-C3, and Fz-PO18. These
measures have been previously validated in military, industrial, and educational research19.
Participants
Participants were undergraduate engineering students at James Madison University (N = 9). All
participants were either sophomores (n = 4) or seniors (n = 5), and all were male except for a
single female sophomore. Participants received an $11 school dining voucher upon completion
of the study as incentive.
EEG Sessions
Participants were primarily communicated to via a script/protocol, which was written to
standardize the experience between participants as much as possible. Researchers gave the
participants specific points during the study in which participants could ask questions about the
study. After competing an informed consent document, all participants were fitted with the B-
Alert X10 system and the device’s connection was tested for impedance per instructions from the
device’s manufacturer. Participants then completed metric benchmarking tasks to create a
baseline for B-Alert’s algorithm to utilize. This algorithm allows B-Alert Live to compare each
individual subject’s baseline EEG activity to that same subject’s EEG activity under load.
After participants completed the metric benchmarking tasks, a research team member provided
participants a paper list of descriptions of all dimensions the NASA-TLX assessed, and data
acquisition began. During data acquisition, participants completed five physics problems of
increasing difficulty. Participants were offered 1-minute breaks between questions. After
participants completed a problem, researchers provided participants with a NASA-TLX report
sheet a scale for each of the six measured dimensions. At the end of data acquisition, participants
completed a final NASA-TLX task that compared each of the six dimensions to the other five, as
instructed in the NASA-TLX manual16. A participant’s time commitment for completing the
entire procedure ranged from 45 minutes to 90 minutes.
Results
When collecting the data, each participant was identified by a four-digit system. The first digit is
a representation of the student’s gender (0=male 1=female). The second digit is a representation
of the student’s grade level (0=sophomore 1=senior). The last two digits are a representation of
the participant number (ex. 01). Before the data could be analyzed, it first had to be cleaned. This
process began by selecting the data points that were collected while the participant was solving a
problem. This was done by using the recorded start and stop times noted above. After the data
associated with each problem was identified, the data was then scanned for invalid epochs. These
epochs occur when more than 128 (out of a possible 256) values are deemed corrupted by the
software, and thus the software labels that one second epoch as corrupted. These errors are
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identified as values of -99999 and are to be excluded from analysis according to B-Alert’s User
Manual.
Data Analysis
The researchers wanted to create a problem set that
increased in difficulty from problem to problem.
Table 1 shows the percentage of participants across
each of the five problems that answered the problem
correctly. This table shows that, generally, the
problems increased in difficulty as the experiment
progressed.
Table 1 Correct Responses Across Five
Problems
Problem Correct
Responses
Percent
Correct
1
9
100%
2
7
63%
3
7
63%
4
4
44%
5
4
44%
Researchers focused on averages of B-Alert Live’s cognitive load metric, overall correctness
score on the problems, and NASA-TLX self-report data. Average load can be defined as the
mean intensity of load during the performance of a single task (in this experiment, a single
question). This was calculated by averaging B-Alert Live’s cognitive load variable across the
duration of a problem. Overall correctness was scored based on a total of 15 potential points
throughout all five problems. NASA-TLX score was calculated by multiplying the weight of a
dimension against its raw score. This new adjusted score for each dimension was then summed
and divided by 15, creating an overall workload score ranging from 1 to 10. This workload score
was then averaged
across all five
problems for each
participant, giving
the researchers an
overall index of
load for the
experiment.
A consistent,
gradual increase in
average load
throughout the five
problems was
observed visually,
as seen in Figure 1.
This figure
demonstrates the
average cognitive
load that was experienced for each problem, and comparing sophomore averages against seniors.
As hypothesized, sophomores experienced a noticeably larger amount of cognitive load than
seniors did, with this difference increasing as the problems increased in difficulty.
0.62
0.64
0.66
0.68
0.7
0.72
0.74
12345
Average Cognitive Load
Problem Number
Sophomores
Seniors
Figure
1 Average Load Compared Between Sophomores and Seniors (a
higher problem number indicates a greater level of difficulty)
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Researchers then compared overall average load against the correctness scores of each
participant. This did not reveal any visible relationship or any kind. The researchers then
compared NASA-TLX overall load to each participant’s score, which also showed little
relationship.
Though correctness did not seem to have an impact on either an individual’s cognitive load, as
measured by the EEG, or via their own reporting via the NASA-TLX, there was a noticeable
difference between sophomore and senior participants in self-reported overall workload. As
demonstrated in Figure 2, seniors perceived that they were working harder than sophomores
perceived themselves working, despite EEG evidence to the contrary.
With this small of a
sample size (N = 9),
we did not run
inferential statistics on
this data. However,
inferential statistics are
appropriate tools to use
with both NASA-TLX
results and B-Alert
Live’s cognitive load
metric. Despite the
lack of statistical
analyses, there is a
visible difference in
self-reported workload
between sophomore
and senior participants.
Discussion
The researchers began this project with the goal of creating problems that scaled upward in
difficulty based on Jonassen’s hierarchy2. Based on the results demonstrated in Table 2, the
researchers feel confident that the problems increased in difficulty as the experiment progressed,
since correctness scores, on average, decreased as participants progressed through the procedure.
Researchers predicted that sophomore students would have to work harder, and thus exhibit more
cognitive load, than senior students to solve the same problems. Figure 1 supports this hypothesis
as measured through EEG data.
The research team implemented the NASA-TLX as a measure of self-reported workload to have
a secondary measure of problem difficulty, and to see if there was a difference between how hard
individuals thought that they worked and how hard individuals worked (as measured by the
EEG). This led the research team into some surprising findings as senior participants reported
that they were working harder on each task than sophomores, however they were experiencing
less cognitive load than sophomores. The researchers are not sure why this difference in
0
1
2
3
4
5
6
12345
NASA TLX Overall Workload
Problem Number
Sophomores
Seniors
Figure
2 Overall NASA-TLX Workload Compared Between
Sophomores and Seniors
(a higher problem number indicates a greater
2018 ASEE Southeastern Section Conference
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perceived, or self-reported, workload exists. It may be attributed to senior students nearing their
graduation dates, but regardless the results are interesting. The difference in perceived workload
does not strongly contradict the researchers’ originally hypotheses, given the other results of this
study, but it does raise some interesting questions about motivation.
Future Work
The research team is currently evaluating differences in cognitive load measured through both
EEG and self-report measures between linear and systems thinking. The researchers are doing
this by having senior undergraduate engineering students complete two tasks related to a specific
problem in the field of engineering. These two tasks are either the construction of a concept map
related to the specified problem or the listing of as many related concepts to the task as possible.
The listing of terms/concepts related to the problem is a linear thinking task, and concept maps
involve systems thinking. The current experiment being conducted in our lab can be directly
attributed to the success of the study presented in this paper. The validation of both the NASA-
TLX and B-Alert’s EEG quantification of cognitive load in our specific use case is essential to
our current study. Our overarching goal is to find ways to create teaching methods that naturally
promote the efficient management of cognitive resources, to train more effective engineers
within the sustainable design paradigm. The results of the study detailed in this paper, and the
experience that the research team gained during this study, provide the foundation that our
current research is built upon.
Acknowledgement
This material is based upon work supported by the National Science Foundation under Grant
Nos. 0846468 and 1463865. Any opinions, findings, and conclusions or recommendations
expressed in this material are those of the author(s) and do not necessarily reflect the views of
the National Science Foundation. The authors would also like to thank the student participants
that took part in this study and Dr. Olga Pierrakos and Ms. Winifred Opoku who contributed to
the study.
References
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Charles M. Cowan
Charles Cowan is a post-graduate research assistant at James Madison University. He received
an M.A in Psychological Sciences in the spring of 2017.
Justyn Girdner
Justyn Girdner is an undergraduate Engineering student at James Madison University. His
expected graduation date is spring 2020.
Blaize Majdic
Blaize Majdic competed his bachelor’s in Engineering at James Madison University in the spring
of 2017. He is currently working on his graduate degree at Virginia Commonwealth University.
Elise M. Barrella
Dr. Elise Barrella is an assistant professor, and a founding faculty member, of Wake Forest
University’s Department of Engineering. Prior to joining the founding faculty team of Wake
Forest University, she performed research and instructed classes at James Madison University.
Her research interests include education, assessment, transportation, and sustainability.
2018 ASEE Southeastern Section Conference
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Robin Anderson
Dr. Robin Anderson is the department chair of the Department of Graduate Psychology at James
Madison University. She specializes in the validation of assessment and program evaluation
procedures.
Mary Katherine Watson
Dr. Mary Katherine Watson is an assistant professor of Civil and Environmental Engineering at
The Citadel. She has been with The Citadel since August of 2013.Her research interests include
sustainable engineering and environmental engineering.
Appendix
1. Examine the figure below. If there is a upward force of 1200N applied to an object, at the
same time a downward force of 800N is applied to the object, determine the magnitude and direction of
the net force that is applied on the object.
2. Using the triangle [above], solve for the angle θ.
3. A gymnast has a mass of 55kg and is hanging vertically from a pair of
parallel rings (as shown below). If the ropes supporting the gymnast are
completely vertical and attached to the ceiling above, what is the tension force
in each of the ropes?
4. If the same ropes mentioned in the problem above are connected to the ceiling, where
θ=45°, what is the tension force in each rope?
5. For the pulley system shown below, if the mass of block m is equal to 10kg, what force must be applied at the
end of rope 1 to keep the system in static equilibrium?
ResearchGate has not been able to resolve any citations for this publication.
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Within the cognitive load theory research community it has become customary to report theoretical and empirical progress at international conference symposia and in special issues of journals (e.g., Educational Psychologist 2003; Learning and Instruction 2002). The continuation of this custom at the 10th European Conference for Research on Learning and Instruction, 2003, in Padova, Italy, has materialized in this special issue of Instructional Science on the instructional implications of the interaction between information structures and cognitive architecture. Since the 1990s this interaction has begun to emerge as an explicit field of study for instructional designers and researchers. In this introduction, we describe the basics of cognitive load theory, sketch the origins of the instructional implications, introduce the articles accepted for this special issue as a representative sample of current research in this area, and discuss the overall results in the context of the theory. It is generally accepted that performance degrades at the cognitive load extremes of either excessively low load (underload) or excessively high load (overload) – see e.g., Teigen (1994). Under conditions of both underload and overload, learners may cease to learn. So, whereas learning situations with low processing demands will benefit from practice conditions that increase the load and challenge the learner, learning situations with an extremely high load will benefit from practice conditions that reduce the load to more manageable levels (Wulf and Shea 2002). Cognitive load theory (CLT; Paas, Renkl and Sweller 2003; Sweller 1988, 1999) is mainly concerned with the learning of complex cognitive tasks, where learners are often overwhelmed by the number of information elements and their interactions that need to be processed simultaneously before meaningful learning can commence. Instructional control of this (too) high load, in order to attain meaningful learning in complex cognitive domains, has
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