by Jack White-Foy, Dulwich College

ABSTRACT

Neuroscience
encompasses every aspect of brain and behaviour, from tiny molecular
interactions to exchanges between groups of whole organisms. Educational
neuroscience is the relatively new field of applying our understanding of
development, cognition and behaviour to teaching and learning. This project set
out to introduce students to cutting-edge Brain-Computer-Interface (BCI)
technology through wearable, wireless EEG headsets. Students were encouraged to
design and run their own EEG research projects through a school society. They
were then asked to reflect on using such technology in schools and their views
on the usefulness of neuroscience for students and teachers. The results show
that students as young as 12 years old can successfully learn how to set up and
carry out small-scale research projects. Whilst they struggled to take full
advantage of the technology, they have grasped a rudimentary understanding of
the relationship between recorded electrical activity and associated brain
function. Additional findings related to the practicality and benefit of
setting up such projects both to teachers and students themselves. Limited
value was attached to the findings of EEG activity, which they ranked low to
both students and teachers. Teachers’ enthusiasm and subject knowledge were
ranked highest.

INTRODUCTION

In an
unfortunately over-cited article from 1997, John Bruer discussed the aggressive
progress in neuroscience and its appeal to educators. Whilst full of praise for
the technological achievements and advancement of knowledge, Bruer’s article is
best known for stating that educational neuroscience was a “bridge too far” and
that neuroscience was of little value to a classroom teacher (Bruer, 1997).
Bruer went on to state that this was due to a lack of knowledge and that with more
information, perhaps a bridge could be built. More than 20 years later and educational
neuroscience has shown no signs of slowing down. We know far more about the
development of brain structures and individual differences in cognitive
abilities and many traditional ideas around the brain have been proven to be
myths. Despite this progress, there is still not much to show for it in terms
of direct impact on teaching practice (Thomas et al, 2019). More troubling has
been the prevalence of neuro-myths that are still lingering today, which are
often taken seriously because they are loosely based on genuine research
findings (Grospietsch & Mayer, 2019). The repercussions of teachers
adopting strategies based on neuro-myths has been well documented and includes wasted
resources, time and even impaired student attainment (Dekker et al, 2012). More
research clearly needs to be done in neuroscience and education before the two
can be bridged effectively. The field is enormous and the organ at the centre
of it is the most complex system in the universe (Kaku, 2014). It is of little
wonder then that it might take some more time to unlock the brain and translate
how it works into a language that is understood by educators.

A Google
Scholar search for ‘neuroscience+research+education’ returned nearly 2.5
million results (June 2019). However, a search for
‘neuroscience+research+classroom-based’ returned just 6,590 results. Of
particular interest to me was conducting neuroimaging-based research in real
classroom settings (1,380 results on Google Scholar). With limitations
notwithstanding, this could provide a unique setting away from a laboratory
experiment and would hopefully generate ecologically valid results, which would
be more relevant to practising teachers. I also wanted to carry out this
research with students acting as assistants. The hope was that they would learn
simple concepts in neuroimaging as a platform for developing research skills.
With training in neuroscience to help them access its principles, I was also
interested in the value they thought the discipline could offer to teaching and
learning.

Zhang et
al (2019) investigated the extent to which school-aged children could learn how
to use EEG technology. Their aim was to identify factors that affect the
efficacy of Brain-Computer-Interface use with severely disabled children. Zhang
et al argued that the bulk of research into such EEG technology has involved
adults, despite the technology being of most benefit to children.  The study found that children were able to
effectively learn how to use the BCI technology to manipulate virtual
environments. The study did not explore whether their participants were
familiar with setting up the devices or with the neuroscience principles behind
the technology. The educational neuroscience programme described in this report
sought to investigate this. The programme was created with the following aims:

1. Short-term: set up a new school society, inviting
   all students aged 11 to 18 to learn how to use the EEG devices. Then assess
   whether students were interested in and able to learn how to research
   neuroscience in education. Finally, ask the students how relevant they saw
   neuroscience to teaching and learning based on their first-hand experience.
2. Long-term: build a network of students and
   teachers across disciplines and partnership schools to research neuroscience in
   and out of classrooms.

The
current report reviews progress made against the short-term goal.

THE NEURO LAB

Neuroimaging
technology has revolutionised the field of neuroscience (Cacioppo, 2008). From
its humblest beginnings of recording static structural images, the latest scans
can show beautiful functional videos of the active brain. The very latest
developments in ultra-high speed microscopy, called mesoscale selective plane
illumination microscopy (mesoSPIMS), has even been able to image individual 3D neural
networks (Voigt et al, 2019). The two main technologies have been functional
magnetic resonance imaging (fMRI) and electroencephalography (EEG). Whilst EEG
has superior temporal resolution (≈.05s with EEG compared with ≈1.0s on MRI),
EEG is limited to measuring event-related potentials at the cortex and as such
cannot provide data on subcortical (deeper brain) activation (Aue et al, 2009).

One analogy commonly used to compare fMRI and EEG is that of a sports stadium packed with spectators. With fMRI, the spatial resolution is such that you could record a single conversation between two individuals but with a delay. With EEG, the recording would be equivalent to lowering a single microphone into the middle of the stadium; there would be virtually no delay. The sound would be a combination of thousands of voices all at once with no way of distinguishing one individual. With such limitations, EEG is perhaps better suited to recording general brain wave patterns, rather than trying to identify neural activity in discrete brain regions (see Figure 1). One such example is gamma waves, which are high frequency and are associated with high-level cognitive tasks such as problem-solving (Roohi-Azizi et al, 2017). Delta waves are low frequency and are associated with coordinating large regions of neural activity moving front to back (prefrontal cortex to occipital lobe). This frequency of wave is typically observed during periods of deep sleep (see Figure 1) and is associated with consolidation of memory. Ideally, fMRI would be the imaging tool of choice but with costs in the hundreds of thousands of pounds, it is accessible only to the most advanced of university research laboratories. Even EEG, the cheaper alternative can exceed tens of thousands of pounds. It is no wonder then that neuroscience research has had trouble entering classrooms. This is about to change.

In 2015, the company Emotiv released its first version of the Insight, a wireless EEG headset with 5 channels and costs just £200. The advanced model, the EPOC+, costs £600 and has 14 channels (see Figure 2). A recent study at the University of Macquarie in Australia compared the EPOC+ with university research-grade EEG hardware. It was determined that the EPOC+ was equal in performance (Kotowski et al, 2018).

Whilst considerably cheaper than a university EEG, the devices produced by Emotiv are still not insignificant for school budgets. Fortunately, in June 2018 my own department agreed to purchase a single Insight device. By September 2018, the headset arrived and in October 2018, The Neuro Lab society was launched with 20 members from Year 7 to 13 (11 to 18 years old). At this time, I was still learning how to set up the headset and use the software. The advantage of this was that I was not much further ahead of the society’s new members. This gave us the genuine situation where we were all learning together. My input was limited to an initial idea for a project, which was based on a video I had seen by IBM to demonstrate their new cloud service. A programmer had connected an Emotiv Insight to a Raspberry Pi (a credit card sized computer) which translated the brain pattern from the Insight into commands to control a remote-controlled Star Wars toy (the BB-8). A diagram of the setup is in Figure 3.

This project at first appeared to be a challenging and interactive task for the new society to complete. Instructions were available online and the hardware was simple to obtain. The Neuro Lab students developed the idea to include a challenge, so that cognitive performance could be measured. The task was to control the route of the BB-8 device through a maze. The time to complete the maze and the number of errors (e.g. hitting walls, moving in the wrong direction) would be recorded and used to track improvement with practice. The design of the project is shown in Figure 4 below.

In practice, the integration of the different software packages and learning the different programming languages meant this project had to be put on hold. An alternative approach to The Neuro Lab was needed to adjust the difficulty level to be more appropriate to the members. The students chose to focus on developing confidence with the following software, developed by Emotiv:

1) EmotivBCI: this is the main software suite that displays live electrode activity translated as mood indicators (see Figure 5). This uses the award-winning algorithm designed by Tan Lee, the founder of the company.

2) The software also contains a Brain-Computer Interface environment, whereby the programme is trained to recognise distinct brain patterns and translate them into specific commands. These commands are carried out by a virtual cube (see Figure 6). Commands include moving the cube up and down as well as growing and shrinking in size.

3) 3D Brain Visualizer: This software produces a live 3D model of the brain and maps regional activation. The colours correspond to wave frequencies (see Figure 7 below and Figure 1 above).

The Neuro Lab sessions were entirely student-led, with each learning at their own pace and developing their own project ideas. Due to the enthusiasm of the members, I increased the sessions to lunchtimes every Monday, Wednesday and Friday. This gave the members the opportunity to maintain a steadier rate of progress. Figure 8 shows photographs taken during real sessions in The Neuro Lab.

EXAMPLE PROJECT: DULWICH CREATIVE

During a special curriculum enrichment week at the College in Summer 2019, The Neuro Lab wanted to get involved and celebrate the theme: creativity. Members chose to observe neural activity (using the visualiser software) with the creative task of unguided drawing; no parameters or goals were set as to what or how to draw. Figure 9 shows the setup and a screenshot from the video that was produced.

EVALUATING
NEUROSCIENCE IN EDUCATION

The
potential benefits of increased understanding in neuroscience have been debated
for decades and will likely continue to be for years to come. What is arguably
true is that greater knowledge is always welcome. The aim of this project was
to see how students would respond to opportunities to study neuroscience in
school and whether it was too advanced. I was also interested in what they
thought of the potential applications for them as learners. Near the end of the
2018/2019 academic year, I asked members of The Neuro Lab to complete a short online
questionnaire. They completed it either in a Neuro Lab session, or in their own
time.

The
survey recorded respondents’ names to ensure no duplicates were submitted and
to allow me to know who to send reminder requests to. Students were assured
that their names would be removed and replaced with a numerical ID, that only I
would have access to. Students were informed that taking part was entirely
voluntary and that they could remove their data at any time.

The
questions were organised into four sections:

1. Familiarity with the neuroimaging
   headsets
2. Knowledge and understanding of
   neuroscience
3. Views on applications of neuroscience in
   education
4. Views on teaching and learning priorities
   for teachers and for students.

For
section one, the first question asked respondents if they felt confident carrying
out various tasks. They could choose as many as they wished. This was followed
by open qualifier questions that challenged their responses to the previous check-box
question, by asking for technical responses to scenarios (e.g. “How could you
improve signal quality from an electrode on the EPOC+?”). For all sections,
opinions were sought using Likert-style questions to give respondents greater
choice and to help differentiate between opinions. Eight students (aged 11-13)
answered the questionnaire. All responses are in Appendix 1 with highlights
described below.

SURVEY
RESULTS

Figure 10 summarises responses to the question on confidence using the technology. All respondents indicated they were very confident with setting up and using the devices (options 1-6). Fewer responses stated they were confident using the software and interpreting the results (option 7). In the follow-up questions, all students were able to give correct solutions to technical problems. Their knowledge of which brain structures could be measured with the headsets varied. Half of students had misconceptions, thinking that EEG can measure subcortical structures, which it cannot.

Question 9 in the survey asked students to consider how challenging certain aspects of The Neuro Lab were (see Figure 11). There was considerable variation in response, which reveals the differences in students’ strengths. Some members were full of ideas for projects, whilst others found this challenging. Interestingly, the question that drew the greatest range of results was regarding setting up the headsets, which will be discussed later.

Questions 10, 12 and 13 interrogated students’ views on the value on neuroscience in schools. Specifically, they were asked to rate suggested applications of the field and then rank teaching and learning priorities for students and for teachers (see Figures 12, 13a and 13b).

The majority of responses were positive when rating the applications of neuroscience in schools. This contrasted with where they ranked neuroscience findings, specifically brain region activation with associated tasks, in priorities for students (5^th out of 9) and teachers (5^th out of 6). The most highly ranked factor was a teacher’s subject knowledge and enthusiasm. The factor ranked last for both student and teacher priorities was knowledge about what makes students forget information.

CONCLUSIONS

The
students involved with The Neuro Lab have learned a great deal of technical
knowledge and skill. They are confident using the equipment and have made
considerable progress in terms of their understanding of the applications of
the technology as a Brain-Computer-Interface and a way of observing brain
activity. They are still developing their skills in creativity for research
ideas and there is a strong desire to learn more about cognitive neuroscience.
As a subject, it is clear that neuroscience is not necessarily too advanced for
school-age students and instead has been a surprising motivator. It has
provided a challenging and interesting off-curriculum avenue for all students,
from the strongest science fanatic to the weakest non-academic. It is possible
that the freedom to design, test and abandon research ideas in a low-stake
environment has given some students greater confidence where they might
otherwise be lacking and the room to stretch for those already feeling comfortable
with the pace of their formal studies.

The
survey revealed that students felt confident describing how the technology
could be used and they had a strong understanding and skill in this area.
Interestingly, they also found this to be the most challenging aspect of The
Neuro Lab. This was also my experience. Whilst the EEG technology and
underlying algorithms are state-of-the-art, the wireless aspect still relied
upon Bluetooth technology, which was the weakest link in the setup. By June
2019, we had established a reliable sequence of setting up and connecting the
devices. In hindsight, the instructions provided by Emotiv were for individual
units. The Neuro Lab was working with seven simultaneously. This presented
considerable risk of interference from the different Bluetooth connections,
which was arguably the main reason for frustration felt by members and the most
significant drag factor in affecting progress made on projects. At times, it
would take up to 40 minutes just to set up the devices, which left 20 minutes
to use them.  Since the society restarted
in September 2019, we have consistently set up all seven devices in 5 to 10
minutes by following the sequence established by members.

A
surprising result from the survey was the factor ranked lowest as a priority
for teachers and students: ‘understanding forgetting’. Knowing how we learn and
how we forget are arguably of comparable significance, yet they were ranked
quite differently by the students. This highlights the limitations of students’
grasp of pedagogy and cognition. They are not experts in teaching and learning.
Teachers are experts, as a result of years of training and experience. To
expect a student to have a similar understanding of and appreciation for the usefulness
of a field of research, is unrealistic. Whilst valid, caution should be taken
when evaluating any model, intervention or policy using student opinion. In
hindsight, I would have provided students with relatable examples of each
factor in the survey.

IMPLICATIONS

The
teenagers in this project did learn how to use neuroimaging technology and are
interested in neuroscience. They have yet to explore neuroscience principles
in-depth, which goes some way to explain why they placed neuroscience so low in
their priorities for teachers and students whilst at the same time were
positive about the potential applications of neuroscience in schools. To some
degree, this reflects the current state of play of neuroscience in education.
There has been a growth in enthusiasm for learning more about the teenage brain
and the implications of the research. Whilst this hunger has grown, the
real-world applications of neuroscience for teachers and students has lagged.
In the past five years, I have attended half a dozen specialist conferences,
talks and workshops on neuroscience in education. At each of these events, they
have ended with the same questions: what are the practical lessons for teachers
to take from neuroscience and how can we bridge the divide between researchers
and teachers? These questions were followed by a ten-minute brain-storming
session where attendees were asked to come up with ideas. Suggestions were
vague, unrealistic and narrowly focused, lacking in experience on both sides.
The teachers had insufficient experience of the research and most of the
researchers had never set foot in a classroom.

There is
a real risk that in the rush to satisfy educators’ hunger for neuroscience, there
will be a return of the neuromyths that resulted in wasteful and even damaging
policies in schools (e.g. VAK learning styles, Brain Gym, hemispheric learners
and critical periods). I am a strong advocate for educational neuroscience.
There are only advantages to be gained from greater knowledge of how the brain
develops throughout childhood and adolescence and the relationship with
academic performance. Obtaining this knowledge takes time and careful research
design because accessing the brain in valid settings is challenging. Wireless EEG
offers one method we can use to achieve this, but we must remember the
limitations of the technology and the considerable differences between
children. Above all, we need to be patient.

LOOKING
AHEAD

In the
current academic year 2019/2020, the students are now ready to use the
technology with confidence. It is our intention to begin more detailed learning
of the theories of neuroscience to give more context to the data we collect and
to feed the students’ creativity. To start us off, I have been in contact with
a consultant in pain management who has experience in using EEGs to monitor
cortical activity in patients during surgery. This activity is used to measure the
level of consciousness in the patient during surgery and allows the
anaesthesiologist to adjust the anaesthetic accordingly. The consultant has
offered to visit The Neuro Lab to give the members a course in the neuroscience
of pain and make links to the EEG technology with which they are familiar. I suspect
that a greater level of theoretical knowledge will not only challenge the
students, it will help to inspire them all to design their own projects, an
area in which a large number struggled.

When
discussing projects for 2019/2020, The Neuro Lab members decided they would
still like to work on the BB-8 project. I contacted the Head of Developer Marketing
Content Strategy at IBM, the original designer of the BB-8 Brain-Computer
Interface. He has since been in touch and has agreed to help The Neuro Lab work
on the project. With the fundamentals of the technology firmly under their
belts, the society’s members have already been making good progress. They have been
opening up the Python and Scratch coding languages of the devices and are
discussing ways in which the findings could be used: providing alternative communication
methods for those living with paralysis and tools for improving cognitive
abilities in children with special educational needs. There is a long way to go
but we are on the right road.

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