How EEG Can be Used to Create Optimal Learning Environments

by Dr. Roshini Randeniya

Education is a fundamental pillar of our society, and providing rich learning environments is essential for societal advancement. Educational neuroscience is a rapidly developing interdisciplinary field that aims to understand the neural mechanisms of teaching and learning.

Over the past two decades, advances in portable EEG technology have enabled researchers to use EEG headsets in both classrooms and e-learning to create optimal learning environments for students [1]. In this article, we look at how EMOTIV’s EEG headsets are being used to change how we teach and learn.

Optimizing educational content

Designing engaging educational content requires constant subjective feedback from students. Traditionally, determining the effectiveness of a course's content is done through self-reporting feedback measures upon completion of a course.

However, it is often difficult to isolate exactly which aspects of the course delivery can be improved due to reliance on subjective memory. Due to its high temporal resolution (i.e., its ability to measure brain responses in the scale of milliseconds), EEG is able to index pre-conscious processes, which would otherwise go unrecognized with mere self-report measures. When optimizing course content, the most useful metrics are the level of attention and cognitive load - a measure of the amount of effort the brain exerts to retain the information. Attention is often measured by analyzing different brain waves observed in the EEG when someone is learning - such as the levels of alpha (typically associated with being fatigued) and beta waves (typically associated with being alert or focused). Cognitive load, a more complex measure, can also be indexed with varying levels of alpha and theta waves.

Researchers have developed systems with EEG that can monitor attention, allowing to assess attention levels throughout an entire course. Zhou et al. successfully demonstrated a real-time system that monitors the cognitive load of e-learning students engaged in Massive Open Online Courses (MOOCs), which paves the way for optimizing course content in real-time [2].

Analyzing cognitive states made easy

Measuring cognitive states, as in these previous studies, can require some technical skill and expertise. Fortunately, advancements in data science have now enabled the use of pre-built algorithms to measure cognitive states, with minimal technical expertise. Emotiv enables the use of Performance Metrics: machine learning algorithms developed to identify different brain states, including focus, excitement, engagement, frustration, stress, and relaxation in an EEG.

These algorithms are built using controlled experiments designed to elicit specific cognitive states and are useful to optimize educational content. These Emotiv Performance Metrics have been used to compare game-based learning vs. traditional pen-and-paper learning, although the study showed no difference in cognitive states between the two methods of learning [3]. Other researchers have demonstrated the utility of Performance Metrics in grouping children as young as 5-7 years of age based on cognitive states such as engagement, stress, and focus to deem the effectiveness of activities in augmented reality environments.

Above: (A) EEG can be used to measure the brain waves of students in a high school classroom (from: Dikker et al. [4]). (B) Students’ brain waves can show high synchrony with other students, which was found for students that were more engaged in class (left). Low synchrony with other students (right) was found for students who were less engaged.

Enhancing learning environments

Not only is the content of educational material important, when and where we learn are equally important for ensuring that students have good learning experiences. Researchers measured levels of alpha waves during different classroom times and found that mid-morning high school classes showed less alpha waves than early morning and suggest that mid-morning may be the best time to learn [4].

Wireless EEGs have also been used to compare real vs virtual environments, demonstrating the ability to provide equal levels of attention and motivation in both environments [5]. This could pave the way for a richer learning experience for people with physical disabilities, unable to attend in person classrooms. Researchers have also conducted studies on social dynamics in the classroom using EEG. A group of students fitted with EEG headsets can be assessed for how synchronized their neural activity is during a common learning process [6][7]. This method of EEG data collection, called EEG hyperscanning, is a step towards real-time inference of group attention and improving social dynamics in the classroom.

Making education accessible to everyone

Some physical or sensory difficulties can limit students’ learning experiences in the classroom. However, there are EEG-based tools that are improving students’ experiences. Advances in Brain-Computer Interface (BCI) technology has enabled EEG based typing [8][9], which helps students with physical difficulties to take mental notes on their computing device as they learn. BCIs which enable EEG based answering of yes-no type questions are also allowing students with visual impairments to be assessed using computer-based examination, which would otherwise require an interviewer [10].

Personalized learning experiences

Providing personal tutors for students can be expensive but can often be necessary when the general education system is under-equipped to handle unique needs in learning. Intelligent Tutoring Systems (ITS) are a class of computer-based learning software backed with artificial intelligence that can act as personal tutors.

The aim of these systems is to adapt and provide real-time personalized feedback to the student to enhance their learning. Researchers are currently advancing ITS systems by integrating them with EEG. In one study, researchers use EEG to detect student engagement to different types of educational videos (animated content vs videos with human teachers) which allows the ITS to learn and automatically generate content that the student will find more interesting.

When you remove the human element from the teaching process, it becomes increasingly important to keep track of students' cognitive load while using computer-based learning programs to prevent stress and screen fatigue. To combat this, researchers have developed a face expression database based on EEG data that actively detects if a student was bored, engaged, excited or frustrated while using an ITS [11].

This development with EEG is paving the way for the ITS system to continuously learn and adapt to the individual student; by suggesting breaks when they’re tired or continuing to teach when they’re engaged, providing a more effective learning experience for the student.

Above: Students at the New York University (NYU) BrainWaves program play a game while wearing EMOTIV EEG brain technology.

EEG as a STEM learning tool

Emotiv EEG devices and software are easy to use and are an excellent introductory tool to inspire the next generation science, technology, engineering, and mathematics (STEM) scientist as well.

Emotiv devices and software are currently being used at university undergraduate level courses, not only in psychology and neuroscience but also in biomedical engineering. Kurent demonstrates a successful example of integrating Emotiv EPOC devices into the educational process at high school and college level to enable the advancement of BCI devices. Kosmayana et al. find that including EEG-BCI systems in school curriculums boost academic performance. Macquarie University has already demonstrated the successful inclusion of Emotiv devices in their Bachelor of Cognitive and Brain Sciences curriculum, giving students hands-on experience with experimental design and EEG data analysis [14].

Further, White-Foy demonstrates that children as young as 12 years old can successfully learn BCI technology and set up small-scale EEG research projects [13]. Students used online resources to integrate an EMOTIV Insight device to a Raspberry Pi (a miniature computer) which translates EEG into commands to control a remote-controlled Star Wars toy (the BB-8) and navigate it through a maze.

Above: Secondary school NeuroLab. 11-18yr old students integrated Raspberry Pi and BB-8 robot with Emotiv device and used mental commands to navigate BB-8 through a maze (shared with permission from NeuroLabs)

We can see that low cost, mobile Emotiv EEG devices provide not only methods of enhancing the quality of education programs for the educator to deliver exceptional content, but together with developments in BCI also proposes to provide a rich educational environment for individuals with unique needs.

How EMOTIV Can Help

• Improve your students' learning experiences with EMOTIV EEG Lab Starter Kits.
• Build experiments and analyze data with EmotivPRO Builder.
• Launch remote experiments to get data on EmotivLABS.
• Use our open-source data set.

Need Help? Contact Us

 Cover image source: Trevor Day School

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