6. Bridges to Adaptive 21st Century Inclusive Education: The Internet of Things and Wearables for Learning

 

By Larry G. Martin

Integrating Web3.0 technologies into the Internet of Things (IoTs) and wearables is ushering in a new era of adaptive, interconnected, inclusive, learner-centric education.  The IoTs represent a ubiquitous, interconnected system of about 20 billion small, everyday devices (like smart watches, wearables, and handheld gadgets) with Internet connectivity (Sandner & Richter, 2020). Each device has embedded sensors, microcontrollers, and software that monitor and record activity (such as sound, movement, and temperature) (Sandner & Richter, 2020). IoTs are increasingly providing personalized, accessible, and insightful learning and educational experiences for adult learners. They are also revolutionizing the way adults engage with their learning environments by creating safer, more effective, and inclusive experiences for those with a history of heightened anxiety and increased emotional distress in educational situations. Given the Web3.0 improvements over Web2.0 IoTs that impact 21st-century education and learning, you should carefully consider which devices should be added to your personal digital learning hub.

Earlier Web2.0 IoT devices (such as smartwatches, Go Pro wearable cameras, and smart glasses) could collect user data but offer limited interactivity. Like a paper-based map, they provided a reliable outline of the pathways, landmarks, and points of interest ahead. Travelers (students) needed to manually adjust for unanticipated changes or detours. These IoTs thereby facilitated self-directed, inflexible learning journeys and often resulted in overlooking critical incidents that resulted in educational dead-ends. In contrast, the vision for fully Web3.0 IoT devices is to create an educational paradigm where the boundaries between learners, educators, and environments are increasingly blurred.

 

The Vision for Web3.0 IoTs in Education and Learning

Web3.0 IoTs are envisioned to represent bridges toward assisting adults to become more informed, engaged, skilled, and adaptive learners. They will provide devices with more proactive self-learning capabilities, autonomous decision-making abilities, blockchain security features, decentralized data controls, and context-aware interactions. In learning and education, these technologies will be more like dynamic GPS navigation systems that embody responsive guidance and personalized, real-time adjustment to traffic conditions. Web3.0 IoTs guidance systems will offer adults customizable learning experiences that dynamically adapt to their performance, learning styles, and preferences. However, most of the IoTs available to learners and educators today are powered by Web2.5 technologies that fall short of the security provisions and decentralized controls afforded by blockchain-related technologies.

 

Web2.5 IoTs and Wearables

Web2.5 IoTs and wearables provide a broader range of learning experiences tailored to more diverse learning styles and needs. Many Web2.5 IoT devices can facilitate real-time communication and collaboration between teachers and students as well as between students (Nagar, 2023). In these new environments, educators can employ artificial intelligence, machine learning, and advanced data analytics to adapt to each learner’s difficulty level continuously and seamlessly. They can then suggest more appropriate content based on the performance metrics provided by IoT sensors and analytics. Several categories of Web2.5 IoT devices are now available to personalize the learning experiences of adults in various settings.

 

Personalizing Learning Environments with Wearable Technology

IoTs and wearable technologies can guide the structure of learning by capturing data to inform the design of learning activities, track and analyze individual learning patterns, and create personalized learning experiences (Chu et al., 2023). They can optimize educational experiences by considering individual learning styles, goals, and progress.

Optimizing Learning with Smart Watches and Fitness Trackers. Smartwatches and fitness trackers (like Apple Watch and Fitbit) can monitor users' biometric health data, track learners’ physical activity and habits, and offer real-time insights into the learning/educational journey. These personalized data can tell when a learner is most alert or stressed, which can trigger a need to tailor study schedules to optimize cognitive performance. Smartwatches can send alerts and reminders for deadlines and study times, prompt learners with quizzes, flashcards, or language practice, and provide health-related data that might indicate the best times for learning. They can also be helpful for students with intellectual and developmental disabilities to assist their integration into regular classroom settings (Chu et al., 2023).

Smart Glasses for Hands-Free Learning. Smart glasses (such as Vuzix Blade 2 ) can provide on-the-spot information and augmented reality experiences, especially useful for complex tasks requiring hands-on learning or visual cues. They can display app-based and Internet-based information directly in the user's sight, offering hands-free assistance and immediate access to learning materials or instructions during tasks and activities. They can also assist visually impaired learners by capturing and reading text from books or screens, thus providing continuous learning opportunities in various settings.

Adaptive Wearables for Real-Time Learning Support. Adaptive IoT devices can present material based on prior responses and provide real-time support, allowing tailor-made learning experiences that continuously adjust to the learner's progression (Covi et al. 2021). Wearable Cameras (such as GoPro cameras) can make learning more immediate and personal by recording first-person perspectives of experiences. Badges (such as Smart badges) can be equipped with cameras to provide a first-person view of tasks being performed. Smart Jewelry (such as Motiv smart rings, and Invisawear smart bracelets) and Smart Clothing (such as underwear, belts, bras, socks, jackets, etc.) can provide discreet notifications focused on health and safety without needing to consult smartphones or other devices.  

 

Timely Classroom Assistance and Stress Reduction

IoTs can also transform traditional learning materials (pens and notebooks) into interactive experiences. Innovative devices can also capture learners' attention and foster a deeper understanding of learning material, which can improve information retention and provide more stress-free and enjoyable learning experiences.  

Digitizing Notes with Smart Pens and Notebooks. Smart pens can digitize lecture notes and synchronize them with recorded audio, allowing learners to access personalized study material that aligns with their note-taking habits and auditory learning preferences (Van der Meer & van der Weel, 2017). For example, Livescribe pens record written notes and audio, syncing them for later review. Similarly, smart notebooks (such as Rocketbook) allow users to write notes with a pen on paper and then digitize these to the cloud. They can use machine learning to analyze study habits and suggest personalized content based on note patterns and topics covered.

Managing Stress with Smart Biofeedback Devices. Smart biofeedback devices can be helpful for learners prone to a lack of consistent focus and/or emotional stress in classrooms. For example, social anxiety disorder (SAD) is a prevalent psychiatric disorder affecting about 4 percent of the world population (Vilaplana-Pérez, 2020). It is characterized by a persistent fear of social or performance situations (such as classrooms) that risk exposure to unfamiliar people or the scrutiny of others. Adult students with SAD feel intense anxiety, receive poor grades, repeat academic years, and are often expelled from school, causing them to avoid these social situations (Vilaplana-Pérez et al., 2021). Affordable electroencephalogram (EEG) devices and systems designed for non-clinical use are increasingly being employed as a non-invasive means to assess and track brain functions for widely varying applications that include neurofeedback for pain, trauma, wellness management, mindfulness, and cognitive enhancement training. For example, the Muse and Emotiv brain-sensing headbands are embedded with sensors that can monitor brain activity, levels of engagement, and the degree of cognitive focus. They provide real-time data and feedback to help learners develop better concentration techniques. Biofeedback Devices (such as  EmWave Pro and Inner Balance) can provide real-time feedback on heart rate variability, helping learners manage stress and emotions for more effective learning practices.

Emotionally Responsive Education. Artificial intelligence systems are now being deployed on devices to detect emotions via facial expressions. Shame both stymies and motivates adult learning. It prevents adults from participating in educational programs, yet accompanied with self-examination it can be the catalyst for transformation (Walker, 2017). In educational situations, shame is often accompanied by other emotions (such as fear, hurt, or rage).  Emotion Recognition Software, such as Visage Technologies, can detect when learners are frustrated and adapt tasks accordingly. The software can detect the different degrees, intensities, and qualities of emotions in real-time, analyze facial expressions or physiological signals to gauge a learner’s emotional state, and provide insights for personalized content delivery (Harley et al., 2019).

 

Continuous Learning and Accessibility

Learners with hearing impairments can now maintain constant access to audio learning materials and resources through IoT-connected devices (such as Smart Hearing Aids). Similarly, Language Translation Wearables (like Pilot Earbuds) offer real-time foreign language translation, allowing learners to immerse themselves in new languages in real-time language comprehension and practice.

 

Selecting IoTs for Education and Learning

The convergence of Web3.0 and IoTs is still in its early stages. Web2.5 IoTs provide personalized, adaptive, real-time classroom diagnostics, assistance, and stress reduction to optimize adult learning activities, from smartwatches to smart earbuds. However, educational organizations may restrict the use of some of these technologies, many of which have not been secured by Web3.0 blockchain technology. You should carefully consider the pros and cons of each device before adoption into your digital learning hub.

 

Up Next: Big Data and Learning Analytics

Enormous amounts of personal data are generated from the tools in adult learners’ digital toolkits. In my next blog post, I will discuss how organizations use big data, educational data mining, and learning analytics to unlock the educational potential of lifelong learning.

 

Larry G. Martin, Ph.D.
Professor Emeritus, UWM
Follow me on X (formerly twitter) https://twitter.com/larry_martin29 and LinkedIn https://www.linkedin.com/in/larry-martin-142b528/

 

 

 

References

Chu, S. L., Garcia, B. M., & Rani, N. (2023, November). Research on wearable technologies for learning: a systematic review. In Frontiers in Education (Vol. 8, p. 1270389). Frontiers Media SA.

Covi, E., Donati, E., Liang, X., Kappel, D., Heidari, H., Payvand, M., & Wang, W. (2021). Adaptive extreme edge computing for wearable devices. Frontiers in Neuroscience, 15, 611300.

Harley, J. M., Pekrun, R., Taxer, J. L., & Gross, J. J. (2019). Emotion regulation in achievement situations: An integrated model. Educational Psychologist, 54(2), 106-126.

Nagar, T. (2023). Top 6 Things You Should Know About IoT In The Education Industry https://elearningindustry.com/top-things-you-should-know-about-iot-in-the-education-industry/amp

Sandner, P., Gross, J., & Richter, R. (2020). Convergence of blockchain, IoT, and AI. Frontiers in Blockchain, 3, 522600.

Van der Meer, A. L., & van der Weel, F. R. (2017). Only three fingers write, but the whole brain works†: a high-density EEG study showing advantages of drawing over typing for learning. Frontiers in psychology, 8, 248612.

Vilaplana-Pérez, A., Pérez-Vigil, A., Sidorchuk, A., Brander, G., Isomura, K., Hesselmark, E., ... & de la Cruz, L. F. (2021). Much more than just shyness: the impact of social anxiety disorder on educational performance across the lifespan. Psychological medicine, 51(5), 861-869.