8. Learning Portfolios on Blockchain Digital Ledgers: Securing Personal Academic Data in the 21St Century

 

By Larry G. Martin

 

Blockchain digital ledgers offer cutting-edge opportunities for adults to safely manage the massive amounts of personal data, academic transactions, and professional relationships generated from Web2.5 and 3.0 technologies in their digital learning hubs. As a type of shared distributed digital ledger technology (DLT), an increasing number of education and training organizations (such as MIT Digital Diplomas, and Maryville University) are replacing traditional paper-based ledgers with blockchain technology to document, verify, and share data about students’ knowledge and skills. Historically, traditional ledgers are the mortar that binds together the diverse organizations, departments, programs, and services learners have trusted to maintain their academic records and transactions. However, the blockchain technologies responsible for cryptocurrencies (e.g.,  Bitcoin and Ethereum) can increasingly provide adult learners futuristic opportunities to construct viable, decentralized, convenient, learner-owned and controlled, and easily accessible digital learning portfolios (DLPs) to document their education, training, and competencies. Nevertheless, the challenge of replacing traditional ledgers requires a massive and complex undertaking by educational and training organizations and compels adult learners to adopt several blockchain-related apps to their digital learning hubs.

 

Traditional Ledgers in 21^st Century Education

In the 21^st century, traditional paper-based ledgers are becoming obsolete. Lifelong learners now create complex data trails as they participate in various education and training organizations throughout their careers. They thereby accumulate numerous achievements in degrees, certifications, and badges that are documented for individual students within “siloed” data structures in separate educational institutions (U.S. Office of Educational Technology, 2021). These data are routinely collected during each phase of the journey as individuals complete high school; engage in different college, training, and learning opportunities; and participate in numerous employment situations throughout their lives. Still, each organization is responsible for deciding what information is recorded; the conditions for recording, modifying, and verifying the accuracy of transactions; and which transactions should be deleted or destroyed (Grech & Camilleri, 2017). Therefore, for new employment or educational opportunities, adults must submit individual requests to a growing list of different organizations, which must also abide by FERPA protections of student records (U.S. Office of Educational Technology, 2021).

 

Trusting these fragile and disorganized paper-based ledger systems in the 21^st century is like leaving the keys to the family home in unreliable hiding spots, such as under door mats. Like these keys, students’ data and information are unsafe. They risk being lost, damaged, copied, manipulated, or destroyed (Delgado-von-Eitzen et al., 2021). Through digital learning portfolios, blockchain technologies promise to shift control over education and training data from education, workforce, and employer stakeholders to individual learners, workers, and citizens (Lemoie & Soares, 2020).

 

Personalized digital learning portfolios of educational and training competencies can assist adults in keeping pace in today’s rapidly changing work environments, where about 65 percent of all jobs require a post-secondary credential. About 738,428 unique credentials (such as degrees, certificates, digital badges, and apprenticeships) are used in labor market decision-making to conduct transactions such as college admissions or transfers, recruiting, hiring, and promoting employees (Lemoie & Soares, 2020). However, the half-life of the knowledge and skills learned in college is now just five years, and the average time spent on a job is about 4.5 years (Lemoie & Soares, 2020). Through learning portfolios, blockchain digital ledgers provide pathways for education data to accompany learners throughout their lives, experiences, and achievements; and for individuals to document, verify, and efficiently share skills-related data with current and potential employers.

 

Blockchain Digital Ledgers

Blockchain digital ledgers allow learners to place their academic credentials and files in a series of interconnected systems of highly secure safe deposit boxes where each academic record, transaction, and credential is protected by cryptographic locks, ensuring integrity and preventing unauthorized changes. These ledgers typically contain lists of sequential, time-stamped transactions identifying a transaction number, date and time, sender, asset, and receiver (Grech & Camilleri, 2017). The ledgers can empower learners to access their digital credentials immediately without contacting the issuing parties. They also ensure the academic records are

(a)  tamper-proof (Palanivel, 2019);

(b)  quickly verified by employers, educational institutions, and other stakeholders (Lemoie & Soares, 2020);

(c)  owned and controlled by students who can share their credentials with third parties without relying on intermediary institutions (Lemoie & Soares, 2020); and

(d)  recognized globally, making it easier for adults to study and work internationally.

 

These benefits notwithstanding, adult learners seeking to develop a blockchain-based learning portfolio should consider adopting a blockchain app to their digital learning hub.

 

Types of Blockchains

Adult learners should consider several types of blockchain apps: public, private, and consortium.  

 

Public blockchains (such as Bitcoin, Ethereum, Cardano, and Dock) are permissionless and open to anyone seeking to be part of the peer-to-peer network (Delgado-von-Eitzen et al., 2021). They are less efficient than private blockchains in processing power and storage requirements and have slower transaction speeds (Delgado-von-Eitzen et al., 2021).

 

Private blockchains (such as Hyperledger Fabric, Ethereum Enterprise, and R3 Corda) are highly centralized, smaller, and more specialized systems that require permissions. Participants can join only if invited, and they must abide by the organization's rules controlling the network (Delgado-von-Eitzen et al., 2021).

 

Consortium blockchains (such as Polkadot) represent a combination of public and private networks controlled by a group of organizations, and participants can only join if they are invited. Nonetheless, transaction volume, speed, and resources usage are improved when compared to public systems (Delgado-von-Eitzen et al., 2021). Most adult learners will likely find that utilizing a public blockchain (e.g., Ethereum) that is used by many education organizations is a trusted system on which to create personal digital learning portfolios.

 

Creating a Digital Learning Portfolio on the Blockchain

A digital learning portfolio (DLP) is a verifiable record of your learning achievements,

credentials, and skills stored on the blockchain. To take full advantage of the increasing number of innovative blockchain products available through educational organizations (such as MIT Digital Diplomas , ODEM, and BitDegree) you will need to create a DLP that enables the trading of assets across blockchain networks via a wallet or light wallet (Grech & Camilleri, 2017).

 

You can start by selecting and installing (for free) an electronic Wallet app (such as Coinbase Wallet, MetaMask, and Trust Wallet) or a Light Wallet (such as Wallet of Satoshi) directly onto a digital device (a computer or smartphone) to receive and send cryptocurrency and manage personal data (Grech & Camilleri, 2017). When installed, the wallet creates a key pair. The public key is openly shared and used to encrypt messages or data, and the private key should be kept secret and used for decryption (Lemoie & Soares, 2020).

 

Digital wallets can also be hot or cold. Hot wallets (MetaMask or Trust Wallet) are user-friendly, app-based systems connected to the internet that are convenient for daily transactions, like sending and receiving cryptocurrencies. The cost of these transactions (such as Ethereum gas fees) depends on the network. However, hardware-based cold wallets like Ledger Nano S or Trezor are not internet-connected. They are thereby more secure against online threats (hacking, phishing, and malware) and more suitable for long-term storage of large amounts of cryptocurrency. Yet, they are less convenient for frequent transactions because they require connecting the device to a computer or smartphone to access funds, and private keys are stored offline. Once you purchase a wallet, there are no ongoing costs for storing cryptocurrencies, but there are network transaction fees when transferring funds. For security (backup and recovery), a seed phrase must be created, stored, and protected securely.

 

Inexperienced adult learners should start with a hot wallet (like MetaMask) and verify their identity for everyday transactions. Once your wallet app is installed, you should select an app for a blockchain credentialing platform (such as Blockcerts) and purchase compatible cryptocurrency funds (from Ethereum [ETH], or others) that can be used to cover transaction fees for issuing, storing, and verifying your digital credentials on the blockchain. Since many blockchain credentialing platforms, like Blockcerts, operate on the Ethereum network, purchasing ETH is recommended. To purchase a cryptocurrency, you will need a small initial investment of about USD 50.00 worth of ETH to cover several transactions and provide enough experience with the process. Transfer the purchased cryptocurrency from the exchange to the MetaMask wallet (or any other wallet being used). Your initial investment should cover over 90 transactions; however, Ethereum gas fees fluctuate, so you should always check current rates and perform transactions when fees are lower.

 

Once your hot wallet is operational, your future educational and learning plans should prioritize platforms, courses, and training programs (such as Blockcerts, EduChain, EduCTX, Sony Global Education, and Hyland Experience Credentials) that issue blockchain-based certificates or credentials. They will produce digital copies of blockchain-based credentials that are recorded on the blockchain and can be easily stored in your digital wallet, making them effortlessly verifiable by employers or other institutions. However, these credentials should be well organized in your DLP to ensure easy access. Some organizational categories to consider include:

·      Formal Education—degrees, diplomas, and academic certifications.

·      Professional Education—industry-specific credentials, skill badges, and licenses.

·      Work Experience—verified employment history, freelance work, and internships.

·      Non-formal Education—courses, workshops, and training programs from non-traditional institutions.

·      Skill Portfolios—projects, portfolios, or any evidence of skill mastery.

 

In general, cryptocurrencies enjoy a dubious reputation in the public media as instruments of financial investments; however, many educational and learning organizations are actively exploring how the underlying technology can improve digital ledgers. By adopting blockchain technology apps to your digital learning hub and developing your own DLP, you will help accelerate the widespread availability of blockchain digital ledgers. You will also be empowered to more safely share your credentials with potential employers, academic institutions, or other relevant parties by providing access to your electronic wallet or a specific public address. As you complete additional learning experiences and gain new skills, you should continuously add new credentials and achievements to your DLP. Lastly, as the volume of transactions increases, you should consider transitioning to a cold wallet to enhance the security of your digital assets.

 

Up Next: Personalizing Digital Hubs to Enhance and Accelerate Learning

This blog series has examined impactful Web3.0 tools adult learners can add to their digital hubs, from large language model AI chatbots to blockchain digital ledgers. My next blog will discuss how adults can personalize the organization and integration of the tools in their learning hubs to enhance and accelerate their learning efforts.

 

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

Awaji, B., Solaiman, E., & Albshri, A. (2020, July). Blockchain-based applications in higher education: A systematic mapping study. In Proceedings of the 5th International Conference on Information and Education Innovations (pp. 96-104).

Daley, S. (2019). 9 Blockchain Education Companies Earning Straight A's https://builtin.com/blockchain/blockchain-education

Delgado-von-Eitzen, C., Anido-Rifón, L., & Fernández-Iglesias, M. J. (2021). Blockchain Applications in Education: A Systematic Literature Review. Applied Sciences, 11(24), 11811.

Grech, A., & Camilleri, A. F. (2017). Blockchain in education. Luxembourg: Publications Office of the European Union.

Lemoie, K., & Soares, L. (2020). Connected impact: Unlocking education and workforce opportunity through blockchain. American Council on Education.

Palanivel, K. (2019). Blockchain Architecture to Higher Education Systems. Int. J. Latest Technol. Eng. Manag. Appl. Sci, 8, 124-138.

U.S. Office of Educational Technology (2021). The Lifelong Learner: How Blockchain Solutions Can Facilitate Data Transfer and Protect Personal Information for a Lifetime. https://tech.ed.gov/files/2021/02/blockchain-lifelong-learner.pdf

 

7. Big Data and Learning Analytics: Empowering Adaptive Classroom Learning

 

By Larry G. Martin  

Innovations in how educational and training organizations identify, collect, store, analyze, and report students’ digital data pave the way for Web3.0 personalized adaptive learning opportunities. Many higher education institutions are concerned that over 40.4 million Americans have enrolled in college but did not receive a degree (Bryant, 2024). Struggling academically in required courses, often in huge lecture halls, about 24% of first-time undergraduate students drop out of college, and 29.2% drop out after six years (Bryant, 2024). For students, traditional lecture-based classroom teaching is like reading a printed book with fixed content presented in the same order, regardless of the reader’s comprehension level. Classroom teachers, therefore, are seldom aware of the difficulties many students experience in these inflexible learning environments. Also, many organizations seek to make trainer-led employee training more productive and efficient in corporate training. A possible solution is found in the large volumes of big data generated as adults employ tools, e.g., computers, smartphones, Internet of Things devices, etc., from their digital learning hubs to access the digital apps from adaptive learning technologies sponsored by educational organizations (Peng & Spector, 2019).

Digital Apps Collect Diverse Sources of Student Data

Education and training organizations have adopted a variety of digital platforms that assist them in collecting and analyzing large volumes of data about learner interactions. When adults enroll in courses, they’re typically required to adopt several digital apps to assist with organizational enrollment (e.g., admissions, tuition and fees, and course registration) and educational experiences (e.g., course participation, levels of engagement, and academic performance). These apps generate vast amounts of diverse student data from various activities. For example, the Web3.0 Learning Management System (LMS) platform app can collect data from learners' interactions with course materials, assessments, and online discussions. Data from organization-based apps for Student Information Systems (SIS) include demographic, enrollment, academic performance, and financial records information. By monitoring students’ comments, likes, and shares related to educational content in Social Media apps and Online Forums, these organizations can gain insights into learners' preferences, interests, and peer interactions. Apps on students’ Wearable Devices can capture data on students’ physical activities, and data on students’ preferences for temperature, lighting, and other learning environmental conditions can be monitored via an organization’s intelligent Classroom Sensors. Lastly, Web3.0 Adaptive Learning Platforms routinely collect data on learners' interactions, preferences, and performance, which can be shared with educational providers. However, the data from these sources must be systematically collected to aid students’ learning processes.

Three Levels of Big Data Collected by Educational Organizations

Students’ digital footprints in the form of Big Data are collected and used at three broad levels of education and training organizations. At the microlevel, clickstream data are collected automatically during interactions between learners and their respective learning environments (e.g., coursework on LMSs and engagements with adaptive learning systems) (Fischer et al., 2020). Millions of second-by-second digital data points for each student provide detailed information on students’ cognitive strategies, affective states, or self-regulated learning (SRL) behaviors. Instructors can obtain actionable knowledge by assessing which actions are appropriate for different subgroups or student profiles (Fischer et al., 2020). At the Mesolevel, text data are systematically collected within minutes to hours during online writing activities. Obtained from online discussion forums, website databases, and social media interactions, these data provide insight into students’ levels of engagement, understanding, cognitive and social abilities, and affective states (Fischer et al., 2020). Educational organizations routinely collect and analyze Macrolevel organization-wide student data once or twice per term (Fischer et al., 2020). These data provide insight into each student’s demographic profile, admissions information, course enrollments, and degree completion. Although they have powerful potential, these data are powerless if not properly analyzed.

Educational Data Mining and Learning Analytics
Advancements in Web3.0-based learning analytics technologies have made it easier and cost-effective for educational organizations to use the enormous volumes of students’ data to benefit learners. Learning analytics involves measuring, collecting, analyzing, and reporting students' data to optimize learning experiences (Lee et al., 2020). The analysis of students’ data is like studying unorganized puzzle pieces. Educational data mining (EDM) provides a process for systematically organizing these unorganized data pieces into a complete, real-time, and accurate picture of learners and their learning efforts in specific classes. Using algorithms, EDM can discover hidden patterns in adult learners' behavior, learning styles, and preferences. They extract useful information from large datasets to identify patterns, relationships, and trends to create profiles of students' learning behaviors and patterns. These profiles are used to personalize and optimize the educational experiences of each learner.

Johnson et al. (2016) identified six key data points that are often included in the creation of learners’ profiles: demographic information (age, gender, ethnicity, and socio-economic background); academic records (grades, test scores, and completed courses); learning preferences, such as preferred learning styles (visual, auditory, and kinesthetic) and favored types of resources (videos, readings, and interactive simulations); behavioral data (time spent on tasks, participation in discussions, and engagement levels); psychological motivational traits (levels of self-efficacy); and social interactions (levels of collaboration and interaction with peers and instructors). The real-time profiles of each learner created from these data pave the way for innovative adaptive learning systems to customize courses with individual student characteristics (Zlatkovic et al., 2020).

Smart Adaptive Learning Systems

Intelligent adaptive learning systems are digital innovations designed to individualize and personalize the learning content so that students can experience self-governed, autonomous learning while taking courses (Posner, 2017). Adaptive learning (or teaching) is the real-time adaptation of instructional strategies, learning content, and educational resources based on the ongoing analysis of learner data. Integrated with AI, machine learning, and new data analytical techniques, these systems can now monitor the activities of learners, interpret their digital data following specific models that explain their actions, and continuously make micro-adjustments in real time to improve student’s performance and engagement levels (Posner, 2017). By seamlessly adjusting the sequence and difficulty of the learning tasks based on student’s abilities and preferences, these platforms create personalized content and custom learning experiences. They make learning more accessible by adapting to the learning strategies of individual students and altering and modifying the sequence and difficulty learners experience based on students’ demonstrated proficiencies (Zlatkovic et al., 2019). Educational and training organizations are increasingly adopting intelligent adaptive learning platforms as they attempt to scale the benefits of one-on-one personal tutoring to entire classes of learners.

 

Smart Adaptive Learning Platforms

Compared to lecture-based teaching, intelligent adaptive learning platforms are like using an interactive e-reader that allows for a personalized reading experience. The font size of the text and background color can be adjusted, and readers can be more engaged as they highlight, take notes, get instant definitions, and receive engaging and tailored recommendations based on their progress and interests. Accordingly, education organizations seek to emulate the advantages of an e-reader by adopting a wide variety of intelligent adaptive learning platforms to deliver recommended courses and customized learning experiences that align with individual academic interests or career development goals. For example, several universities (such as Northern Arizona University, Texas Tec University, and the University of Alabama) have adopted McGraw-Hill’s Open Learning Solutions as a blended learning approach that combines traditional face-to-face instruction with adaptive learning technologies. The types of existing systems include:

• Web3.0 Learning Management Systems (LMSs) (such as Blackboard and Canvas) can facilitate the seamless integration and data exchange between different educational platforms and dynamically adjust content by recommending additional resources or altering the sequence of course materials based on learner interactions and progress.

• Microlearning Platforms (such as Connecteam,  Whatfix, EdApp, iSpring Learn, and others) enable microlearning for employees by providing short and focused training content.

• Learner Engagement Platforms (such as Teachfloor, Orah, Google Workspace for Education, kahoot, and others) provide a collection of tools to help instructors, trainers, and course creators capture students’ inputs and track their performance to develop personalized learning experiences for their students (Teachfloor).

• Adaptive Learning Systems (such as Knewton Alta, Smart Sparrow, and SmartBook) provide authoring platforms for instructors and course designers, and assist universities or departments in developing a curriculum based on adaptive learning (Johanes & Lagerstrom, 2017).

 

User Interface to Track and Monitor Learners’ Progress

Most intelligent adaptive learning systems include a dashboard allowing learners and teachers to monitor and track their progress. Each customized interface is accessed through smartphones, computers, headsets, and other digital devices, as it manages the interactions and communications between students and the learning system. For example, data visualization tools (such as Tableau and Power BI) can transform complex data into intuitive visual formats and help learners and educators understand patterns and trends in educational data, making it easier to make informed decisions.

Selecting Adaptive Systems for Your Digital Hub
Because educational and training organizations require students to adopt adaptive learning systems apps for classes after selecting an academic organization to pursue educational and training opportunities, learners often need help deciding which apps to add to their digital hubs. However, it would be best to be keenly mindful that most systems currently providing smart adaptive learning opportunities do not use Block Chain technology to secure learners’ data. While adaptive systems offer vast and exciting opportunities for gathering valuable insights for both learners and educational providers, before enrolling in courses, you should be sure ethical considerations are in place to ensure your privacy and security in collecting and using your personal data.

Up Next: Blockchain Technology

The tremendous amounts of personal data generated from Web3.0 tools in adult learners’ digital toolkits should be protected. My next blog post will discuss the transition to blockchain technology to manage and secure students’ data.

 

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

Bryant, J. (2024). College dropout rate in the U.S. https://www.bestcolleges.com/research/college-dropout-rate/#:~:text=In%202022%2C%2029.2%25%20of%20students,college%20experience%20but%20no%20degree.

Fischer, C., Pardos, Z. A., Baker, R. S., Williams, J. J., Smyth, P., Yu, R., ... & Warschauer, M. (2020). Mining big data in education: Affordances and challenges. Review of Research in Education, 44(1), 130-160.

Johanes, P., & Lagerstrom, L. (2017, June). Adaptive learning: The premise, promise, and pitfalls. In 2017 ASEE Annual Conference & Exposition.

Johnson, L., Adams Becker, S., Estrada, V., Freeman, A., Johnson, L., AdamsBecker, S., ... & Freeman, A. (2016). NMC Horizon Report: 2016 Higher Education Edition. Austin, Texas: The New Media Consortium. Retrieved August, 20, 2016.

Lee, L. K., Cheung, S. K., & Kwok, L. F. (2020). Learning analytics: Current trends and innovative practices. Journal of Computers in Education, 7, 1-6.

Peng, H., Ma, S., & Spector, J. M. (2019). Personalized adaptive learning: an emerging pedagogical approach enabled by a smart learning environment. Smart Learning Environments, 6(1), 1-14.

Posner, Z. (2017) What is Adaptive Learning Anyway? https://www.mheducation.com/news-insights/blog/what-is-adaptive-learning.html

Zlatkovic, D., Denic, N., Petrovic, M., Ilic, M., Khorami, M., Safa, A., ... & Vujičić, S. (2020). Analysis of adaptive e‐learning systems with adjustment of Felder‐Silverman model in a Moodle DLS. Computer Applications in Engineering Education, 28(4), 803-813.

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

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