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

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.

 

5. Immersive AR, VR, and XR Environments: Facilitating Context-Based Learning and Education

 

Innovative Augmented Reality (AR), Virtual Reality (VR), and Extended Reality (XR) immersive technologies provide unparalleled opportunities to develop insightful context-based knowledge and skills when integrated into personal digital learning hubs. Available to the public since the 1980s and 1990s (Elmqaddem, 2019), these are Web2.0 era technologies that provide simulated real-world interactive scenarios and engaging immersive practice environments. Among adults, immersive technologies are increasingly being adopted and used for learning and work, and by 2030, 23 million jobs are projected to require their utilization (Wong & Humayoun, 2022). The appeal of these technologies originates with their ability to integrate both physical and virtual immersive interactive environments.  Employing approaches aligned with both constructivist (Vygotsky, 1978) and experiential learning (Dewey, 1938), users experience a sense of context awareness (Shoikova et al., 2017) as they experiment, manipulate objects, and make discoveries that construct knowledge and aid their understanding.

 

With the availability of Web3.0, these immersive technologies allow learners to be absorbed in self-contained artificial or simulated practice environments while experiencing them as real. Supported by integrated systems of devices that engage a user’s senses, immersive environments can present both learners and workers with rich, varied, and complex learning content while also assisting them in sharpening their technical, creative, and problem-solving skills. However, the types of virtual learning environments differ regarding the degree of immersion provided or the number and form of technical features needed (Rauschnabel et al., 2022).

 

Web2.5 Virtual Reality (VR)

Web2.0, virtual reality (VR) is like the experience of exploring the wonders of the ocean with others by visiting aquarium exhibitions siloed in Plexiglas-encased ocean environments. The outside world is shut out as you view a world under the sea with assorted varieties of fish, whales, sharks, stingrays, etc.  In contrast, VR systems integrated with Web3.0 technologies provide an interactive, more personalized, and dynamically changing viewing arrangement. In these environments, the spectators can interact with the creatures of the sea, creating a more optimized, immersive, and engaging experience. However, lacking the securities of blockchain technology, non-fungible tokens (NFTs), and other Web3.0 technologies, most VR educational and learning experiences today are akin to Web2.5.

 

Web2.5 VR systems require several integrated technologies to experience realistic images, sounds, and other sensations while fully immersed in a digital environment (Fernandes et al., 2023). Head-mounted displays (HMDs), like the Meta Quest 2, are needed to access a computer. A variety of sensors are available to experience touch (e.g., sensor gloves), track body movements (e.g., Omni One), or capture hand movements (e.g., Leap Motion). Therefore, the Web2.5 version of Virtual Reality is like a deep-sea diving expedition. Through software-generated realistic images, sounds, and other sensations, users are fully submerged in an ocean environment as they interact with whales, sharks, stingrays, porpoises, plants, currents, and other features. These contextualized interactive features appeal to training and education in diverse practice fields such as the military, medicine, and architecture.

 

Educational and training organizations are leveraging the benefits of VR for instructing learners in new, complex topics, and/or dangerous and unusual contexts:

1. Specialized virtual education and training environments have been designed to mimic high-risk and complicated practice settings, e.g., airplane cockpits, assorted military battlefields, chemical laboratories, and so on. After gaining theoretical knowledge, these context-based environments can be used to assist learners in sharpening their technical, creative, and problem-solving skills by applying their acquired knowledge to complete challenging tasks (Kamińska et al., 2019). Learners can safely simulate practice in highly complex, stressful, and potentially dangerous virtual environments, such as aviation flight training scenarios, military training exercises, and clinical practice interventions (Rizzo, 2013).
2. Mobile immersive classroom equipment allows educators and trainers to bring affordable, innovative virtual reality lessons into conventional classroom settings. For example, the mobile ClassVR unit connects to a portal (containing curriculum-linked 3-D virtual content, activities, and lessons) to teach practical skills following previously acquired knowledge (Kamińska et al., 2019). The unit provides standalone portable storage, sets of virtual reality headsets, wired hand-held controllers, and a portable charging station.
3. Specialized smart immersive classrooms use a Learning Management System of Virtual Context (LMSVC) to generate 3-D artificial virtual classroom displays of contemporary knowledge in a particular field and support the acquisition of theoretical knowledge such as terminology, dates, and scientific theories (Kamińska et al., 2019). Touch interactions allow learners to immerse themselves in virtual content using wireless sensors, haptics, and wearables (Memos et al., 2020). Augmented Reality learning opportunities are also viable options for educators and trainers.

 

Web2.5 Augmented Reality (AR)

Educators and trainers primarily use Augmented Reality (AR) as supplementary tools to promote students’ interactive experiences with coursework, encourage collaboration between students, improve motivation, and increase learning gains (Loveless, 2023). Through Web2.5 technologies, AR applications bring the virtual world into our own. It is like having a personalized underwater aquatic experience wherever you go, with digital elements from the ocean seamlessly integrated into your real-world environment (e.g., a shark on a loan company logo, or a sting-ray on a car) providing real-time information and creating new layers of interaction. By overlaying computer-generated augmentations on top of the real world, AR enhances (but does not replace) reality.

 

Educators are increasingly providing access to AR simulations in classroom and training situations. Some examples include:

1. Commercially available AR apps like Snapchat, Google Lens, and Math Solver can be paired with smartphones. These applications can enhance learning by blending digital components into the real world to enhance one another but remain easily distinguishable. For example,  Mondly provides 3-D language learning through a virtual teacher. It assists learners to practice their language skills seamlessly and interactively in various context-based settings such as coffee shops, and vacation travel.  
2. Smartphones can be paired with VR headsets (such as Oculus Quest 2) to access app-based 3-D immersive content (such as Unimersiv and Google Earth VR) (Bookwidgets Blog, 2021). 
3. Digital knowledge-sharing platforms (such as JigSpace) are centralized hubs for creating, storing, and sharing information via AR smartphone applications, which can be accessed by students, participants, and trainees.

 

While AR applications can be highly creative and insightful, the immersive learning experiences they provide pales in comparison to the promise of learning in the Metaverse.

Web3.0 Extended Reality (XR) and the Metaverse

Still under construction, the Metaverse offers a futuristic vision for Web3.0 immersive Extended Reality (XR) learning and training applications, which will increasingly shape the way people teach and learn in the modern world (Sutikno & Aisyahrani, 2023). Extended Reality applications allow learners to explore virtual environments, manipulate digital objects, collaborate with peers in remote locations, and engage in hands-on simulations that enhance understanding and retention of complex concepts. Learning in the Metaverse is like being submerged within a constantly evolving and interactive deep-sea experience where the physical and digital worlds seamlessly blend, allowing for deeper engagement, real-time collaboration, and experiential learning that breaks boundaries between traditional and digital spaces.

 

The emergent metaverse contains many self-sustaining synthetic digital worlds. Like a digital Oklahoma land rush, individuals and organizations aggressively use cryptocurrencies and blockchain transactions to purchase and secure ownership of expensive, commercially available tracts of digital space to create specialized worlds. Each world is comprised of user-controlled avatars, digital things, and 3D immersive virtual environments (e.g., university campuses, classrooms, conferences, workshops, libraries, and other computer-generated elements (Wang et al., 2022). For example, VictoryXR partners with over 20 higher education institutions to develop digital twin replicas of their campuses and provide various interactive courses in the Metaverse. Similarly, education and training investments in the metaverse can secure the provision of interactive, collaborative, personalized, safe, and adaptive learning experiences for high-risk training scenarios across geographical barriers (Srivastava, 2023). Using mixed reality Head-Mounted Display (HMD) devices, such as Microsoft's HoloLens or Apple Vision Pro in real time, adult learners (represented by avatars) can see and interact with virtual objects integrated into the real world while also being aware of, and interacting with, the physical environment as they tour campuses, take classes, attend conferences, participate in workshops, complete assignments, collaborate, and socialize with each other (Wang et al., 2022).

 

Selecting Immersive Learning Experiences

My reading of the digital tea leaves suggests your future as an adult learner will likely involve knowing how to use immersive technologies for education and training. Each application discussed in this Blog provides distinct advantages you should consider adding to your digital learning hub. However, creating functional applications can be expensive, and some people may experience discomfort when using connective devices. Also, creating digital twins of yourself in the Metaverse could create identity theft concerns as your digital twin includes your personal information. You should experiment with different immersive learning applications to ensure they are safe, secure, affordable, maintainable, and easy to use.

 

Up Next: The Internet of Things and Wearables

In my next blog post, I will analyze the contributions the Internet of Things and Wearables are making to learning and which of these should be considered for adult learners’ digital toolkits. 

 

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

Beck, D., Morgado, L., & O'Shea, P. (2020). Finding the gaps about uses of immersive learning environments: a survey of surveys. Journal of Universal Computer Science, 26, 1043-1073.

Bookwidgets Blog, (2021). 20 Powerful virtual reality apps for your classroom of the future. https://www.bookwidgets.com/blog/2021/01/20-powerful-virtual-reality-apps-for-your-classroom-of-the-future (Downloaded 1/12/2023).

Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., ... & Zhang, Y. (2023). Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712.

Dewey, J. (1938). Experience and education. New York, NY: Macmillan.

Elmqaddem, N. (2019). Augmented reality and virtual reality in education. Myth or reality?. International journal of emerging technologies in learning, 14(3).

Fernandes, F. A., Rodrigues, C. S. C., Teixeira, E. N., & Werner, C. (2023). Immersive Learning Frameworks: A Systematic Literature Review. IEEE Transactions on Learning Technologies.

Kamińska, D., Sapiński, T., Wiak, S., Tikk, T., Haamer, R. E., Avots, E., ... & Anbarjafari, G. (2019). Virtual reality and its applications in education: Survey. Information, 10(10), 318.

Loveless, B (2023). Using Augmented Reality in the Classroom. https://www.educationcorner.com/augmented-reality-classroom-education.html (Downloaded, 1-23-2023)

Rauschnabel, P.A., He, J., Ro, Y. K. (2019). Antecedents to the adoption of augmented reality smart glasses: a closer look at privacy risks. Journal of Business Research, 92, pp. 374–384.

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4. Innovative Mobile Learning Technologies: Facilitating Just-in-Time Learning

 

 

Adults experiencing situational learning barriers (such as lack of employer support for learning, difficult work schedules, and/or family responsibilities) (Roosmaa & Saar, 2017) should add innovative mobile learning technologies to their digital learning hubs. Through the ubiquitous availability of mobile devices, adults increasingly engage in mobile learning (M-learning) using smartphones and small-screen devices. M-learning enables them to address situational learning barriers by accessing information independently of time and space and managing their own learning processes based on their individual differences (Talan, 2020). Through location-aware mobile devices, learning opportunities are immediately available and personalized to learners’ interests and needs (Bruck et al., 2012). They place students at the center of the teaching and learning process by prioritizing individual differences and helping learners analyze and synthesize information (Talan, 2020). With the widespread use of mobile apps on small-screen devices, new mobile learning opportunities are increasingly available to adults seeking to overcome situational barriers.

 

The Embrace of M-learning

The M-learning apps on mobile computing devices present an exciting new frontier in adult education. They offer flexibility, self-paced learning, access to global knowledge pools, and personalized content to deliver a unique learner-centered education (Talan, 2020). Students’ learning can take place anywhere throughout the day; and focus on work demands, self-improvement, or leisure (Wang et al., 2019). However, adults need access to these technologies to engage in M-learning.

 

Effective M-learning requires three essential elements. First, adults must have access to mobile learning devices (such as small, portable devices equipped with wireless communication abilities, strong computational power, and context-aware tools) (Wang et al., 2019). In 2021, most American adults owned these devices: 85 percent owned smartphones, and 50 percent owned tablets (Pew Research Mobile Factsheet, 2021). Second, adults need access to an appropriate communications infrastructure (such as the Internet) that can connect their mobile computing devices to relevant learning materials and/or other learners (Wang et al., 2019). About 93 percent of American adults use the Internet, and 75% have broadband Internet service at home (Pew Broadband Research, 2021). Third, adults should have access to learning activities, in traditional classrooms, outside the classroom, or in informal learning contexts (Wang et al., 2019). Through ownership of mobile devices and access to the Internet, most American adults can pursue learning activities through mobile learning apps.

 

Web2.5 Mobile Learning Apps       

Historically, mobile apps are Web2.0 tools available since the early 1990s. These legacy apps were like traditional radio stations that broadcasted the same songs or shows to all listeners without personalization. There was no real interaction; everyone received the same information simultaneously and in the same sequence. Similarly, adults could be frustrated using Web2.0 mobile apps. They only allowed learners to receive the same content in the same format and sequence, regardless of their individual learning preferences and needs.

 

Many M-learning apps are now more appealing to busy adults. They are successfully integrating elements of Web3.0 technologies that allow adults to gain online access to interactive learning materials, micro-learning, personalization, simulations, voice and image recognition capabilities, and rich educational games (Damyanov & Tsankov, 2018). These Web2.5-oriented apps are like a streaming service resembling Spotify that provides more highly personalized experiences. Through AI, they understand learner needs and preferences and recommend more personalized, efficient, and engaging learning experiences based on each person’s behavior, preferences, and performance. These empowering capacities have also gained the attention of established educational organizations, as mobile apps are now essential in educational courses (Talan, 2020).  Consequently, the worldwide market for M-learning apps is expected to grow from $7.98 billion U.S. dollars in 2015 to $325 billion by 2025 (Wang et al., 2019). This explosive growth has produced a dizzying array of desktop-first and mobile-first apps available to assist adults in learning a large quantity and variety of content.

 

         Desktop-First Mobile Apps. One way to make sense of mobile apps' confusing number and variety is to categorize them by the devices for which they were originally designed. For example, some desktop-first apps were designed specifically for desktop and laptop computers, allowing learners more extensive, in-depth, and longer learning sessions. Learners can use a mouse, keyboard, and large computer screens to engage a broad range of content for 30 minutes or more. Consequently, most mobile versions of desktop-first apps tend to be retrofitted into more compact mobile designs.

 

Desktop-first mobile apps are often available from a broad range of credit (and non-credit) courses in established education and training organizations supported by Learning Management Systems (such as Blackboard, Canvas, and Moodle). Similarly, these apps are available from digital learning platforms that support many general courses (such as Coursera, Udemy, LinkedIn Learning); literacy courses (such as Learning Upgrade); and language learning courses (such as Rosetta Stone). Many of these are fee-based platforms and typically require enrollment to access their mobile apps. Other platforms, including Khan Academy, TED Talks, and Sololearn, provide free mobile learning apps on various topics and assist learners in developing a wide range of knowledge and skills. These desktop-first mobile apps tend to have more limited functionality than desktop applications; however, they often have Web2.5 features (such as personalization and adaptive learning). A fuller range of Web2.5 mobile learning features is generally more apparent in mobile-first learning apps.

 

Mobile-First Learning Apps. Mobile-first learning apps (such as Babbel and Duolingo ) have been specifically designed for small-screen mobile learning applications but often provide access via desktop and laptop computers. They ensure that the apps are optimized for mobile devices through responsive designs, simplified layouts, and mobile-friendly interactions. Device-specific interactions and capabilities like touch gestures, sensors, and push notifications ensure that mobile-first app users can initiate swipe gestures for navigation, utilize the device camera for augmented reality features, or use push notifications for reminders about upcoming lessons or assignments. Also, location-based features can provide localized learning experiences. This can include location-specific content or recommendations based on the learner's location. Many of these features are integrated into the Duolingo Mobile app.

 

The Duolingo Mobile-First Learning App. The Duolingo mobile-first language learning app illustrates how using Web2.5 technologies can empower learners. It operates under a freemium business model and provides the core services of the app free of charge. Yet, it seamlessly integrates personalization, gamification, and adaptive learning experiences based on a user’s individual abilities, preferences, progress, and learning style (Viktor, 2021; Marr, 2020). Through smart learning environments, the platform uses several innovative approaches to keep learners updated and engaged on the app:

 

·      Intelligent tutoring systems enhance content based on learners' needs, strengths, and weaknesses (Marr, 2020). They provide helpful, personalized feedback, hints, and guidance during learning.

·      Mobile-Based microlearning units optimize the duration, focus, and benefits of learning into bite-sized learning modules, lessons, and short quizzes, that can be completed in a few minutes (Moore et al., 2024).  

·      Spaced repetitions optimize learners’ memories of targeted content by providing scheduled reviews of personalized language lessons over longer intervals (Marr, 2020; Tabibian et al., 2019).

·      Stealth assessment reduces learner anxieties by intentionally blurring the distinction between assessment and learning so that the learner is oblivious to the assessment process (Georgiadis et al., 2020).

 

Notwithstanding these innovative interventions, the Duolingo mobile app does not fully utilize the depth of Web3.0 technologies.

 

Web3.0 Mobile Apps

Fully Web3.0 mobile apps are scarce but still emerging. Nevertheless, one example is the BitDegree platform, which provides a mobile app for learning and exploring knowledge about blockchain technology. The platform offers many courses on cryptocurrencies and provides numerous educational materials to assist learners interested in digital currencies and blockchain technology. It utilizes blockchain tools (such as tokens and certificates) to track learning achievements transparently, award scholarships, and provide incentives.

 

Selecting M-learning Apps

As you consider mobile apps for adoption into your digital learning hub, you should ponder the extent to which you have access to appropriate M-learning tools and the types of apps suitable for your learning needs. Top of mind should be desktop-first and mobile-first Web2.5 learning apps capable of providing relevant learning experiences. Fully Web3.0 mobile apps are still in the development stage. You should be mindful of adopting developmental technology.

 

Up Next: Immersive Learning in Augmented and Virtual Reality Environments

In my next blog post, I analyze the key features of AR and VR environments and the tools that should be considered for adult learners’ digital tool kits. 

 

 

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

Bruck, P. A., Motiwalla, L., & Foerster, F. (2012). Mobile learning with micro-content: a framework and evaluation.

Damyanov, I., & Tsankov, N. (2018). Mobile apps in daily learning activities. iJIM, 12(6).

Georgiadis, K., van Lankveld, G., Bahreini, K., & Westera, W. (2020). On the robustness of stealth assessment. IEEE Transactions on Games, 13(2), 180-192.

Marr, B. (2020) The Amazing Ways Duolingo Is Using Artificial Intelligence To Deliver Free Language Learning. https://www.forbes.com/sites/bernardmarr/2020/10/16/the-amazing-ways-duolingo-is-using-artificial-intelligence-to-deliver-free-language-learning/?sh=16c276275511

Moore, R. L., Hwang, W., & Moses, J. D. (2024). A systematic review of mobile-based microlearning in adult learner contexts. Educational Technology & Society, 27(1), 137-146.

Talan, T. (2020). The effect of mobile learning on learning performance: A meta-analysis study. Educational Sciences: Theory and Practice, 20(1), 79-103.

Roosmaa, E. L., & Saar, E. (2017). Adults who do not want to participate in learning: A cross-national European analysis of their perceived barriers. International Journal of Lifelong Education, 36(3), 254-277.

Tabibian, B., Upadhyay, U., De, A., Zarezade, A., Schölkopf, B., & Gomez-Rodriguez, M. (2019). Enhancing human learning via spaced repetition optimization. Proceedings of the National Academy of Sciences, 116(10), 3988-3993.

Viktor (2021). The Duolingo Business Model – How Does Duolingo Make Money? https://productmint.com/duolingo-business-model-how-does-duolingo-make-money/

Wang, Y. Y., Wang, Y. S., Lin, H. H., & Tsai, T. H. (2019). Developing and validating a model for assessing paid mobile learning app success. Interactive learning environments, 27(4), 458-477.