3. Distance Education Alternatives for Lifelong Learning: Web2.5 and 3.0 AI-Powered Online Digital Learning Platforms

 

By Larry G. Martin, PhD

 

Adult learners seeking to improve their knowledge or skills should consider adding one or more computer-based online digital learning platforms to their digital toolkits. These platforms are important alternatives to place-bound educational options. In the future, they are expected to become a preferable education and learning mode for self-study and life-long learning (Wang et al., 2020). Innovative Web 3.0 digital tools (e.g., artificial intelligence, machine learning, and others) are progressively being adopted by existing online platforms which emerged in the mid-1990s (Kentnor, 2015). These new tools create emergent Web2.5 platforms as they are grafted onto traditional online systems (including Massive Open Online Courses [MOOCs] such as Coursera and Udacity, and microlearning platforms like Skill share). Web 2.0 and 2.5 are the fastest-growing form of distance education, as evidenced by their adoption by 65% of colleges and universities (Kentnor, 2015). However, a transformative group of fully Web3.0 digital learning platforms are featuring a fuller array of Web3.0 tools and striving to make adult education and learning more affordable, decentralized, and trustworthy. Therefore, adult learners can now choose from various digital learning platforms.

 

Existing Web2.0 Platforms as Alternatives to Traditional Classrooms

Existing Web2.0 platforms were pioneered as alternatives to traditional classroom settings and offered online course marketplaces comprised of educational and learning customers, advertisers, service providers, producers, and suppliers (Nichols & LeBlanc, 2020). Like digital bookstores, these platforms offer educational content spanning various topics, and they have provided adult learners with increased flexibility, mobility, and affordability. E-learning is now a fundamental part of the student learning experience in adult and higher education (Urh et al., 2015). However, like off-the-shelf ready-made clothes, these platforms are pre-designed and generalized to fit a broad range of learning needs. Their business models differ in the extent to which they provide: free vs. paid membership, live teachers and/or coaches, general vs. specialized classes, quality instructional staff, certification of knowledge/skills, and recognition by accredited educational organizations. While some learners may locate what they need, many others face challenges in finding courses or learning pathways that align with their personal needs, costs, learning styles, and goals.

 

Consequently, the infrastructure of Web2.0 digital platforms has not been congruent with the needs and expectations of all online students (National University, 2023). For some students, the ineffective management of personal time has inhibited them from putting in maximum efforts towards learning. Also, untimely delays and the lack of timely and consistent interactive communication with teachers has caused frustration among students. These frustrations have been exacerbated by the inability of many students to receive timely feedback on completed assignments. Dispirited adult learners who witnessed a lack of engagement and fuzzy expectations from online teachers (National University, 2023) may reconsider these platforms after considering several emergent Web2.5 design changes. 

 

Emergent Web2.5 Learning Platforms

Emergent Web2.5 online learning platforms are embedding Web3.0 digital tools into existing Web2.0 platforms to provide more dynamic and effective educational environments for adult learners. Like specialized clothing stores offering alterations to off-the-rack pants, dresses, and suits, many traditional online learning platforms are transitioning to employing innovative Web3.0 tools to tailor their offerings. Nevertheless, they retain some of their existing Web2.0 structural configurations where a single authority owns both the platform and students’ data. Yet, they employ Web3.0 tools to offer innovative features (such as personalization); prediction of learning status, performance, or satisfaction; resource recommendations; and automatic assessments (Ouyang et al. 2022). Here are some examples of how AI assistants, gamification, adaptive learning, and VR and AR are being deployed to optimize several Web2.5 online digital platforms.

 

1.    Artificial Intelligence (AI) Assistants are providing learners personalized guidance, answers to questions, and targeted learning recommendations. Learner-centric information is being provided by context aware AI-powered chatbots that understand what content a learner is researching. Multiple-choice questions, essay responses, and rapid feedback are being provided by AI-automated grading systems to improve responsiveness. Also, personalized assessments and helpful feedback are being provided by AI-driven intelligent tutoring systems (Diwan et al., 2023). For example, Udemy utilizes AI to provide personalized course recommendations to learners; recommend specific video lectures within courses based on each learner’s occupational goals; and locate specific content so that learners can more quickly translate their unique knowledge and approaches into effective learning.

2.    Elements of Gamification include digital badges, leaderboards, or progress tracking, that can make learning more engaging, enjoyable, and motivating for adult learners (Belford, 2023). For example, Coursera incorporates social learning communities, gamification elements (e.g., badges), and adaptive learning technologies to provide learners personalized recommendations based on their progress and preferences.

3.    Adaptive Learning Technologies use real-time data analytics and algorithms to adjust instructional content, pacing, and delivery methods to provide optimized learning experiences to all students. For example, Pluralsight incorporates adaptive learning technologies to track learners' performance and skill levels and provide personalized content and recommendations.

4.    Virtual Reality (VR) and Augmented Reality (AR) technologies create immersive learning experiences by simulating real-world environments or overlaying digital content onto the physical world. For instance, Udacity incorporates VR simulations and projects to help learners enrolled in its courses and nanodegree programs to practice skills, explore complex concepts, and engage in hands-on learning in a realistic virtual environment.

 

These Web2.5 innovations in online learning platforms provide more flexible, engaging, and impactful learning options for personal digital learning hubs. Yet, they contain only some key futuristic features essential for transformative Web3.0 platforms.

 

Transformative Web3.0 Learning Platforms

Transformative Web3.0 platforms are designed to provide futuristic solutions to current and potential issues expected from the expanding usage of Web3.0 technologies. It is expected that without safeguards or interventions, Web 3.0 technologies will eventually concentrate a massive amount of digital data and power into the hands of a few centralized authorities and organizations (Belford, 2023). Through the decentralization of power and information individual learners can be empowered to take personal control of their data. Second, as learning organizations gather more and more personal data from platform users, Web3.0 technologies will unwittingly amplify the opportunities for unauthorized surveillance and increase the possibility of AI-generated discriminatory conduct (Belford, 2023). Personal data should thus be encrypted and controlled by individual students. Third, learning platforms can lead to feelings of isolation and should encourage learner engagement. Like a tailored suit, Web3.0 platforms address these concerns by encouraging learner engagement through decentralization, personal data control, peer-to-peer interaction, and ownership.

 

Fully Web3.0 digital learning platforms are being developed in targeted markets as alternatives to existing platforms. For example, Education Ecosystem is a project-based platform for professionals and college students interested in futuristic technology topics such as artificial intelligence, cybersecurity, game development, and data science. Alternatively, Studyum is being developed to address global learning barriers such as inequality, location, and unconventional learning styles that obstruct the ability of people worldwide to reach their full potential (Belford, 2023).

 

Education Ecosystem and Studyum are decentralized and utilize blockchain technology to record verified transactions (such as academic transcripts, credit transfers, or grade reports) to allow for greater transparency, personal data control, and security. Through distributed ownership, blockchain allows learners to engage course content and materials by incorporating real-time interactions, gaming elements, peer-to-peer learning, and tokenized rewards for participating on the platform (Ma, 2018). Both Education Ecosystem and Studyum use nonfungible tokens (i.e., LEDU BEP20 and  STUD) as unique digital assets that provide value, flexibility, and vitality to the learning process. As digital medals, certificates, or smart contracts, tokens can symbolize learner achievements that learners can track, trade, or redeem. They allow learners to participate in the platform's governance and own part of the platform.

 

Selecting Online Learning Platforms

Computer-based Web2.5 platforms are strong candidates for addition to digital learning hubs. They offer a broad range of topics, proven track-records, and improvements via Web3.0 technologies. Transformative Web3.0 platforms seem to be in the experimental design stage of development. Learners concerned about personal data security, and the centralization of power, should perhaps take a preemptive step into the future by adopting one or more of these platforms.

 

Up Next: Mobile E-Learning Platforms and Gamification

In my next blog post, I analyze mobile e-learning platforms’ key features (e.g., gamification), and which platforms 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

Belford, D.T. (2023). The Revolutionary Impact of Web3 In eLearning. https://elearningindustry.com/revolutionary-impact-of-web3-in-elearning/amp

Diwan, C., Srinivasa, S., Suri, G., Agarwal, S., & Ram, P. (2023). AI-based learning content generation and learning pathway augmentation to increase learner engagement. Computers and Education: Artificial Intelligence, 4, 100110.

 Ma, S. (2018). Using blockchain to build decentralized access control in a peer-to-peer e-learning platform (Doctoral dissertation, University of Saskatchewan).

National University (2023). Challenges of Distance Learning for Students https://www.nu.edu/blog/challenges-of-distance-learning-for-students/

Nichols, T. P., & LeBlanc, R. J. (2020). Beyond apps: Digital literacies in a platform society. The Reading Teacher, 74(1), 103-109.

 Ouyang, F., Zheng, L., & Jiao, P. (2022). Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020. Education and Information Technologies, 27(6), 7893-7925.

 Urh, M., Vukovic, G., & Jereb, E. (2015). The model for introduction of gamification into e-learning in higher education. Procedia-Social and Behavioral Sciences, 197, 388-397.

Wang, X., Chen, W., Qiu, H., Eldurssi, A., Xie, F., & Shen, J. (2020, November). A Survey on the E-learning platforms used during COVID-19. In 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) (pp. 0808-0814). IEEE.