9. Personalizing Digital Hubs: Enhancing and Accelerating Learning

 

By Larry G. Martin

In the 21^st century, digital technology is a beacon of unprecedented access to smart, personalized tools for building learning hubs that can actively assist adults in enhancing and accelerating the achievement of their lifelong learning goals. By strategically adopting and integrating these tools into convenient and accessible hubs, adults can more effectively improve their knowledge, skills, and competencies (Yen et al., 2019). These digital learning hubs can proactively personalize learning pathways through LLM AI chatbots and adaptive learning platforms, which analyze individual learner data and tailor content to meet their unique needs. Accessibility and flexibility to pursue learning are afforded through mobile learning platforms and IoTs, which provide on-demand access and enable learners to fit learning smoothly into their demanding schedules. Extensive learner engagement is provided through immersive learning environments that transform abstract concepts into interactive experiences, enhancing understanding and retention. Lastly, trustworthy, portable, and secure credentials can be stored via blockchain ledgers that allow learners to own and transparently manage verifiable records of their achievements.

This constellation of independent technologies not only empowers learners but, when integrated with others, they evolve dynamically to meet adults’ lifelong needs. For example, to keep adults engaged and on task, continuous feedback loops can be created through IoTs and big data, which enable real-time monitoring of progress, thus allowing learners to self-correct as they advance and for educators/trainers to provide timely interventions. Therefore, to fully optimize these hubs, each learner should strategically build them by adopting the appropriate digital tools to address their distinctive formal, non-formal, and informal learning needs and goals. 

 

Different Pathways for Propositional and Procedural Knowledge

Adults pursuing propositional (i.e., fact-based) knowledge via formal learning goals should focus heavily on digital tools (such as, AI online digital learning platforms, big data and adaptive learning, LLM AI chatbots, and blockchain digital ledgers). These adults typically seek learning opportunities from hierarchically structured formal educational and training systems (such as university-based education, organizational training, etc.) (Johnson & Majewska, 2022). Through highly defined roles for educators and trainers, these systems rely on sequentially structured learning goals and grading systems (Johnson & Majewska, 2022). Although formal learning is important in lifelong learning, non-formal and informal learning are estimated to constitute 70-90% of lifelong learning (Yen et al., 2019).

Several digital tools (such as, LLM AI chatbots, mobile learning, immersive learning environments, IoTs, and wearables) often support non-formal learning of procedural (skill-based) knowledge involving real-world problems. Encouraging self-directed learning through organized and systematic instructional activity, it tends to occur outside the framework of formal educational systems (Johnson & Majewska, 2022). These tools provide access to non-direct teaching behaviors (e.g., facial expressions, tone of voice, gestures, etc.) that often occur in authentic, highly motivating, and engaging contextual environments. Many of these tools can also support informal learning by providing access to meaningful unstructured activities and unsystematic processes through which adults can acquire and accumulate knowledge, skills, attitudes, and insights to address perceived needs (Johnson & Majewska, 2022). These types of learning should be top-of-mind as adults build interdependent and interconnected learning hubs. However, they should first adopt the core technologies to anchor the system. 

 

Core Technologies to Anchor Personal Learning Hubs

Adults should consider adopting a robust, independent AI-powered personalized learning management system (LMS) to anchor their learning hub. An independent LMS can be a transformative tool that synergistically creates a learning ecosystem that evaluates their learning needs and preferences; tracks their progress; recommends courses and learning opportunities; adapts to their learning pace; identifies personalized formal, non-formal, and informal learning pathways; and allows design flexibility to upgrade and change technologies in accordance with changing learning needs.

 

Ideally, the system will incorporate AI features for personalized feedback and assessments while providing access to teaching and learning resources (e.g., MOOCs, tutorials, and multimedia tools). Therefore, learners should consider prioritizing systems with robust application programming interface (API) capacities for educational tools that allow strong LMS integration abilities and allow adopted third-party educational tools, platforms, and technologies to work seamlessly together. Also, reports issued by LMSs can provide comprehensive accounts of learners’ subject matter progress and inform adults at different stages and phases of their learning efforts about how well their learning needs are being accomplished. With the vast number of options available, adults should adopt an AI-powered system with the most desirable attributes to address their lifelong learning needs.

 

Adopting an AI-Powered Personalized LMS

 

Several different LMS platforms can be personalized as a central hub for formal, nonformal, and informal learning experiences. The OpenLMS platform is a robust blockchain-based LMS with highly flexible API access that integrates with various LMS platforms and educational tools to access multiple courses from universities and companies. For learners anticipating formal and informal learning opportunities, Coursera, LinkedIn Learning, and Skillsoft Percipio, should be considered. They are highly versatile AI-powered platforms that offer strong API capabilities (e.g., interoperability with chatbots and some immersive technologies). Through adaptive content and assessments, peer grading, and personalization, they can suggest the most relevant courses based on a learner’s past behavior and learning goals.

 

Similarly, WileyPLUS with ORION can be integrated with the LMSs employed by higher education and training organizations to provide adaptive practice learning paths, real-time analytics, and personalized study recommendations. Altoura should be considered for adults prioritizing experiential learning. Through AI-powered virtual simulations and immersive learning content, it simulates real-world scenarios. Through IoT integration, it leverages advanced wearable technology (such as VR headsets and smart devices) to deliver skill development in a variety of learning and educational settings. 

 

Case Examples: Creating Integrated Digital Learning Hubs

 

Based on their applied knowledge of digital learning tools and technology affordability, learners should select digital tools to optimize the achievement of their learning goals. Below are two hypothetical examples of learning hubs built to respond to job- and employment-related challenges.

 

Case 1: Customer Service Representative to Technical Support Specialist

 

Maria is a 25-year-old high school graduate and single mother living in an urban community. She is among the 1,554,799 customer service representatives currently employed in the United States. However, key customer service functions are now being replaced by AI-driven chatbots and virtual assistants (Phudech, 2024). At lower costs, these technologies can provide 24/7 service, answer inquiries, and successfully troubleshoot customers’ problems without human intervention (Phudech, 2024). Although she has a laptop computer, smartphone, and home Internet connection, Maria is among 31% of Americans who are “cautious clickers” (Horrigan, 2016) with limited eLearning experience. To upskill for her job in the service economy, she developed an affordable integrated digital learning hub to pursue certification as a technical support specialist. This new certification builds on her work experiences and prepares her to assist customers with technical issues related to a company’s products or services.

With limited digital technology experience and a low budget, Maria created a rudimentary and accessible learning hub on her computer. She focused primarily on formal learning and anchored her hub with the highly versatile Coursera LMS.  By integrating with a local college LMS platform, she gained access to a variety of free and paid self-paced courses and assessments that led to a technical support specialist certification upon completion. This type of integration enabled personalized learning paths, real-time insights, and tailored feedback while allowing her to maintain control over her learning data within a personal system. Mobile learning apps from the campus LMS enhanced Maria’s access to formal courses, allowing her to complete assignments and access educational resources anytime.

 

For the non-formal and informal learning technologies in her hub, she integrated Chat GPT-4o with Coursera to gain access to a wealth of knowledge across various topics. Acting as a virtual assistant, it provided immediate coursework support and guidance through instant conversational responses; accessed her course progress, performance data, and learning history to provide automated tutoring and support; and created personalized content by dynamically generating personalized quizzes, summaries, and feedback tailored to her learning progress. 

 

Case Study 2: Manufacturing Worker to Robotics Technician

 

Jamerson is a 40-year-old manufacturing worker with an associate degree in manufacturing technology. A father of three, he has spent the last 15 years in assembly line production. However, with the rise of automation and smart manufacturing technologies, including robotics capable of performing repetitive tasks faster and more precisely than humans, he is among the 13 million manufacturing workers faced with job displacement due to increasing automation on the assembly line (Moseman & McKittrick, 2024). For employment security, Jamerson aspires to upskill by becoming a robotics technician. Among17% of Americans who are “digital ready” (Horrigan, 2016) he has a strong Internet service, highly capable computer, and a proficient smartphone. He thereby developed a highly integrated and connected digital learning hub that offers speed, scalability, and seamless experiences to assist his transition.  

 

For formal learning activities, he anchored his hub with the Altoura LMS. Through robust API capabilities, he was able to link Altoura to a variety of digital technologies to support his learning journey. Connecting it to his company’s training LMS platform gave him access to company-related training sources. By integrating with college LMS platforms like Udemy, he consulted with academic advisors and enrolled in courses focused on robotics, Industrial Internet of Things (IIoTs), automation technologies, and programming languages such as Python and C++. These courses also exposed him to data analytics in manufacturing through the use of big data software applications (such as Netstock and Delmia Works) that he linked to Altoura to understand how companies collect, store, analyze, and interpret massive amounts of data generated from various sources like machine sensors, digital twins, and automated production lines and supply chains.

 

For the non-formal and informal learning technologies in his hub, he integrated Chat GPT-4o with Altoura to gain access to a highly knowledgeable virtual academic advisor and coach to assist his transformation. As an experiential learner, James integrated several immersive technology tools (such as Oculus for Business and STRIVR) with Altoura. This integration allowed him to experience complex robotic mechanisms, troubleshoot common machinery issues, and enhance his technical skills in a safe environment by simulating real-world interactive scenarios with virtual robots in manufacturing settings (Brynjolfsson & McAfee, 2014).

 

Conclusion

 

The individual technologies in learning hubs can provide personalized learning pathways, on-demand access to knowledge and information, interactive experiences, and secure storage and access to credentials. The above two hypothetical cases demonstrate how, when these technologies are integrated, digital learning hubs can create gestalt effects through which adult learners in highly varied life situations can receive unprecedented access and assistance in formal, nonformal, and informal learning situations. Consequently, well-designed hubs can provide transformational learning opportunities to adult learners when the tumult of technology innovations signals an urgent need to keep learning throughout the life course. 

 

References

 

Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & company.

Horrigan, John B. “Digital Readiness Gaps.” Pew Research Center, September 2016. Available at: http://www.pewinternet.org/2016/09/20/2016/Digital-Readiness-Gaps/

Johnson, M., & Majewska, D. (2022). Formal, Non-Formal, and Informal Learning: What Are They, and How Can We Research Them? Research Report. Cambridge University Press & Assessment.

Moseman, M. & McKittrick, S. (2024). How Automation Can Help Overcome Labor Shortages in Manufacturing. https://develop-llc.com/insights/how-automation-can-help-overcome-labor-shortages-in-manufacturing/

Phudech, P. (2024). AI and Smart Customer Services: Revolutionizing the Customer Experience. Journal of Social Science and Multidisciplinary Research (JSSMR), 1(3), 1-20.

Yen, C. J., Tu, C. H., Sujo-Montes, L. E., Harati, H., & Rodas, C. R. (2019). Using personal learning environment (PLE) management to support digital lifelong learning. International Journal of Online Pedagogy and Course Design (IJOPCD), 9(3), 13-31.

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.

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