Friday, May 6, 2022

11. Evolving Institutional Models in Response to Education 4.0


The escalating pace of technological innovation experienced during Industry 4.0 has drastically changed the nature of employment. It has catapulted us into a knowledge-based economy. Some effects of this emerging economy can be observed in the widespread lack of skilled workers, and in the increased difficulties many companies are experiencing filling job vacancies for the skilled trades. This episode explores how adult and higher education institutions are innovating their organizational structures to accommodate the learning needs of displaced workers, adult learners, and students seeking competitive advantages during Industry 4.0. Join in the online forum to follow the discussion how adult and higher education intuitions are innovating their organizational structures to accommodate the learning needs of displaced workers, adult learners, and students seeking competitive advantages during Industry 4.0. 

Friday, April 29, 2022

10. Academic Records Management and the Transition to Blockchain Technology

Blockchain technology is disrupting the academic record-keeping and influencing processes for maintaining student information systems in adult and higher education organizations. Listen to our podcast to learn more about blockchain technology, key design features, and some of the ways adult and higher education organizations are currently deploying it.

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Other Ways Blockchain Technology is Impacting AHE

  • Educational Resources. These resources (e.g., learning resources in MOOCs for teachers and learners, share time-release exam questions, etc.) that can be safely shared and protected from unauthorized access, copied, modified or even deleted.
  • Assessments/Exams. Student assessments and exams can be provided through automated exams and assessments when certain conditions are met.
  • Awarding Tokens. Organization-based tokens have been used for fees, credits and currency transfer. For example, university-specific cryptocurrency (e.g., Kelvin coins) have been provided to the best students.
  • Smart Contracts. These contracts can be used to build flexible blockchain based distribution solutions for the benefit of all participants in an online learning system, including students teaching staff, and administrative personnel. They can be used for managing and storing student data such as a self-executed code that applies roles and conditions between two or more parties. These include support services applications through digital transactions using a cryptocurrency such as Bitcoin, and digital certificate applications that provide students control of earned certificates, and decreased dependance on adult and higher education institutions for storing, verifying, and validating credentials.
  • Accreditation. A process for assessing and ranking educational institutions according to the academic results of their learners.


Friday, April 22, 2022

9. Big Data, Educational Data Mining, and Learning Analytics


Educational data mining and learning analytics use big data generated from Learning Management Systems and organizational computer systems to track learner connections and interactions. These analytics can inform adult and higher education decision making and planning related to levels of learner attrition, course failures, and repeat classes. This episode explores how big data, educational data mining, and learning analytics are influencing how instructors assess and evaluate students’ learning behaviors and develop interventions in the interest of optimizing both the learning efforts and environments of learners.

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  • Aguilar, S. J. (2018). Learning analytics: At the nexus of big data, digital innovation, and social justice in education. TechTrends, 62(1), 37-45.
  • Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing Teaching and Learning through Educational Data Mining and Learning Analytics: An Issue Brief. Office of Educational Technology, US Department of Education.
  • Conn, R. (2020). Seven Key Differences Between Data Analytics and Data Mining. Accessed on 2-10-2021.
  • Gedrimiene, E., Silvola, A., Pursiainen, J., Rusanen, J., & Muukkonen, H. (2020). Learning analytics in education: Literature review and case examples from vocational education. Scandinavian Journal of Educational Research, 64(7), 1105-1119.
  • Herodotou, C., Rienties, B., Boroowa, A., Zdrahal, Z., & Hlosta, M. (2019). A large-scale implementation of predictive learning analytics in higher education: the teachers’ role and perspective. Educational Technology Research and Development, 67(5), 1273-1306.
  • Lee, L. K., & Cheung, S. K. (2020). Learning analytics: Current trends and innovative practices. Journal of Computers in Education, 7(1), 1-6.
  • Picciano, A. G. (2012). The evolution of big data and learning analytics in American higher education. Journal of asynchronous learning networks, 16(3), 9-20.
  • Picciano, A. G. (2014). Big data and learning analytics in blended learning environments: Benefits and concerns. IJIMAI, 2(7), 35-43.
  • Secades, V. A., & Arranz, O. (2016). Big data & eLearning: A binomial to the future of the knowledge society. IJIMAI, 3(6), 29-33.
  •  Yupangco, J., (2020). The Essential Guide to Learning Analytics in the Age of Big Data.

Friday, April 15, 2022

8. Internet of Things and Adult and Higher Education


The Internet of Things is a ubiquitous system containing billions of everyday devices that have Internet connected embedded sensors, microcontrollers, and software that monitor and record activity (such as sound, movement, and temperature). This episode explores how Internet of Things is impacting the teaching and learning experiences of faculty and students in adult and higher education organizations.

  • What are your experiences with Internet of Things (for example, smart pens, smart glasses, or augmented reality)? How are they impacting your teaching or learning?
  • How is your organization transitioning into becoming a digital campus? 
  • How is the Internet of Things being used in the classroom with adult learners?


  • AjazMoharkan, Z., Choudhury, T., Gupta, S. C., & Raj, G. (2017, February). Internet of Things and its applications in E-learning. In 2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT) (pp. 1-5). IEEE.
  • Attallah, B., & Ilagure, Z. (2018). Wearable technology: Facilitating or complexing education. International Journal of Information and Education Technology, 8(6), 433-436.
  • Kortuem, G., Bandara, A. K., Smith, N., Richards, M., & Petre, M. (2012). Educating the Internet-of-Things generation. Computer, 46(2), 53-61.
  • Negahban, M. B., & Selvaraja, A. (2019). The Application of Interactive and Intelligent Web in E-Learning. Interdisciplinary Journal of Virtual Learning in Medical Sciences, 10(4), 75-77.
  • Ogallo, G. G. (2018). IoT–Enhancing Data-driven Decision-making in Higher Education. Case Study of Ohio University (Doctoral dissertation, Ohio University).
  • Saeed, M. K., MUNIR, A., SHAH, K., HASSAN, M. U., KHAN, J., & NAWAZ, B. (2021). USAGE OF INTERNET OF THINGS (IOT) TECHNOLOGY IN THE HIGHER EDUCATION SECTOR. Journal of Engineering Science and Technology, 16(5), 4181-4191.
  • van Deursen, A. J., van der Zeeuw, A., de Boer, P., Jansen, G., & van Rompay, T. (2021). Digital inequalities in the Internet of Things: differences in attitudes, material access, skills, and usage. Information, Communication & Society, 24(2), 258-276.

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Friday, April 8, 2022

7. Chatbots and Holograms and Adult and Higher Education


Several spatial web digital technologies are currently disrupting the teaching and learning environments of adult and higher education organizations. This episode explores how chatbots and digital holograms are impacting learning applications in adult and higher education.

  • What personal experiences have you had with chatbots and digital holograms in educational and learning situations?


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Bonfield, C. A., Salter, M., Longmuir, A., Benson, M., & Adachi, C. (2020). Transformation or evolution?: Education 4.0, teaching and learning in the digital age. Higher Education Pedagogies5(1), 223-246.

Mavrikios, D., Alexopoulos, K., Georgoulias, K., Makris, S., Michalos, G., & Chryssolouris, G. (2019). Using Holograms for visualizing and interacting with educational content in a Teaching Factory. Procedia Manufacturing31, 404-410.

Pates, D. (2020). The holographic academic: Rethinking telepresence in higher education. In Emerging technologies and pedagogies in the curriculum (pp. 215-230). Springer, Singapore.

Riel, J. (2021). Essential features and critical issues with educational chatbots: Toward personalized learning via digital agents. In Handbook of Research on Modern Educational Technologies, Applications, and Management (pp. 246-262). IGI Global.

Tsivitanidou, O., & Ioannou, A. (2020). Users' Needs Assessment for Chatbots' Use in Higher Education. In Central European Conference on Information and Intelligent Systems (pp. 55-62). Faculty of Organization and Informatics Varazdin.

Turk, H., & Seckin Kapucu, M. (2021). Innovative Technology Applications in Science Education: Digital Holography. Journal of Education in Science, Environment and Health7(2), 156-170.

Friday, April 1, 2022

6. The Shift to Innovation Producing Education

Education 4.0 is a major new development in the missions and instructional practices of Adult and Higher Education organizations as they transition from knowledge producing to innovation producing education. The purpose of this episode is to explore: (1) the meaning of Education 4.0 and the shift to innovation producing education, (2) The skills for digitally literate lifelong learners, and (3) the development of personalized intelligent learning pathways for all students. 


  • What does the shift to innovation producing education mean to you?
  • How will the trend toward assisting students to become digitally literate lifelong learners impact your practices?
  • Share examples of interventions currently being used and how your organization is implementing personalized intelligent learning pathways for all students.

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Bonfield, C. A., Salter, M., Longmuir, A., Benson, M., & Adachi, C. (2020). Transformation or evolution?: Education 4.0, teaching and learning in the digital age. Higher Education Pedagogies, 5(1), 223-246.

Bekmanova, G., Ongarbayev, Y., Somzhurek, B., & Mukatayev, N. (2021). Personalized training model for organizing blended and lifelong distance learning courses and its effectiveness in Higher Education. Journal of Computing in Higher Education, 1-16.

Ehlers, U. D., & Kellermann, S. A. (2019). Future skills: The future of learning and higher education (pp. 2-69). Karlsruhe.

Friday, March 25, 2022

Key Terms, Descriptions, and Definitions

We have used a few terms in our postings with which you may not be familiar. This posting contains key terms, descriptions, and definitions. 

Big Data Analytics and Artificial Intelligence allow factories to analyze the data derived from the digital connections between virtual and physical worlds and quickly optimize their operations.  

Cloud Computing and 5G allows smart factories to store, process, and share data with greater flexibility at a lower cost than traditional on-premises alternatives. Interconnected devices and machines can quickly upload large amounts of data that can be processed to provide feedback and make decisions in near real-time.

Cyber-Physical Systems (CPS) are collections of physical and computer components that are integrated with each other and controlled or monitored by computer-based algorithms. The most advanced of these systems are Intelligent Products. For example, fully autonomous manufacturing robots can produce patterns, reason, learn, anticipate events, and link with larger networks.

Digital Twin (DT) technology creates in virtual spaces, high-fidelity, parallel, 3-D virtual digital images of physically existing environments, processes, or individual objects. It simulates the behaviors of the objects in the real world and provides feedback. By quickly and accurately detecting and predicting physical issues with CPS systems, it contributes to the optimization of manufacturing processes.

Human-Machine Interface (HMI) is the user interface that allows operators to connect the controller for an industrial system. The controllers are integrated software designed to monitor and control the operation of machinery and associated devices in industrial environments.

Industrial Internet of Things (IIoTs). These are interconnected sensors, instruments, devices, machines, and/or processes that are linked by data communication systems to enable the exchange and use of data between people and machines. They allow higher levels of automation and production optimization by collecting and storing data on a cloud for tracking, data exchange, and analysis.


11. Evolving Institutional Models in Response to Education 4.0

  The escalating pace of technological innovation experienced during Industry 4.0 has drastically changed the nature of employment. It has c...