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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AHE FutureTech Episode 10 Transcript 

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.

 

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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AHE FutureTech Episode 9 Transcript

 

References

• 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. https://www.codemotion.com/magazine/dev-hub/big-data-analyst/data-analytics-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. https://www.lambdasolutions.net/blog/essential-guide-to-learning-analytics-in-the-age-of-big-data-lf2

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?

 

Listen To Podcast

AHE FutureTech Episode 8 Transcript 

References

• 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.

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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 AHE FutureTech Episode 7 Transcript

 

References 

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.

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 Manufacturing, 31, 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 Health, 7(2), 156-170.

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.

Listen to Podcast

 AHE FutureTech Episode 6 Transcript

 

References

 

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.

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.

 

5. Digital Disruptions of Work and Skills Needed During Industry 4.0

 

This episode focuses on identifying who is most affected by Industry 4.0. It explores the concept of Smart Factories and how digital innovations are disrupting the manufacturing jobs of workers. It concludes by examining Learning Factories as an example of collaborative work-learning opportunities for current and future workers.

• What sectors of our population do you believe is (or will be) most affected by Industry 4.0?
• What are your experiences with Smart Factories? What impact will they have on the future nature of work?
• What are your experiences with Learning Factories? To what extent should they be adopted for the preparation of learners for jobs during Industry 4.0?
• What populations of adult learners will be most challenged to obtain quality employment opportunities in an era of Smart Factories and Learning Factories?
  

 Listen to Podcast

 AHE FutureTech Episode 5 Transcript

References

Burghardt, A., Szybicki, D., Gierlak, P., Kurc, K., Pietruś, P., & Cygan, R. (2020). Programming of industrial robots using virtual reality and digital twins. Applied Sciences, 10(2), 486.

 

Elbestawi, M., Centea, D., Singh, I., & Wanyama, T. (2018). SEPT learning factory for industry 4.0 education and applied research. Procedia manufacturing, 23, 249-254.

 

Mohammed, S. &  Fiaidhi, J. (2019). Cyber physical systems: A new frontier of artificial intelligence: Summary paper pp.23-30. http//dx.doi.org/10.14257/ijca.2019.12.1.03

 

Morgan, J. (2019). Will we work in twenty-first century capitalism? A critique of the fourth industrial revolution literature. Economy and Society, 48(3), 371-398.

 

Muro, M., Liu, S., Whiton, J., & Kulkarni, S. (2017). Digitalization and the American workforce. Organisation for Economic Co‐operation and Development. (2019). Preparing for the changing nature of work in the digital era.

 

Prinz, C., Morlock, F., Freith, S., Kreggenfeld, N., Kreimeier, D., & Kuhlenkötter, B. (2016). Learning factory modules for smart factories in industrie 4.0. Procedia CiRp, 54, 113-118.

 

Raff, S., Wentzel, D., & Obwegeser, N. (2020). Smart products: conceptual review, synthesis, and research directions. Journal of Product Innovation Management, 37(5), 379-404.

 

Tao, F., Qi, Q., Wang, L., & Nee, A. Y. C. (2019). Digital twins and cyber–physical systems toward smart manufacturing and industry 4.0: Correlation and comparison. Engineering, 5(4), 653-661.