13. The Case of Duolingo Digital Language eLearning Platform and Mobile App

This episode is part of the Series Adult and Higher Education FutureTech. It is for practitioners, administrators, and researchers in adult and higher education. In this episode, we explore how the explosive growth of mobile learning is disrupting traditional AHE delivery systems and provide an example of Duolingo, a digital eLearning platform and mobile app. 

• What are your experiences with mobile learning?
• What do you believe will be the future of mobile learning?
• To what extent will mobile learning complement, compete with, or replace traditional learning systems?

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 AHE FutureTech Episode 13 Transcript

 

References

Chai, W. (2020). Microlearning (microtraining). TechTarget. https://www.techtarget.com/whatis/definition/microlearning

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

Intelligent tutoring systems. (2022). https://en.wikipedia.org/wiki/Intelligent_tutoring_system

Jiang, X., Chen, H., Portnoff, L., Gustafson, E., Rollinson, J., Plonsky, L., & Pajak, B. (2021). Seven units of Duolingo courses comparable to 5 university semesters in reading and listening.

Jimenez L., & Boser U., (2021). Future of Testing in Education: Artificial Intelligence. https://www.americanprogress.org/article/future-testing-education-artificial-intelligence/ (Downloaded, 6/2/2022)

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

Spaced repetition. (2020). https://en.wikipedia.org/wiki/Spaced_repetition

Stephenson, B. (2021). What Are Smart Clothes? Discover how your clothes can improve your life. https://www.lifewire.com/what-are-smart-clothes-4176103

Stevenson, M. (2020).  Data That Proves the Continued Importance of Employee Learning. https://www.corporatelearningnetwork.com/learning-design/articles/stunning-corporate-learning-stats

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.

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.

Vázquez-Cano, E. (2014). Mobile distance learning with smartphones and apps in higher education. Educational Sciences: Theory and Practice, 14(4), 1505-1520.

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

 

 

12. The Creation of Smart Learning Environments Through the Emergence of eLearning Platforms

This episode explores how the creation of smart learning environments through the explosive growth of digital eLearning platforms are disrupting traditional Adult and Higher Education delivery systems. 

• What are your experiences with digital eLearning platforms?
• What do you believe will be the future of these types of educational and learning delivery systems?
• To what extent will these delivery systems complement, compete with, or replace traditional educational organizations and institutions?

 

Listen to the Podcast

AHE FutureTech Episode 12 Transcript 

 

References

 

Harman, M. (2021). The Role of Digital Learning Platforms in the Academic Growth of Students. https://kitaboo.com/the-role-of-digital-learning-platforms-in-the-academic-growth-of-students/ (downloaded, 6/18/ 2022).

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

Shoikova, E., Nikolov, R., & Kovatcheva, E. (2017). Conceptualising of smart education. Electrotech. Electron. E+ E, 52.

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.

10 Best Online Learning Platforms In 2022 To Jumpstart Your Career. https://sites.google.com/site/videoblocksreview/online-learning-platforms

 

 

 

 

 

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. 

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