Course Overview
Course pre-requisite(s): Basic understanding of learning process, data processing and AI.
The Automation of the Learning Process course is designed for learners and educators with the aim of gaining insights into how technology intervenes in the learning process and how this change affects the learning process and learners themselves. The special focus is on real-life examples of solutions that automate parts of the learning process and the role of artificial intelligence.
Learning Outcomes
The active participants would be able to:
Identify phases and levels of learning automation.
Discuss their impact on skill development and effective learning.
Evaluate various examples of learning automation.
Examine their advantages, disadvantages, and limitations.
Gain insights into the impact of automated learning on organizations and society.
In teams, create and evaluate proposals to enhance the real-life project.
Participants will be capable of identifying potential solutions applicable in a classroom setting.
Course Content
The course will cover learning theories, focusing on behaviorism and constructivism, degrees and levels of automation in the learning process, empirical evidence regarding the influence of higher levels of automation, recommender systems in education, and Course Signals—a predictive learning system developed at Purdue University in the USA. Additionally, the course will explore topics such as learning path determination, the application of Robot Nao, and phenomena like the filter bubble, pygmalion effect, learning resistance, defense, and the generation effect. These phenomena will serve as a basis for critical thinking about the automation of the learning process.
Instructional Method
The course is structured around lectures, discussions, debates, and group projects.
Required reading materials
Taxonomy of function allocation between human and automation:
Wickens, C. (2017). Automation Stages & Levels, 20 Years After. Journal of Cognitive Engineering and Decision Making, 12(1), 35–41. doi:10.1177/1555343417727438
Onnasch, L., Wickens, C. D., Li, H., & Manzey, D. (2014). Human performance consequences of stages and levels of automation: An integrated meta-analysis. Human factors, 56(3), 476-488.
Recommendation systems in education:
Deschênes, M. Recommender systems to support learners’ Agency in a Learning Context: a systematic review. Int J Educ Technol High Educ 17, 50 (2020). https://doi.org/10.1186/s41239-020-00219-w
https://educationaltechnologyjournal.springeropen.com/articles/10.1186/s41239-020-00219-w
Purdue Course Signals:
Course Signals, a predictive learning analytics system
Intro (3min)
Course Signals Explanation
https://www.youtube.com/watch?v=-BI9E7qP9jA
More details (1 hour)
Case study
https://analytics.jiscinvolve.org/wp/files/2016/04/CASE-STUDY-A-Purdue-University.pdf
Course Signals at Purdue: Using Learning Analytics to Increase Student Success
https://www.youtube.com/watch?v=kURsmrkdS04
Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59, 64-71.
https://d1wqtxts1xzle7.cloudfront.net/39893408/s11528-014-0822-x-libre.pdf?1447232769=&response-content-disposition=inline%3B+filename%3DLets_not_forget_Learning_analytics_are_a.pdf&Expires=1695995239&Signature=D1ikNY-Ny1EzIxoU78qp3pk5SAMwYpSdCo6AENxCRVUVPr5WV9bZwi8gTimqc7xBLVbllZ3NaInX6HIId9jS0OQ77s-Clv-jBM6sZ0ERuPt4Mp1hsQYzwB2xh0hngduXOBXzBqDBFKGUOVML7stCACGGiAjpgvtuyxsC1TbcSDOyIALKMBSNH5Lu8PqAAekWZECuLgucT6URfr6jQgBIZI1Wq6~DrHmNOzl65bUCSW~0Tjq75~qKYQ3W93CqLdcT3x~1Yz8I1YJP2xrSvGqaFQ~8seu7RfCfaETG6kXrDOrgqg2bWnBTE8FOVifnbfV4CP~KtoWHH8Y7ga4E2pRAnQ__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA
Criticism (30min)
What the Course Signals "Kerfuffle" is About, and What it Means to You
https://er.educause.edu/blogs/2013/11/what-the-course-signals-kerfuffle-is-about-and-what-it-means-to-you
Purdue’s Non-Answer on Course Signals
https://eliterate.us/purdues-non-answer-course-signals/
Learning path determination:
Zhao, C., & Wan, L. (2006, July). A shortest learning path selection algorithm in e-learning. In Sixth IEEE International Conference on Advanced Learning Technologies (ICALT'06) (pp. 94-95). IEEE.
https://support.duolingo.com/hc/en-us/articles/6448741924237-FAQ-Duolingo-s-new-learning-path
NAO robot:
Robaczewski, A., Bouchard, J., Bouchard, K., & Gaboury, S. (2021). Socially assistive robots: The specific case of the NAO. International Journal of Social Robotics, 13, 795-831.
https://constellation.uqac.ca/id/eprint/6272/1/Socially%20Assistive%20Robots.pdf
Tapus, A., Peca, A., Aly, A., Pop, C., Jisa, L., Pintea, S., ... & David, D. O. (2012). Children with autism social engagement in interaction with Nao, an imitative robot: A series of single case experiments. Interaction studies, 13(3), 315-347.
https://hal.science/hal-01265990/file/Autism_Studies_2012.pdf
Assessment
Assessment will be conducted by evaluating each phase of the group project completion. Each phase includes specific indicators, allowing for a comparison of students' projects to desirable values associated with those indicators. Additionally, after each phase, there is a planned discussion on the results and ideas for potential improvements.