This is an interesting reflection I wanted to share. In summary, the takeaways for me are the following:
- Machine learning and big data analytics are proliferating higher ed
- Their purpose is in complementing human skills and not replacing them
- Should not be thought as replacing “human social interactions”
- Striking the right balance is key
- Three aspects where machine learning and big data analytics can play a role: teaching and learning; predictive analytics; and student support.
- Risk: privacy
- According to the author the balancing act to reap the benefits, “higher education may have to either commit to a certain level of privacy invasion — students will have to volunteer more and more data to refine the models — or sacrifice certain analytic power to provide students the relative privacy they want to maintain.” but acknowledges that can be tricky.