How do machine learning techniques make the “new personalized learning ” possible?
Machine learning technologies can support higher education students by designing a personalized learning path that is unique to each student, and caters to their interests and strengths. Current challenges in higher education institutions include “disengaged students, high dropout rates, and the ineffectiveness of a traditional “one-size-fits-all” approach to education” (Rouhiainen, 2019). Personalized learning paths, designed through artificial or machine learning technologies, have the opportunity to support student learning by providing just-in-time remedial learning opportunities when a student is struggling with a concept. Additionally, these technologies can deepen the level of student learning when the student shows competency in a particular subject.
Personalized learning has the opportunity to engage students and expand upon their interests. The traditional method of teaching to the middle can be updated so that each student receives the type of education they need, as an individual, to be successful. Personalized learning would provide students with the immediate feedback they need to support their learning process through the use of machine learning technologies (Khurana, 2018). Students could leave school excited to learn, with a focused mindset on continuing education that expands upon the knowledge they learned in the classroom.
Should personalized learning be applied to all topics in the K-20 curriculum? Why or why not??
We’re not ready yet. The impact of the COVID-19 pandemic, and the quick pivot to emergency remote instruction thrust society into the realm of online learning sooner than expected. Over the two years of the pandemic teaching and learning was turned on it’s ass, and we uncovered the good and the bad, and now we need to reflect and think about where to go from here.
Pertaining to personalized learning, how do we measure that the students are receiving an equal education? Among many things, the pandemic also brought to the forefront the impact of equity in student learning. Some students were without devices, some without internet, some within a roof over their heads, etc. How can personalized learning also be equitable when each student is receiving a unique learning path?
In the case of higher education, if two students are pursuing the same degree but one student struggles with the content and receives hours of remediation content, and the second student is successful in their studies and receives the standard amount of content, or has the ability to dive deeper into the content; how is this equal? In the end, is it fair that both students receive the same degree when they had a different experiences?
Additionally, how would the student competencies be monitored? Western Governors University was under fire for its mode of competency-based education, which has similar attributes to the personalized learning paths. When it comes to accreditation and Title IV (financial aid), it was determined that Western Governors University did not meet the inspector general’s requirements and was facing a potential fine of close to $1 billion (Fain, 2017). This is a larger topic than the prompt for this post, but how will personalized learning paths be fair when competency-based education failed?
Finally, don’t get me started on FERPA. Data leaks are per the norm in today’s society, and adding to the database of student data collection may not be wise at the moment. What if a potential employer discovered that their future employee required remediation? What if that discovery lost that individual the job?
Nope. We’re not ready.
Resources
Fain, P. (2017, September 22). Education dept.’s inspector general calls for Western governors to repay $713 million in Federal Aid. Education Dept.’s inspector general calls for Western Governors to repay $713 million in federal aid. Retrieved September 18, 2022, from https://www.insidehighered.com/news/2017/09/22/education-depts-inspector-general-calls-western-governors-repay-713-million-federal
Khurana, S. (2018, February 6). Personalized learning through artificial intelligence. Medium. Retrieved September 18, 2022, from https://medium.com/swlh/personalized-learning-through-artificial-intelligence-b01051d07494
Rouhiainen, L. (2019, October 14). How AI and data could personalize higher education. Harvard Business Review. Retrieved September 18, 2022, from https://hbr.org/2019/10/how-ai-and-data-could-personalize-higher-education
