Adaptive Learning

What is Adaptive Learning?     

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     Adaptive learning refers to a process of learning in which the content taught and/or the way it is taught adapts based on an individual student’s ability or knowledge. It helps to personalize instruction and works at the pace of the learner (Oxman & Wong, 2014). While initially adopted in the K-12 education system it is now beginning to expand into higher education. Adaptive learning involves software technology that adjusts what is presents to the learner based on observing their previous behaviours to ensure they have learned the content (Feldstein, 2013).

     There are three components to adaptive learning; a content model, a learner model and an instructional model. A content model describes how the content is structured including what is to be learned, proposed sequencing and learning outcomes. The learner model is how the system is able to adjust to the learner’s needs and performance. It tracks the student’s knowledge estimates their ability level on different topics. Finally, the instructional model determines how the system chooses which content the student will learn next based on the information from above (Oxman & Wong, 2014).

Challenges

     While adaptive learning presents many opportunities it also comes with its own challenges. Most often these challenges are not associated with the technology, but within the structures that are working to implement it. An example of this is what happens to term lengths if some of the class is able to finish the content in significantly less time than others. Another common challenge identified is the significant training required for faculty, students and support personnel to implement the transition to this type of learning (Oxman & Wong, 2014).

Application & Opportunities

     In opposition of the challenges there are also many predicted opportunities and benefits to adopting adaptive learning.

• Professors are able to provide personalized exam review for students and differentiated learning experiences based on the data collected about their learning needs and gaps in understanding of the course content (Green-Lerman, 2015).

• Adaptive Learning could significantly reduce instructor and teaching assistant compensation budgets as students are able to receive personalized lessons and assistance based on their specific needs (Bowen et al., 2012).

• With improvements in computing technologies information on student’s progress can be available instantly allowing a professor to provide a targeted approach to students who need extra assistance early in a semester (Oxman & Wong, 2014). This could help reduce drop-out rates.

• In current models, not every student can afford a private tutor. The individualized approach of adaptive learning could make this support available to all students (Feldstein, 2013).

There is a positive but slow movement towards the integration of adaptive learning due to the many benefits to students, the institution, and faculty. The earliest implementation drivers are textbook publishers looking for opportunities to enhance their offerings. Companies such as McGraw-Hill are pairing adaptive learning software with textbooks to make their product both more appealing and beneficial to faculty looking for course texts (Oxman & Wong, 2014). This added-value may spark a greater shift in higher education to adaptive learning.

References

Bowen, W. G., Chingos, M. M., Lack, K. A., & Nygren, T. I. (2012). Interactive learning online at public universities: evidence from randomized trials. New York: ITHAKA.

Feldstein, M. (2013, December). What Faculty Should Know About Adaptive Learning. Retrieved from http://mfeldstein.com/faculty-know-adaptive-learning/  

Green-Lerman, H. (2015, March). How Instructors Use Adaptive Assignments in the Classroom. Retrieved from http://www.knewton.com/blog/adaptive-learning/how-instructors-use-adaptive-assignments-in-the-classroom/

Oxman, S. & Wong, W. (2014, February). White Paper: Adaptive Learning Systems. Retrieved from http://snapwiz.com/wp-content/uploads/2014/03/DVx_Adaptive_Learning_White_Paper.pdf