How Adaptive Learning Can Make Higher Ed More Customized and Effective (Part 1)

— Co-Written with Karen Vignare | Associate Provost, University of Maryland University College—

What if an online learning environment could adapt to an individual student’s needs with data collected in real time about his or her ability level? Rather than being forced to learn at the average speed of the class, each student could take the time necessary to learn. For some, this would mean a shorter time to completion; for others, it would be an extended time to fill in holes in learning.

While not reflective of today’s traditional college classrooms, this is the promise of adaptive learning. Adaptive learning refers to tools or technologies that personalize learning. While a typical online learning environment delivered through a learning management system (LMS) assumes all students start at the same learning level and requires them to advance at the same pace, adaptive learning systems are designed to adapt content based on information about a learner. This can be based on learner preferences to tailor how content is delivered, the use of a pre-test to allow learners to test out of part of a course or continuously tailored learning delivery based on a student’s actions at every step (Thompson, 2013). By modifying the presentation of content dynamically based on student interaction with content, learning can be tailored to each student’s ability level. Adaptive learning software enables the online learning environment to evolve from being an analog of the traditional classroom to one that provides a 360-degree view of a learner’s interaction with content and more opportunities to personalize instruction and increase engagement.

Learning environments with adaptive technologies can support the learner in new ways. In an adaptive system, a student who is overconfident about her knowledge can quickly learn her limits and be corrected and guided to fill in any gaps in learning. A learner who is not as confident may find he can advance more quickly than he imagined because the system adjusts to his progress based on his mastery of content. A student who may fall behind in a more traditional learning environment can be guided to supplemental resources based on data about the specific issue she’s struggling with rather than a well-intentioned but general referral to an academic support service.

Incorporating adaptive learning software into the online classroom can help alleviate issues related to individual student readiness needs by providing students with foundational knowledge and analysis of knowledge gaps without creating an additional burden on faculty. An adaptive learning pathway is individualized for each student, assessing the student’s current grasp of a concept and guiding the student toward remediation, reinforcement or the next related concept accordingly. The student is given the opportunity to build a foundational skillset similar to a gaming environment, where the content experts have laid out all of the instructional options in the adaptive tool. Through the use of multiple types of assessments, especially low-stakes quizzes, students receive instant feedback on their progress and instructors are more available to coach and mentor students individually as well as monitor the progress of the class as a whole and adjust instruction accordingly. The data created by the student’s interaction with the adaptive software creates value at multiple levels: to the student, for the purposes of self-awareness; to the instructor, for the purposes of focused instruction and feedback; to support services such as tutors; and to the institution, for a greater understanding of student needs with regards to curriculum, instruction and support.

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References

Bloom, B. (1984). The 2 Sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher(13)6, 4-16.  Retrieved from http://web.mit.edu/5.95/readings/bloom-two-sigma.pdf

Smith, P., ed. (2014). MyLab and Mastering humanities and Social Sciences: Efficacy and implementation and results. Retrieved from http://www.pearsonmylabandmastering.com/northamerica/results/files/HSSLE_EfficacyResults_2014.pdf

Thompson, J. (2013). Types of adaptive learning. Retrieved from http://www.cogbooks.com/white-papers-adaptive.html

This is the first of a two-part series by Beth Mulherin and Karen Vignare exploring the potential for adaptive learning in the higher education space. In the conclusion, Mulherin and Vignare outline some examples of how adaptive learning technologies and practices have been experimented with and implemented.