University students adaptive learning

Adaptive Learning

What is Adaptive Learning?

Adaptive learning is a technique to use data-driven instruction to adjust and tailor learning experiences to meet the individual needs of each student. Adaptive learning systems can track data such as student progress, engagement, and performance, and use the data to provide personalized learning experiences.

While equal education opportunity affords individuals equal access to resources, equitable education recognizes and addresses the differences between learners by providing the fitting material aligned with each to reach their academic endeavor. Adaptive learning along with adaptive teaching and assessment strives to provide equity in education to all learners.

Adaptive Learning & Assessment

Adaptive learning is part of interactive learning which addresses the needs of individuals through learning pathways, effective feedback, and supplemental resources; as opposed to an one-size-fits-all curriculum (Kurt, 2021). Technology advancement makes adaptive learning easier to implement. There are three areas one can implement adaptive learning: adaptive content, adaptive sequence, and adaptive assessment.

  • Adaptive content provides feedback to students’ specific response (e.g. hints, review materials on the relevant skill, further scaffolding) without changing the overall sequence of skills.
  • Adaptive sequence continuously collects and analyzes student data to automatically change what a student sees next.
  • Adaptive assessment changes the questions a student sees based on his or her response to the previous question. The difficulty of questions will increase as a student answers them accurately, while if the student struggles the questions get easier.

Adaptive learning software often incorporates all three areas. First, it breaks down course material into manageable sections based on each learning objective. Then, it provides learners with immediate assistance, resources specific to their learning needs, and relevant feedback. The software adjusts content, sequence, and assessment according to the interactive responses stored in the system. Furthermore, instructors can adapt instruction by making just-in-time, data-driven informed decisions to cater the course to each individual’s needs.

Process for Creating Adaptive Learning Scenarios

When an instructor designs an adaptive learning scenario, content, sequence, and assessment will be developed with the chosen adaptive technology in mind. The phases for developing the content, sequence, and assessment are listed below:

Step 1
Step 1: Identify small knowledge units

An instructor will begin by developing objective-based small knowledge units, or short lessons, that are connected to overall learning objectives (Cavanagh et al., 2020, p. 178). These lessons provide the foundation for the adaptive learning scenario for which the sequence will be developed and with which the assessments will be correlated.

Step 2
Step 2: Develop assessments and feedback

After designing the content and organizing it into small knowledge units, assessments and feedback will need to be developed to create a holistic and personalized learning experience for students. As with any traditionally designed course, assessments in an adaptive learning environment will be aligned with learning objectives and activities and will help to determine a student’s learning path based on assessment performance. Since students will engage in an adaptive learning experience independently, structuring feedback is an important addition to consider when creating assessments. Writing feedback to students as they answer questions, with explanations about why an answer is correct or incorrect, can help enable performance mastery (Cavanagh et al., 2020).

Step 3
Step 3: Design the adaptive learning path

Once small knowledge units, assessments, and feedback have been planned, an instructor can think about the pathway for students to progress through the content. Based on a student’s pre-assessment performance, adaptive learning software will assign them to a pathway based on the instructor’s preferences. Typically, pathways evolve from foundational knowledge to more complex content to build student mastery of the learning objectives (Cavanagh, 2020). While an instructor can design the basic learning pathways for students, AI software will also make personalized recommendations to students based on their assessment performance. For example, if a student doesn’t perform well on a particular assessment, the AI software may recommend that the student review an earlier unit before moving onto the next.

 Benefits of Adaptive Learning

Adaptive learning has several potential benefits (McGuire, 2021):

  • Adaptive learning may enable students to become more successful and self-directed by providing insight into their level of mastery and allowing them to work at their own pace.
  • It potentially improves student engagement by providing lessons and activities that are tailored to their needs.
  • It can be used as a cost-effective alternative to expensive textbooks.
  • It provides a structure that keeps course objectives, lessons, practice activities, and assessments in alignment and shows students how each element of the course relates to the course objectives. Likewise, when students are having trouble mastering a concept, faculty can review if some instructional elements aren’t well aligned with the objectives.
  • It provides relevant and timely data that faculty and administrators can use to identify how targeted subpopulations in a course are doing. This is potentially a powerful tool for identifying and confronting barriers to equity for minoritized and poverty-affected students.
  • It enables faculty and administrators to provide timely and targeted support by identifying individual students, or even particular sections in a multi-section course, that need attention.
  • It enables faculty and administrators to make continuous improvement by comparing data across semesters.
  • Adaptive learning enables the delivery of personalized learning at scale, it also reduces cheating because the content and assessments can vary for each student.
  • Adaptive learning can maximize learning outcomes since instructors can have a better sense of the areas that students are struggling and who need more help and provide intervention before students are at risk of withdrawal.

Best Practices for Making Adaptive Learning Successful

  • Adaptive learning requires human planning and interactions to be successful. Instructor presence is still essential for adaptive learning to take place as they assist students in understanding the value of the adaptive system and help transition them from passive to active collaborators and learners.
  • It is recommended to select the adaptive platform that provides instructors the ability to select specific learning activities and assessments to make sure that content is aligned with the course objectives.
  • Instructors should plan ahead when designing adaptive learning and identify who they can reach out to for support
  • Instructors should take the time to understand how the adaptive system works and relay that information to students.
  • Present clearly the expectations for the course and the process of engaging in the adaptive material.
  • Become familiar with the learning analytics provided by the adaptive learning tool. Utilizing learning analytics effectively to inform instructors the interventions and implementing learner-centered teaching have been the key.

Works Cited and Additional Resources

Cavanagh, T., Chen, B., Lahcen, R.A.M., & Paradiso, J. (2020). Constructing a design framework and pedagogical approach for adaptive learning in higher education: A practitioner’s perspective. International Review of Research in Open and Distributed Learning, 21(1), 173-197.

Kurt, S. (2021). Adaptive learning: What is it, what are its benefits and how does it work? Educational Technology.

McGuire, R. (2021). What is adaptive learning and how does it work to promote equity in higher education. Every Learner Everywhere.

Peng, H., Ma, S., & Spector, J.M. (2019). Personalized adaptive learning: an emerging pedagogical approach enabled by a smart learning environment. Smart Learning Environment, 6(9).

Redmon, M., Wyatt, S., & Stull, C. (2021). Using personalized adaptive learning to promote industry-specific language skills in support of Spanish internship students. Global Business Languages, 21, 92-112.