Personalization and adaptive delivery has consistently been mentioned as one of the top emerging trends in e-learning. Adaptive learning comes in many varieties, from simple pre-assessment to artificial intelligence (AI) algorithms that dynamically adjust content, activities or learning sequences. These systems make use of learner profiles and feedback, instructor interventions, assessment results, learning management systems (LMS) and related platform data, and deep learning algorithms to provide a personalized learning experience.

With thoughtful instructional design, adaptive learning can be especially useful when there is a large cohort or when learners are interacting with online material on their own. It provides tailored learner support and delivers content at the appropriate level and sequence. However, many challenges exist in creating effective adaptive learning. Below are some areas to inform your design process:

1. Use Actionable Data 

Adaptive learning will improve as more data are collected, but only if the data provides insights learners and course designers can act on. For example, in adaptive learning design, it is not entirely meaningful to collect data on how frequently a learner logs into an LMS. On the other hand, formative assessment data (e.g. quick checks for understanding or a feedback mechanism where learners can enter comments) could be useful when assessments provide results that course designers use to adjust the content sequence, or for learners to be able to seek further clarifications.

2. Know What to Adapt 

Adaption could be many things. You can adapt learning at a macro level for a curriculum – provide pre-test to assess learners’ knowledge, allow learners to skip certain learning modules, or ask people for their learning preferences. At a micro level, learning materials can be adapted based on what learners have done and clicked on, where they want to go, and how to re-sequence content based on the interaction. Adaptive assessments can be applied to adjust the level of difficulty for each question, as well as regulate the frequency of the tests. Know what you need to adapt for your learners and select the appropriate type of technique accordingly.

3. Apply Sound Principles

For adaptive learning to work well, it is pertinent to apply sound pedagogical principles. Adaptive learning has less to do with the technology and more to do with how the learning design meets objectives and competencies. Learning theories such as spaced repetition could be integrated into the adaptive algorithm to spread out learning across multiple sessions and remind learners what they have forgotten. Spaced repetition theory incorporates increasing intervals of time between reviews of previously learned material that leads to greater retention of knowledge. The intervals could be adjusted based on each learner’s performance, test scores and other attributes. Instructional designers who work on adaptive learning systems must have a deep understanding of pedagogical principles in relation to the target learners: how these principles are applied, how the learners interact with the systems, and to what extent does the adaptive design reflect the underlying assumptions of the learners.

4. Put Learners in Control

The promise of adaptive learning is to create a learner-centered experience that is sensitive to your individual needs, as well as context. Learners should be in control of their learning journey and the adaptive systems need to benefit their learning process, support them in becoming self-directed learners and contribute to their overall learning experience. Learners need to have the ability to choose, provide feedback, adjust and be informed about how decisions are made based on assumptions about them. In other words, adaptive systems must be transparent in how and what they adapt. It is difficult to make informed decisions and correct the paths we are on in we don’t know how our systems function.

Adaptive learning can be an effective way to bring individualized learning to large cohorts or to engage learners in a self-paced online learning environment. At the same time, we need to be mindful of how this technique is best utilized to cater to the diverse learning needs.