Adaptive Learning Just Might be The Future of Online Education
Adaptive learning is a tech-enabled method for tailoring learning experiences to individual needs. In recent years, many organizations have excelled at helping students and instructors meet their educational goals through coursework that’s made more effective, more efficient, and more engaging – via adaptive learning models and technology.
But what exactly is adaptive learning, and how does it work?
What are some examples of organizations succeeding at it, and how can course creators duplicate these robust results?
Adaptive Learning is Data-Driven, Personalized Learning
A great advantage to online or hybrid learning is that it can capture data on how students are progressing, and/or what approaches work best for them, and then use this data to help course creators personalize the learning experience for each student.
This means courses can support customized learning journeys that adapt to each student’s individual learning style and pace. It also helps instructors determine a given student’s level of understanding, and empowers them with more information for supporting their aspirations towards the next level up.
For students, this can mean a more comfortable and confident path to learning. They can work at their own pace, without feeling held back or left behind by peers.
How Does Adaptive Learning Work?
An adaptive learning system works by utilizing software to analyze data on student interaction. This helps the software and its operators to create a learning path unique to each student, usually based on:
- Their individual strengths
- Their weaknesses
- Their learning preferences
Coursework is then automatically modified, often within a certain range of predetermined parameters, to empower students to work at their own pace. It also enables either the course itself or its instructors (or both) to deliver targeted feedback and support, to help focus efforts on areas where students need the most attention.
Data is collected through a variety of methods, including quizzes and other assessments, and other touchpoints with the specific learning software. Pathways to generating actionable data are engineered through the course content itself, and how it is structured and executed.
For course videos, engagement data can show how long it took a student to review a topic, where they might have lingered or replayed, or how they interacted with the content for any videos where this functionality was present. Assessment modules can provide even more robust data sets, since students are directly interacting with course materials, again at a certain measurable pace and success rate.
As each student continues to work through their course, an adaptive learning system can provide real-time support, and/or adjust to the students needs based on what it learns from these data sources.
5 Examples of Successful Adaptive Learning Models in Action
Adaptive learning has become increasingly popular across a number of educational environments, including public education, higher education, and continuing education (both formal and informal).
Here are some popular and cutting-edge examples of successful adaptive learning models:
Typically recognized as one of the most popular and widely-respected online learning platforms in the world, LinkedIn Learning utilizes adaptive learning technology to personalize content to each of its users.
The courses on LinkedIn Learning vary widely by subject, but generally focus on business, technology, and creative topics. Despite this variety, the platform approaches adaptive learning similarly across topics, leveraging algorithms to first help identify likely matches for appropriate coursework, and then to suggest new courses to help students stack additional skills upon what they’ve already learned.
This helps students stay interested and engaged. LinkedIn Learning also tracks goals and automates reminders and suggestions that help keep students on track. Finally, interactive exercises and quizzes within course modules help reinforce concepts taught in complementary video modules, helping students consistently develop skills they need to remain competitive at work.
We chose Rosetta Stone as an example over Duolingo for the purpose of this post. The two platforms are similarly-focused and Duolingo is an even more popular learning platform that excels at adaptive learning, but it isn’t as reliant on video for compiling course modules.
Video course modules from Rosetta Stone introduce lessons on vocabulary and grammar via a context of real-life simulations with native speakers. Students immerse themselves in practice exercises based on the content in the videos. The system’s adaptive learning algorithms adjust lesson difficulty and pacing based on measured performance and perceived need. Speech recognition software evaluates results like pronunciation and then generates targeted feedback and then exercises from the course catalog.
Pairing an innovative supplementary solution like speech recognition software is an interested example of video course creators to think about when considering their own adaptive learning models.
The adaptive learning experience engineered by Mathspace provides students with a mix of video modules and interactive activities personalized to each user. Videos explain concepts, and supplement quizzes and practice problems provide a more interactive experience. Mathspace advertises itself as a sort of partner to teachers and students, as opposed to a standalone solution.
Mathspace also employs AI-powered recommendations and adaptive pathways to further personalize and power student growth, even within a standardized course. An example of this might be the delivery of an additional explanation that one student might require, but another does not. The first student can slow down and stick with the module until they understand it more fully, while the second can either complete their work or move on, depending on how their instructor has structured the course.
Again, the example of pairing powerful software with video course modules provides some food for thought for course creators.
An interesting method employed by online learning platform Coursera is their use of data analytics to dynamically adjust the difficulty and pace of lessons. They offer a wide range of video-based courses taught by leading experts from around the world, with video modules designed to provide students with clear and engaging explorations of complex topics. As with many other examples we’ve looked at, Coursera often integrates video course modules with quizzes and assessments as a reinforcement tool.
Adaptive learning algorithms from Coursera analyze patterns in student behavior and performance. From there, they automatically identify and target areas where any one student might be in need of additional support. The goal is an efficient and effective learning experience. Video course creators could learn from this example of layered, dynamic adaptive teaching in seeking to generate their own adaptive learning model for their course(s).
As one of the largest e-learning platforms in the world outside of the traditional higher education environment, Udemy takes a more layered approach to adaptive learning. Rather than only using data to adapt the content of individual courses, Udemy also personalizes coursework on a higher level, recommending entire other courses based on an individual’s preferences and engagement in their wider learning environment.
This empowers students to take an active role in the own learning, much as they would in a more traditional educational setting. At the same time, on the course level, students continue to receive personalized support in the form of interactive tools and assessments customized to their progress and unique needs. Again, data is shown to help drive a closer and more refined experience for course creators.
How Can Video Course Creators Adopt Their Own Adaptive Learning Models?
If you’re a subject matter expert (SME) or aspiring course creator looking to build a competitive online school or course, incorporating an adaptive learning model into your curriculum could be greatly beneficial to how you serve your students.
Adaptive learning empowers course creators to:
- Design custom learning paths
- Quickly assess student performance
- Employ interactive media for greater engagement
- Encourage self-directed and self-paced learning
- Provide continuous, data-driven feedback
There are many tools and resources available to build adaptive learning into the tech stack for your online course. These include companies like Knewton, Smart Sparrow, and DreamBox learning. Some Learning Management Systems (LMS) provide adaptive learning tools as part of their feature sets. Examples of these include Canvas, Blackboard and Moodle.
Several open-source libraries also exist to enable adaptive learning, such as OpenEdX and the Adaptive Learning Open Source Initiative (ALOSI). Finally, many platforms offer APIs and SDKs for customizing solutions for adaptive learning.
Conclusion: Adaptive Learning is Here to Stay, So Prepare Accordingly
As with so many other aspects of our increasingly data-driven work and personal lives, adaptive learning is likely here to stay. In fact, it might only become more of an influence on the future of all forms of education, as artificial intelligence and machine learning continue to expand our definitions of what’s possible.
Already, we see from the examples discussed here how algorithms are changing how humans learn. There are reasons to believe this could be a great thing for course creators. A rising tide lifts all boats, and the sort of personalized learning experience enabled by adaptive learning has been shown to close achievement gaps, improve outcomes, and empower new generations of learners and doers.