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Created page with "'''Adaptive learning''' is an approach within educational technology that features the use of computer software, algorithms, and analytics to identify changes to a computer-based educational product based on an individual learner's performance. Thus, the system will adapt the activities and content provided to each individual learner's participation and mastery of material, giving learners a personalized ex..."

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'''Adaptive learning''' is an approach within [[educational technology]] that features the use of computer software, algorithms, and analytics to identify changes to a computer-based educational product based on an [[Participant, student, or learner|individual learner's]] performance. Thus, the system will adapt the activities and content provided to each individual learner's [[participation]] and mastery of material, giving learners a [[Personalization|personalized]] experience.
== Definition==
An adaptive learning system is one that will change the digital content and activities that are provided to [[Participant, student, or learner|individual learners]] based on the levels of [[participation]] and [[Learning objectives and outcomes|achievement]] that they demonstrate. Adaptive learning software will identify [[evidence]] of students' [[Learning effectiveness evaluation|learning]] and [[Participation|interactions within the system]] and will provide [[Personalization|customized, adapted]] content and activities based on this performance.

==Additional Information ==
Adaptive computing systems in education are a popular area of research and development in recent years. With the improvements made in [[AI-based technologies: chatbots, digital agents, analysis (EdTech)|artificial intelligence (AI) and machine learning (ML)]] technologies, computers can be used to identify patterns of [[Participation|learner behavior]] and mastery of skills and content. With this in mind, a computer can make decisions to give learners more or less challenging activities to perform based on the learner's skill level, provide additional content to them as needed, or offer scaffolds and supports.

The power in adaptive technologies is that the computer identifies evidence in [[Formative assessment|formative evaluation]] data on a learner's [[Competency evaluation|competence]] and [[Learning effectiveness evaluation|changes in their skill]] (i.e., learning). The system can also detect [[Participation|patterns of behavior]] within a digital system, such as how a learner participates and interacts within a digital learning environment. Using this [[evidence]], the adaptive software can ideally be programmed to display custom content based on each learner's individual skill levels, needs, and interests.

The use of adaptive learning in practice still remains highly limited, however. AI and ML approaches are still not very common in educational settings, which often require a different set of skills among designers and educators than is present in typical classroom contexts (i.e., these are most commonly seen in computer science and corporate settings). AI and ML systems also require large [[Data|datasets]] of actual interactions of learners so that the computer may "learn" the patterns that they are looking for by working through the data case by case. Additionally, AI systems are often "trained" by experts to how classifications of behavior should occur and what categories should even exist in the system in the first place. These trainers of computer systems also program the AI software to determine appropriate outputs based on how the system interprets the data, which in this case includes how to change the system to provide new content, deliver supportive help or scaffolding, or to change the activity to better meet the needs of the learner.

Adaptive systems typically do the following tasks:

* Identify patterns in how a learner is participating in a system
* Identify evidence of a participant's learning from a system (often through formative assessments or through examining the participant's work products)
* Make decisions on how to change a system based on the learner's participation in the system OR their level of demonstrated learning/competence.

AI computer systems in real-world contexts have seen limited success that can both (1) accurately identify and predict students' skill and participation levels within a system and (2) subsequently make changes to the interface that align with students' needs and interests. Further research and development are needed in these areas.

There are also [[equity]], [[ethics]], and [[accessibility]] issues associated with adaptive learning technologies. Philosophically, it can be easy to rely on a computer algorithm to assign whether a learner succeeds or not, and an algorithm may not pick up on all of a learner's individual needs, [[Prior Knowledge and Experience|background experiences]], and affect. This, in turn, can give learners an unfair learning experience if the system misidentifies or incorrectly categorizes a student based on the expected levels of behavior. Designers of adaptive systems for learning should thus be cautious about how computer systems categorize and subsequently present information and activities to students to ensure that each and every student is equitably treated within such systems.

== Tips and Tricks ==

* Adaptive learning systems can be difficult to create and implement. If you are considering such a system, a [[Constraints|large investment of time and financial resources]] is likely required. You may also need to assemble a team of experts with a broad range of skills to implement such a system.
* It is important to consider what kinds of [[Participation|behaviors]] and [[Learning effectiveness evaluation|evidence of learning]] that you want the AI/adaptive system to look for. [[Formative assessment|How often]] should participants be assessed? This will help you program adaptive systems to spot the kinds of behaviors and mastery that will help the system make subsequent changes for the learner to personalize the system.
* It is also important to consider the types of "outputs" that you want the system to make. What kinds of changes to the system should occur, based on the system's [[evaluation]] of a learner's performance? How should the system change content, make activities more difficult or easier for learners, or how should the system present [[Scaffolding and support mechanisms|supports and scaffolds]] to help students when they need help? How should the system identify when a learner needs help in the first place - what does that look like?
* Sometimes adaptive systems and [[AI-based technologies: chatbots, digital agents, analysis (EdTech)|AI/ML systems]] in general can help you spot patterns of behavior and categorize new behaviors before you even see them as a researcher or designer. Lean on the statistical analyses that your system might provide to identify clusters of new behavior or patterns of how people are demonstrating mastery to help you consider how and why people use your product to learn.

==Related Concepts==

* [[AI-based technologies: chatbots, digital agents, analysis (EdTech)]]
* [[Formative assessment]]
* [[Evidence]]
* [[Evaluation]]

==Examples==

* None yet - check back soon!

== External Resources==

* None yet - check back soon!

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