What Is Adaptive Learning?
The “adaptive learning” method tailors lessons, readings, practice activities, and assessments to each student’s talents and performance.
Artificial intelligence and machine learning “adapt” each learner’s learning path in real time, enabling personalized learning. Faculty and administrators may later examine the data collected by adaptive learning software to assess the requirements of specific students or groups of students enrolled in a course. They may then modify a course in between terms and modify training to meet those requirements throughout a term. In a similar vein, students may modify their learning strategies by using data about their performance and abilities.
The term “adaptive learning software” often refers to the platform or underlying technology over which many courses may be constructed. A collection of digital lessons and practice exercises for a single course that make use of an underlying adaptive learning system is sometimes referred to as adaptive courseware. A library of content, lessons, practice exercises, and tests is often created by publishers of adaptive courseware on a specific topic, from which teachers may choose, arrange, and modify for their own course.
By enhancing course results and reducing the cost of course materials, adaptive learning, according to Every Learner Everywhere and its network partners, has the potential to advance equality in higher education, especially for students who are Black, Latino, Indigenous, or impacted by poverty.
However, there are certain restrictions associated with that possibility. Algorithmic bias may turn software into a tool that reinforces rather than narrows equity disparities, and schools are being provided “faux adaptive” software that falls short of expectations. Positive outcomes with adaptive learning, even with high-quality software, rely on a number of elements, including research-driven implementation and instructional design that is student-centered and equity-focused.
How Does Adaptive Learning Work?
In order to increase learning, adaptive learning software, at its most basic level, evaluates a student’s acquisition of a concept or skill in real time and then dynamically modifies the subsequent lesson or practice task that is given to that student.
Consider a college course unit that covers ten new ideas, for instance, and ends with a quiz before the class goes on to the next one. Assume that there are 100 pupils in this class and that the quiz’s historical average score is 70%.
Although there is an average score, seasoned teachers are aware that there is no typical explanation for why 30% of the exam questions are missed. It might be that:
- Student A had trouble understanding the last three concepts in the unit;
- Student B had trouble understanding three different concepts;
- Student C may have had a single key concept that affected 30% of their performance;
- Student D was disengaged because the “new” concepts weren’t new at all;
- And Student E may not have had a problem with the material, but rather missed important lessons due to work or family obligations.
Before the unit exam is over and the class goes on to the next unit, a college teacher who is dedicated to individualized learning has the difficulty of comprehending and meeting the requirements of each of those pupils.
The way adaptive learning software works is by instantly determining which specific ideas or abilities are important for each student’s development. Certain software programs also assess how well students understood the content, making a distinction between “performance data” and “engagement” data, including logins and time spent on task. The adaptive learning software then displays what it deems to be the best review or practice exercise for each current student after analyzing data on the learning pathways and performance of prior students using artificial intelligence and machine learning methods.
Individual students using adaptive learning courseware will thus follow distinct and nonlinear learning trajectories. A reminder and a few fresh practice questions from a bank of quiz questions will be displayed to one student, while another will be given the original lesson on a certain subject and another will be sent to other resources for a different idea.
The adaptive learning program might raise the average quiz score over the historical standard in it fictitious unit quiz example. If so, more students ought to grasp the material, which should lead to better learning outcomes at the conclusion of the term and during the course of the degree program.
The adaptive learning software, meanwhile, offers information that each student can use to comprehend and guide their own development as well as information that the teacher can use to modify their lectures before going on to the next unit.
Benefits Of Adaptive Learning
When used properly, adaptive learning may provide individualized learning strategies including scaffolding, flexibility, and focused training and materials. Faculty reported using the technology to support evidence-based teaching practices, including low-stakes formative assessments, clear learning objectives, immediate feedback on practice activities, and information on how to improve, in a study of adaptive learning pilot projects funded by Every Learner Everywhere.
There are several other possible Benefits of adaptive learning:
- Students may be able to be more successful and independent as a result. Students know where to focus their efforts when they see data from adaptive learning software regarding the abilities they have and have not mastered.
- Bypassing previously learned topics and revisiting and practicing others more slowly, it enables pupils to study at their own speed.
- By offering classes and activities that precisely correspond with students’ needs and preparedness, it may increase student engagement.
- It may serve as an affordable substitute for pricey textbooks.
- It offers a framework that ensures that lessons, practice exercises, evaluations, and course objectives are all in line with one another and demonstrates to students how each component of the course connects to the goals of the course. Faculty might also assess if certain instructional components aren’t properly matched with the goals when students are struggling to understand a subject.
- Faculty and administrators may utilize the timely and pertinent data it offers to determine the performance of certain subpopulations in a course. According to this article on breaking down learning data, this might be a very effective way to find and address obstacles to fairness for students from underrepresented groups and those living in poverty.
- By identifying specific students or even portions in a multi-section course that need attention, it allows instructors and administrators to provide prompt and focused help.
- By comparing data between semesters, it helps administrators and professors to make ongoing improvements.
