The Right Experience at the Right Time: An Insider’s Guide to Adaptive Technology
Part 1: The Science Behind Adaptive Learning
In the not-so-distant past, students who struggled were often left behind, while those who needed a greater challenge were expected to wait for the rest of their class to catch up. Now, educators can use personalized learning strategies, tools, and resources to adapt existing curricula and the instructional practices for every learning level. This empowers them to provide remediation for struggling students, supportive practice for on-level students, and enrichment for advanced students, so every student has an equal chance to achieve their full potential.
School and district leaders who want to pursue personalized learning curricula have ample resources at their disposal but may find it difficult to make comparisons between them.
Each educator has a unique set of goals and challenges, but all educators can benefit from understanding the best practices for choosing adaptive technologies.
In this blog series, we will present a purposeful guide for educators who are just starting out with adaptive technology. Rooted in learning science research, this adaptive technology roadmap will help you understand, approach, and implement a sound personalized learning strategy that can benefit all your students. Read on for Part 1 of our series, an overview of adaptive technology and its scientific origins.
Adaptive Tech Today: A Birds-Eye View
Adaptive learning is not a new concept. Any learning experience that is adapted to suit the needs of the individual student — whether in terms of pace, the order of presented concepts, type of assessment, or other means — can be considered adaptive. Educators of all stripes have been striving to make their educational offerings adaptive for as long as there has been education. What is new is the technology that can allow instructors to offer adaptive learning at an unprecedented scale and with unprecedented efficacy.
The best adaptive learning technology collects information about the student’s skills, knowledge, and confidence levels, and uses it t choose material, tasks, and scaffolds that match their needs.
The Origins of Adaptive Tech
In 1984, Benjamin Bloom — the educational researcher best known for Bloom’s Taxonomy — published a paper on om the “2 Sigma Problem.” Bloom’s research divided students into three groups:
- Lecture Group: This control group received conventional, class-based instruction and periodic assessments to track how much they had learned.
- Mastery Learning: This group also received conventional, class-based instruction, but they were given formative assessment tasks through the course as well, designed to help them learn the material and establish when they had mastered a topic and were ready to progress.
- Individual Tutoring: This group received the same formative, mastery-based feedback as group 2, along with one-on-one tutoring.
Bloom’s results were striking.
Students who had received individualized tutoring showed improvement over the control class by two standard deviations (or grades), with many of the tutored students surpassing 98 percent of students in the controlled group.
Meanwhile, mastery learning boosted results one standard deviation above the control group. The mastery students also surpassed 84 percent of the students in the control group.
The results, which have subsequently been reproduced and expanded upon, suggest that a combination of mastery learning and personalized tutoring allows learners to achieve their potential.
The Neuroscience Behind Adaptive Tech
Adaptive learning technology rests on other well-established concepts in educational psychology.
One of the most common bases for adaptive learning is the idea that the students who are most prepared to put in the effort, are the ones most likely to reap the benefits. This concept has been given a variety of names, with subtle, albeit important, differences between them. But the basic principle is consistent: conscientiousness, grit, self-control, deliberate practice, and willpower all share the notion that practicing something difficult will eventually lead to success. Adaptive technologies are designed to make that practice efficient, constructive, and rewarding.
Metacognition is another concept that is central for certain adaptive learning technologies. This is the ability to think about one’s thought process or observe one’s learning. Flavell, the psychologist credited with developing our understanding of the importance of metacognition in education, writes that “I am engaging in metacognition if I notice that I am having more trouble learning A than B; if it strikes me that I should double-check C before accepting it as fact.”
Enhanced metacognition has been found to improve students’ understanding in a variety of disciplines, including mathematics, physics and reading comprehension, also helping to transfer skills to new settings. Adaptive learning can help students develop their metacognition by breaking down each skill into its respective components, monitoring their progress towards learning goals, repeating questions where they showed lower confidence and indicating discrepancies between self-assessed certainty and actual performance on a task.
Ebbinghaus Forgetting Curve
It is possible for adaptive learning technology to not only help students learn information, but also retain it. This is based on theoretical work with roots back to the 19th century, like the Ebbinghaus forgetting curve, which mathematically models the rate at which people lose information after first learning it.
Some adaptive learning systems use these models to establish the best time for students to review information they have already learned, contributing to the long-term retention of information. Using these models also discourages cramming, which results in information being lost immediately after assessment.
What Does This Mean for Educators?
Adaptive learning is grounded in well-established pedagogical theory, but as a new technology, robust evidence of results are only beginning to be published. Early results indicate that the promise suggested by Bloom is being fulfilled by the best adaptive learning technology, which results in boosted student performance and engagement. Some promising early results suggest that adaptive learning technology has the potential to improve student engagement and motivation, reduce course drop-out rates, and help students to achieve learning outcomes more efficiently.
For instructors, one of the primary benefits of adaptive learning technology is the change in how effectively time can be used. Far from replacing the human element of education, the software liberates it.
Instructors are able to offload tasks to adaptive learning software that would normally be spent on the repetitive and time-heavy task of grading. Instead, that time can be spent learning how individual students are coping with the material, developing and recommending resources, and generally taking on the role of coach and mentor.
Additionally, because students are able to receive better individual support on foundational concepts in their own time through adaptive learning software, class time can be used to work on higher-level concepts, stimulate discussion and interaction, and focus on skills like critical thinking. This also means increased time for instructors to engage with students on an individual level, offering help where it is most needed.
Continue reading our series to learn how to start integrating adaptive tech in your classroom today.