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In education, adaptive learning tools are built around the interaction between teachers, students, and automated technologies, often addressing the imbalance between teachers’ limited capacity and students’ high demands.

A large group of students might need individual attention the instructor cannot possibly provide on their own, but adaptive learning courseware can deliver personalized learning activities using a model designed to measure student knowledge states. Within a subject-appropriate framework, it adapts activities to match students’ strengths, weaknesses, proficiencies, and knowledge gaps.

How does adaptive learning work?

Most adaptive learning functionality follows a similar process:

  1. Set goals for the student’s work session (like a homework assignment) and/or the whole educational context (like a course). This frequently involves an instructor creating a syllabus or assigning some learning objectives.
  2. Deliver assessment questions relevant to those goals.
  3. Estimate the student’s knowledge state based on the correctness of their answers, the difficulty of the questions, and/or the alignment of the questions to a knowledge graph of skills, concepts, or learning objectives.
  4. Decide what activity should come next based on the student’s estimated knowledge state—whether that means delivering easier or harder questions, providing an instructional intervention, remediating a gap in prerequisite knowledge, suggesting the student speak to their teacher, or some other activity.

The cycle of steps (two to four) then continues until each student reaches the goals set in step one.

(*Learn more about how Wiley’s proficiency model estimates student knowledge here)

What are the benefits?

Even in early days, the most immediate benefit of adaptive learning was personalization at scale: Students can receive something like personalized attention without requiring 10 times as many instructors. Adaptive learning tools act as “virtual teaching assistants,” engaging each student with the material most likely to improve their learning outcomes.

When students can receive assessment and instruction that meets them at their current knowledge state—which may differ significantly from that of their peers—they’re more likely to reach higher levels of achievement. Students requiring more practice, instruction, and remediation to prerequisite skills can receive additional support, while more advanced students in the same cohort can engage with challenging problems.

Learning science studies have shown that adaptive learning generally causes “all boats to rise” and that computer-assisted personalization at scale can be at least as effective as human-run group tutoring sessions in pedagogically appropriate contexts.

What are common challenges?

  • Lack of student agency and motivation: Students may feel they’re not in control if they’re required to follow whatever “the algorithm” thinks they need. If the tool neither explains why the material has been selected nor considers the student’s input, the resulting experience can be frustrating and possibly counterproductive.
  • Insufficient connections to human support: No adaptive learning tool can close every knowledge gap for every student, even with robust interventions. Without actionable analytics for instructors to identify struggling students or timely “off-ramps” motivating students to seek (human) help, some students may spin their wheels unproductively instead of receiving high-value, efficient support from an expert teacher.
  • Fragmentation of student cohorts: Adaptive learning tools can differ on how “far apart” to let a cohort get. In extreme cases, a teacher might find that advanced students progress quickly through the syllabus while less prepared students remain stuck working on early units or prerequisite skills. Successful implementation of adaptive learning keeps students working on similar learning objectives at the same time, depending on how important that may be for the course cadence or structure.

How does adaptive learning courseware shift instructors’ and students’ mindset?

For instructors, using adaptive technology typically means giving up step-by-step control. For the courseware to successfully adapt to a student’s needs, it can’t just deliver pre-selected materials in an instructor’s pre-set order. Successful courseware implementations of adaptive learning aim for a middle ground, where instructors choose the goals, content areas, and/or pedagogical functions for each student activity, but the adaptive technology assigns variable amounts and types of content to students as needed. The adaptive learning courseware should also provide easily interpretable analytics and calls to action so the instructor understands each student’s learning and what kinds of activities or 1:1 attention would yield the best results.

For students, adaptive learning activities may be different from their experience with “traditional homework.” Working on assignments differing in length and type from that of their peers can feel arbitrary and punitive without an explanation of the benefits of this arrangement. Additionally, students typically have a fear of submitting wrong answers since that would traditionally penalize their final grade. Adaptive learning usually reframes this penalty: Students are expected to get wrong answers as they learn and can achieve an excellent final grade on an adaptive activity even after a poor start, provided they put in the effort to learn from their mistakes. We find that students tend to respond positively to this “growth mindset” orientation.

How do Wiley and Knewton Alta approach adaptive learning?

Our two solutions support mastery-based learning in different ways:

  • Knewton Alta: In Alta, the bulk of each student’s outside-of-class time is spent working on fully adaptive assignments. Instructors assign learning objectives, and students receive personalized assignment experiences designed to help them master the material. Alta offers just-in-time support via focused practice questions, instructional texts and videos, and real-time remediation to prerequisites. Since these interventions can be time-intensive, Alta integrates student agency touchpoints, empowering students to help guide their learning. Students can, at high-leverage decision points, decide (1) to override Knewton Alta’s remediation to prerequisite skills and return to assigned material or (2) to switch to another topic they feel better prepared to cover. (*See the effectiveness of refreshers in Knewton Alta here)
  • WileyPLUS: This courseware includes adaptive assignments powered by Knewton, but a given WileyPLUS course typically contains at least as many non-adaptive assignments (i.e., static problem sets, resources, or instructor-provided materials) as adaptive ones. Therefore, the student agency in Alta adaptive assignments is less important than instructor flexibility at adaptive assignment creation time. Each WileyPLUS adaptive assignment implements one of three broad pedagogical functions: introduce, practice, or review. Instructors can choose on an assignment-by-assignment basis which function best fits their syllabus.