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One important but less frequently discussed recursive feedback concept in digital learning environments is Computer Adaptive Testing (CAT). While many participants have highlighted formative assessment, dashboards, and peer review, CAT stands out because it demonstrates recursive feedback in real time, adjusting assessment pathways based on each learner’s performance.

Definition

Computer Adaptive Testing is an assessment method in which the difficulty of test items dynamically changes according to the learner’s responses. When a learner answers a question correctly, the system presents a slightly more challenging item; when the learner answers incorrectly, the next item becomes easier (Weiss, 2011). This creates a moment-to-moment feedback loop, where the test continuously recalibrates to estimate a learner’s true ability level with greater precision.

Example in Practice

A clear example of CAT is the Graduate Record Examination (GRE), which uses adaptive algorithms to tailor question difficulty based on the test-taker’s performance. In e-learning environments, platforms such as Khan Academy, Duolingo, and EdReady use similar adaptive engines to adjust question sequences, ensuring that learners receive tasks at an optimal challenge level. This creates recursive feedback because each learner action becomes input for the system’s next decision, creating a loop of assessment → adjustment → new assessment.

In a MOOC context, CAT can help reduce frustration from items that are too easy or overwhelming, while providing instructors with more accurate analytics about student proficiency.

References (APA Style)

Duolingo. (2024). How Duolingo uses adaptive learning. https://www.duolingo.com Weiss, D. J. (2011). Better data from better measurements using computerized adaptive testing. Journal of Methods and Measurement in the Social Sciences, 2(1), 1–27. Educational Testing Service. (2023). How the GRE test adapts. https://www.ets.org