Research and Insights

Evidence-based writing on learning science and digital learning: how memory, practice schedules, feedback and assessment design shape what learners actually retain. Read more about Ligascript eLearning software solutions.

Why Retrieval Practice Changes Memory, Not Just Measurement

Testing is usually treated as something that happens after instruction—a way to measure what learners retained. Retrieval practice serves a different purpose. By asking learners to recall, reconstruct, explain or apply knowledge without the answer in front of them, it can strengthen their ability to access that knowledge later. This article examines the testing effect, the limits of passive review, and the roles of difficulty, feedback and repetition in turning well-designed questions into part of the learning process itself.

Why Spacing Works Better Than Cramming for Long-Term Retention

Cramming can produce a persuasive sense of mastery: the material feels familiar, answers come quickly, and performance improves within the study session. Yet much of that fluency may disappear once the information is no longer immediately accessible. Spacing takes a different approach, returning learners to important knowledge over days or weeks so that retrieval becomes more effortful—and potentially more durable. This article explains how distributed practice differs from massed practice, why there is no single perfect spacing interval, and how material difficulty, existing knowledge, retention goals, and practice design should shape schedules for courses, assessments, review systems, and digital learning platforms.

Why Learning Feels Stronger Than It Really Is

Learning can feel secure long before it is durable. Familiar terminology, fluent rereading, recently viewed answers and explanations that seem obvious while visible can all create confidence without requiring knowledge to be independently reconstructed or applied. This article examines why subjective confidence and actual retrievability diverge across studying, language learning, mathematics and professional training—and why self-testing, delayed retrieval, explanation and application provide more credible evidence of what a learner will still know when the support is gone.

How Prior Knowledge Changes What We Remember

Learning never begins from zero. Existing knowledge shapes how people organize new ideas, make inferences, manage cognitive demands, and decide what an explanation means. It can make unfamiliar material easier to understand, but inaccurate assumptions can also distort new information and make misconceptions harder to correct. Understanding this interaction explains why experts and novices can encounter the same material yet remember very different things.

What Should an Assessment Actually Measure?

An assessment can cover the right subject matter and still measure the wrong capability. The task presented determines whether a learner is demonstrating recall, understanding, application, reasoning, transfer or authentic performance—and a numerical score alone cannot explain the difference. This article examines how to align assessment with its intended purpose, identify meaningful observable evidence, avoid common mismatches and determine whether the conclusions drawn from a result are genuinely supported.

Worked Examples Versus Independent Problem Solving

Independent problem solving is essential, but it is not always the most effective place for novices to begin. This article examines how worked examples reduce unproductive cognitive load, make expert reasoning visible, and help learners build the schemas needed for successful performance. It also explores self-explanation, completion problems, gradually fading guidance, and the point at which extensive support becomes inefficient for more experienced learners.

How Digital Learning Evolves Through Feedback Loops

Digital learning is most effective when it does more than deliver a predetermined sequence of content. By capturing evidence from errors, confidence, repeated attempts, time-on-task and progress patterns, a learning environment can adjust what happens next—changing the sequence, level of support, difficulty, examples, review activities or need for instructor intervention. This article examines how meaningful feedback loops differ from simple branching, how they can improve both individual learning experiences and course design, and why careful human judgement remains essential. It also considers the risks of noisy data, excessive automation and systems that optimise measurable activity rather than lasting understanding.

The Difference Between Digitizing a Course and Transforming Learning

Uploading PDFs, recording lectures and moving quizzes online can improve access without changing how learning happens. Genuine digital transformation goes further by redesigning practice, feedback, participation, accessibility and assessment around capabilities that digital systems make possible. This article offers a practical framework for distinguishing a change of medium from a meaningful change in the learning process—and for deciding when straightforward digitization is sufficient, when deeper redesign creates real educational value and why adding more technology is never, by itself, evidence of better learning.

From Digital Content to Responsive Learning Systems

Digital learning becomes responsive when it moves beyond presenting the same material to everyone and begins using evidence from learner activity to shape what happens next. This article examines the progression from fixed content to systems that remember progress, respond to errors, recommend review, adjust sequencing, and vary support—while explaining why meaningful responsiveness depends on sound educational reasoning, transparent decisions, reliable evidence, and more than behavioural personalisation alone.