Learning How to Learn: 12 Evidence-Based Strategies That Work

What does “learning how to learn” mean?

“Learning how to learn” means deliberately replacing intuitive, comfortable study habits with cognitive strategies validated by empirical research. Most people acquire their study habits through accident, cultural folklore, or immediate convenience: they highlight paragraphs, re-read textbook chapters, and cram before examinations. While these passive habits feel easy and produce an immediate sense of familiarity, cognitive science demonstrates that they produce fragile memories that decay rapidly within days.

True metacognitive competence requires understanding how human cognitive architecture processes, encodes, and consolidates information. When learners transition from passive input consumption to active cognitive processing, learning becomes substantially more durable and transferable. As you cultivate expertise through self-directed learning such as learning strategies, choosing the correct cognitive tools determines whether your study hours yield lasting capability or transient familiarity.

Which learning strategies work best?

Cognitive and educational psychologists have spent decades evaluating how different instructional strategies affect long-term retention and conceptual transfer. In their foundational review, Dunlosky and colleagues (2013) evaluated common techniques, while a comprehensive meta-analysis of 242 studies by Donoghue and Hattie (2021) quantified their exact effect sizes.

The twelve strategies below represent the most prominent cognitive methods available to modern learners. They are ranked by empirical evidence, combining quantitative effect sizes with real-world generalizability:

For an in-depth breakdown of the underlying statistical trials and effect sizes, consult our detailed analysis of learning techniques ranked by evidence.

1. Retrieval practice

Retrieval practice—often termed the testing effect—means actively recalling information from memory rather than passively reviewing or re-reading source materials. When you answer a practice question, complete a blank-page recall drill, or recite core concepts without looking, the cognitive effort of retrieving information fundamentally restructures and strengthens associative pathways in long-term memory. Large-scale meta-analyses consistently confirm that retrieval practice produces substantial retention advantages ($d = 0.74$) over passive restudy, protecting knowledge against retroactive interference and natural decay.

2. Spaced practice

Spaced practice (or distributed practice) involves spreading study sessions across separate time intervals rather than massing practice into a single concentrated block. By introducing temporal gaps between review sessions, learners allow partial forgetting to take place. Retrieving knowledge after a delay requires greater cognitive effort, which triggers robust synaptic consolidation. Decades of research confirm that implementing spaced repetition produces dramatically superior retention across weeks and months without requiring any additional total study time ($d = 0.71$).

3. Interleaving

While traditional schooling encourages “blocked practice”—mastering one category of problems before moving to the next—interleaving mixes distinct problem types, concepts, or skills within the same study session. For example, instead of completing twenty factoring problems followed by twenty quadratic formula problems, an interleaved set mixes both unpredictably. A meta-analysis by Brunmair and Richter (2019) demonstrated that interleaving forces the brain to practice category discrimination—identifying which rule or formula applies—which significantly enhances delayed diagnostic accuracy on complex exams ($d = 0.45$).

4. Elaboration

Elaboration involves asking probing questions about how and why specific facts or concepts work, connecting new information to preexisting mental models. Instead of accepting a factual statement at face value, the learner asks: “Why is this true?”, “How does this connect to what I learned earlier?”, and “What are the boundary conditions?” Combining elaboration and self-explanation deepens semantic encoding, transforming isolated facts into rich, interconnected knowledge schemas ($d = 0.58$).

5. Self-explanation

Self-explanation requires learners to explain each procedural step of a problem or articulate their line of reasoning out loud or in writing. Seminal research by Chi and colleagues (1989) and a meta-analysis by Bisra et al. (2018) established that prompting learners to explain the rationale behind exemplar solutions significantly improves problem-solving transfer ($g = 0.55$). Self-explanation forces learners to identify gaps in their comprehension that passive reading routinely conceals.

6. Concrete examples

Human cognition evolved to understand concrete physical reality before abstract theoretical constructs. When mastering complex or counterintuitive theories—such as cognitive load theory, economic game theory, or statistical variance—collecting and analyzing multiple concrete examples grounds the abstract principles. Comparing two diverse concrete examples that share an underlying structure enables the mind to strip away superficial surface noise and abstract the core invariant principle.

7. Dual coding

Formulated by Allan Paivio, dual coding theory posits that memory operates through two independent yet interconnected cognitive subsystems: a verbal system (processing text and speech) and a non-verbal system (processing visual and spatial images). When instruction pairs clear explanatory text with relevant diagrams, flowcharts, or structural models, information is encoded through both channels simultaneously. Exploring concept maps and dual coding reveals how creating visual representations of structural relationships reduces working memory strain and enhances delayed recall.

8. Worked examples

For novices encountering a completely unfamiliar domain, unguided problem-solving imposes crushing extraneous cognitive load as they resort to inefficient means-ends analysis. Cognitive load researchers (Sweller & Cooper, 1985) proved that studying step-by-step worked examples allows novices to focus their limited working memory capacity entirely on constructing schema representations. However, as competence increases, worked examples become redundant, and learners must transition to unguided problem solving—a phenomenon known as the expertise reversal effect.

9. Feedback

Feedback is among the most powerful instructional interventions, but its effectiveness depends strictly on its focus and delivery. Formative feedback that provides specific, task-level correction—answering “Where am I going?”, “How am I progressing?”, and “What is my next step?”—consistently accelerates skill acquisition ($d = 0.70$). Conversely, evaluative feedback centered on the person (such as generic praise or criticism) directs attention toward ego defense rather than task execution, frequently harming performance.

10. Metacognitive monitoring

Metacognition is the ability to monitor and regulate your own cognitive processes: knowing what you know and accurately identifying what you do not yet understand. Research by Bjork, Dunlosky, and Kornell (2013) reveals that untrained learners exhibit severe metacognitive illusions, mistaking perceptual fluency during reading for genuine mastery. Developing calibrated metacognitive skills enables learners to allocate study time efficiently, diagnose knowledge gaps objectively, and select appropriate cognitive strategies.

11. Sleep after study

Memory consolidation is an active biological process that depends critically on sleep architecture. During slow-wave non-rapid eye movement (NREM) sleep, the hippocampus repeatedly reactivates newly encoded neural firing patterns, transferring labile episodic representations to stable neocortical networks. Systematic reviews (Diekelmann & Born, 2010; Rasch & Born, 2013) confirm that sleeping within several hours of a learning session protects memories from retroactive interference and enhances structural problem-solving insight the following day.

12. Effortful practice (desirable difficulties)

In 1994, Robert Bjork introduced the concept of “desirable difficulties”—learning conditions that slow down initial performance and feel subjectively frustrating, but ultimately produce durable long-term retention and flexible transfer. Techniques such as spacing, interleaving, and generation testing are quintessential desirable difficulties: they force the cognitive architecture to work harder to encode and retrieve traces, signaling to biological memory systems that the knowledge is vital for survival.

How do you build your own study system from these strategies?

Attempting to implement all twelve strategies simultaneously creates operational paralysis. An effective self-learning routine combines two or three complementary core mechanisms into an automated daily workflow:

1. Phase 1: High-Structure Foundation: Begin new topics by studying worked examples and generating concrete analogies to anchor unfamiliar concepts.
2. Phase 2: Active Processing: As initial comprehension forms, transition immediately to elaborative self-explanation and concept mapping to verify structural relationships.
3. Phase 3: Automated Retention: Convert key definitions, principles, and problem variations into flashcards or recall prompts. Review them using spaced repetition and interleaved problem sets.
4. Phase 4: Objective Calibration: Check your answers against rigorous feedback standards, tracking missed items for targeted review.

To adopt a comprehensive, step-by-step framework that operationalizes these evidence-based techniques across your career and academic projects, explore The Self-Learning System.

How do you know a strategy is working?

The greatest hazard in self-regulated learning is judging strategy efficacy by how easy or fluent it feels during practice. Rereading a highlighted chapter feels smooth and effortless, creating a false impression of mastery. In contrast, retrieving answers from memory or solving interleaved problem sets feels halting and error-prone.

To evaluate whether a learning method is genuinely working, you must separate immediate performance vs learning. Performance is your temporary execution during a study session; learning is the durable change in long-term capability that persists across time. True strategy validation requires a delayed assessment: test yourself 48 hours or one week after practice, under closed-book conditions. If you can retrieve and apply the concepts after a delay, the strategy was successful.

Why do many learners prefer strategies that work less well?

The overwhelming preference for passive techniques—such as highlighting and re-reading—is driven by basic cognitive psychology. When reading familiar prose, semantic processing requires minimal working memory capacity. The brain misinterprets this absence of cognitive friction as evidence of deep comprehension, creating a powerful illusion of competence.

Traditional school systems exacerbate this problem by rewarding short-term memorization for Friday quizzes rather than testing for 30-day retention. Students quickly discover that massed cramming yields passing marks on immediate tests, reinforcing poor study habits throughout secondary and higher education. When these individuals later encounter complex professional domains requiring cumulative, durable competence, their intuitive study habits collapse.

How do learning strategies relate to skill acquisition?

While cognitive learning strategies are designed primarily for declarative and conceptual knowledge—such as facts, principles, and theories—physical and procedural competencies require distinct operational mechanics. Mastering a complex craft, sport, or performance discipline demands iterative motor repetition, real-time sensory feedback, and physical chunking.

Understanding how learning strategies interface with skill acquisition such as learning strategies bridges the divide between knowing a theory and executing a craft. High-utility cognitive methods accelerate the initial cognitive stage of skill learning, providing the mental models required before deliberate physical practice can refine automaticity.

Do learning styles change which strategies you should use?

A common educational belief claims that instruction should be customized to a student’s self-reported “learning style” (such as visual, auditory, or kinesthetic). Extensive scientific reviews (Pashler et al., 2008) have investigated this matching hypothesis and found no credible empirical evidence that tailoring instruction to preferred styles improves learning outcomes.

The definitive research on learning styles demonstrates that high-utility cognitive strategies—retrieval practice, distributed spacing, and interleaved categorization—benefit all human learners regardless of their personal preferences. What must vary is not the learner’s style, but the nature of the content: visual diagrams for spatial concepts, verbal explanations for linguistic nuance, and hands-on drills for physical mechanics.