Learning Science Terms Explained Clearly

Why Learning Science Vocabulary Matters

A student says: "I studied for 8 hours." What they did: re-read notes passively.

A teacher says: "This lesson reduces cognitive load." What they mean: reduces any mental effort (missing that some effort is desirable).

A trainer says: "We use active learning." What they mean: students talk more (not necessarily cognitive engagement).

Imprecise learning language leads to ineffective learning strategies.

Learning science—the interdisciplinary study of how people acquire knowledge and skills—has developed precise terminology over decades of research. These terms distinguish effective learning strategies from ineffective ones, productive difficulty from unproductive frustration, and genuine understanding from surface familiarity.

Most educational advice uses this vocabulary loosely, blurring critical distinctions. "Study more" could mean anything from passive re-reading (ineffective) to retrieval practice (highly effective). "Make it easier" could mean reducing cognitive load (helpful) or removing desirable difficulties (harmful).

Understanding these terms precisely helps you:

• Identify what actually works for learning
• Design better learning experiences
• Avoid strategies that feel productive but aren't
• Communicate clearly about learning processes

This is the vocabulary that separates evidence-based learning from educational folklore.

Core Memory and Retention Concepts

Spaced Repetition

Definition: Reviewing information at increasing intervals over time, rather than massing practice into short periods.

The principle (Ebbinghaus, 1885): Memory decays over time. Each retrieval strengthens the memory and extends the interval before next review is needed.

Optimal spacing: Gradually increasing intervals (e.g., 1 day, 3 days, 1 week, 2 weeks, 1 month...)

Why it works:

• Forces retrieval from memory (effortful, strengthening)
• Exploits optimal difficulty (not too easy, not impossible)
• Prevents illusion of mastery from repeated recent exposure

Contrast - Massed practice ("cramming"):

• Repeated review in short time
• Feels productive (fluency builds quickly)
• Produces weak long-term retention

Practice Type	Short-term Performance	Long-term Retention	Efficiency
Spaced	Moderate (forgetting between sessions)	High (durable)	High (fewer total repetitions needed)
Massed	High (fresh in memory)	Low (decays quickly)	Low (requires many repetitions)

Application: Use spaced repetition systems (SRS) like Anki for facts, vocabulary, concepts. Review material on increasing intervals, not repeatedly in one session.

Retrieval Practice

Definition: Actively recalling information from memory, rather than passively reviewing or re-reading.

The principle (Roediger & Karpicke, 2006): The act of retrieval itself strengthens memory—more than additional studying.

Forms:

• Free recall: "Write everything you remember about X"
• Cued recall: "What is the capital of France?"
• Recognition (weaker): "Which is the capital: Paris, London, Berlin?"
• Application problems: "Use concept X to solve this problem"

Why it works (Testing effect):

• Retrieval is effortful → strengthens memory trace
• Reveals gaps in knowledge (diagnostic)
• Produces better transfer than re-reading
• Creates retrieval routes for future access

Contrast - Re-reading:

• Passive exposure to information
• Produces fluency illusion ("I recognize this, so I know it")
• Weak learning benefit

Research finding (Karpicke & Blunt, 2011): Students who tested themselves learned more than students who created concept maps, despite spending same time and rating concept maps as more effective.

Application: After reading/lecture, close the book and write what you remember. Use practice tests. Explain concepts without looking at notes.

Desirable Difficulties

Definition (Bjork & Bjork): Learning conditions that introduce challenges making learning feel harder but produce better long-term retention, understanding, and transfer.

Key insight: Ease of learning ≠ durability of learning. Strategies that make learning feel smooth often produce weak retention.

Types of desirable difficulties:

Difficulty Type	What It Means	Why It Works	Example
Spacing	Gaps between practice	Forces retrieval	Study Monday, Wednesday, Friday vs. 3 hours Monday
Interleaving	Mixing problem types	Requires discrimination	Mix algebra, geometry, trig problems vs. blocking by type
Variation	Changing contexts, examples	Deepens understanding	Learn concept via multiple examples vs. same example repeatedly
Generation	Producing answers before seeing them	Active processing	Try solving before seeing solution
Testing	Retrieval practice	Strengthens memory	Self-test vs. re-read

The paradox: Desirable difficulties:

• Feel less fluent (you struggle more)
• Produce lower initial performance
• Create better long-term outcomes

Contrast - Undesirable difficulties:

• Confusing presentation
• Missing prerequisite knowledge
• Cognitive overload
• Poorly designed materials

These make learning harder without benefit—they're just obstacles.

Application: Don't avoid struggle. Choose learning methods that feel difficult but productive (testing, spaced practice, interleaving) over methods that feel easy (re-reading, massed practice).

Elaboration

Definition: Connecting new information to existing knowledge by explaining, questioning, and building relationships.

The principle: Memory is network-based. The more connections you create, the stronger and more accessible the memory.

Elaboration techniques:

• Elaborative interrogation: Ask "Why?" repeatedly
• Self-explanation: Explain concepts in your own words
• Making analogies: "X is like Y because..."
• Connecting to experience: "This relates to when I..."
• Elaborative encoding: Associate new info with vivid images, stories

Why it works:

• Creates multiple retrieval paths
• Engages deeper processing
• Reveals gaps in understanding
• Builds integrated knowledge structures

Example:

Shallow processing: "Mitochondria is the powerhouse of the cell" (memorize phrase)

Deep elaboration:

• Why is it called powerhouse? (produces ATP, cellular energy)
• How does it produce energy? (electron transport chain, chemiosmosis)
• What would happen if mitochondria failed? (cell death, diseases like mitochondrial myopathy)
• Analogy: Like a power plant converting fuel to electricity
• Connection: This explains why I feel fatigued when sick (cellular energy production affected)

Research: Elaboration dramatically improves retention and understanding compared to repetition.

Application: After encountering new information, don't just repeat it—explain why it's true, how it works, what it connects to, what would happen if it weren't true.

Interleaving

Definition: Mixing different topics or problem types within a single study session, rather than blocking (practicing one type at a time).

The principle: Forces discrimination between problem types and strengthens ability to recognize when to apply each approach.

Example - Math learning:

Blocked practice:

• 10 Area problems → 10 Volume problems → 10 Perimeter problems
• Easy (you know which formula to use based on block)
• Poor transfer (struggle to identify problem type on mixed tests)

Interleaved practice:

• Area, Volume, Perimeter, Area, Volume, Perimeter...
• Harder (you must identify problem type each time)
• Better transfer (learned to discriminate)

Why it works:

• Prevents automatic application without thinking
• Requires active discrimination ("Which type is this?")
• Strengthens ability to recognize when concepts apply
• Produces better long-term retention

Research (Rohrer & Taylor, 2007): Interleaved practice produced 43% better test performance than blocked practice in math, despite students rating blocked practice as more effective.

Application: When practicing, mix problem types. Don't complete all Chapter 3 problems, then all Chapter 4—mix them.

Cognitive Architecture Terms

Cognitive Load

Definition: The total mental effort required to process information in working memory.

The principle (Sweller, 1988): Working memory is limited (~4 chunks). Exceeding capacity prevents learning. Optimize load to maximize learning.

Types of cognitive load:

Type	Definition	Impact on Learning	Goal
Intrinsic	Inherent complexity of material	Unavoidable (depends on content)	Accept it; build prerequisites first
Extraneous	Load from poor presentation	Harmful (wastes capacity)	Minimize it
Germane	Load from building understanding	Beneficial (creates learning)	Optimize it

Example - Learning to factor quadratic equations:

Intrinsic load: Understanding algebraic manipulation (inherently complex)

Extraneous load:

• Confusing notation
• Irrelevant decorative images
• Poorly organized presentation
• Attention split between multiple sources

Germane load:

• Comparing worked examples
• Self-explaining steps
• Identifying patterns across problems

Design implication: Reduce extraneous load (clean presentation, clear organization), manage intrinsic load (proper sequencing, prerequisites), optimize germane load (productive struggle).

Common mistake: "Reduce cognitive load" often means "make it easier"—but you should only reduce extraneous load. Germane load (productive difficulty) is desirable.

Application: When struggling to learn, diagnose load type:

• Too hard because inherently complex? (Intrinsic → build prerequisites)
• Too hard because poorly presented? (Extraneous → find better resource)
• Hard in productive way? (Germane → embrace it, that's learning)

Schema

Definition: Organized knowledge structure that allows you to recognize patterns, understand relationships, and perform skills automatically.

The principle: Expert-novice difference is largely schema-based. Experts have rich, organized schemas; novices have disconnected facts.

Characteristics:

• Chunking: Compress multiple elements into single unit (expert sees "pattern X" where novice sees separate pieces)
• Automation: Skilled performance becomes automatic, freeing working memory
• Recognition: Instantly recognize situations and appropriate responses

Example - Chess:

Novice: Sees individual pieces, must think through each move

Expert: Recognizes board configurations as patterns ("This is a Sicilian Defense setup") and knows appropriate responses automatically

Chase & Simon (1973): Chess masters remember real game positions far better than novices, but no better at random piece positions (expertise is pattern recognition, not memory)

Application in learning:

• Build schemas deliberately: Connect new information to existing structures
• Practice until automatic: Repeated practice automates components
• Learn patterns: Study worked examples to build schema patterns

Why it matters: You can't think deeply about complex problems if you're using all working memory on basics. Automation (via schemas) frees capacity for higher-level thinking.

Working Memory vs. Long-Term Memory

Working Memory:

• Capacity: ~4 chunks (limited)
• Duration: Seconds without rehearsal
• Function: Active processing, conscious thought
• Analogy: Desk workspace (small, cluttered quickly)

Long-Term Memory:

• Capacity: Virtually unlimited
• Duration: Potentially permanent
• Function: Storage of knowledge, skills, experiences
• Analogy: Library (vast, but retrieval is key)

Key insight (Sweller): Learning is change in long-term memory. Working memory is the bottleneck—limited capacity restricts what can be processed and transferred to long-term memory.

Implication for learning:

• Reduce working memory demands: Break complex material into manageable chunks
• Use long-term memory to support working memory: Automate basics so advanced concepts don't overload capacity
• Build schemas: Compress information in long-term memory so it occupies less working memory when retrieved

Example - Reading comprehension:

Poor readers: Use working memory to decode words → no capacity left for comprehension

Skilled readers: Decoding is automatic (stored in long-term memory) → working memory free for understanding meaning

Metacognitive Terms

Metacognition

Definition: Thinking about your own thinking—monitoring, evaluating, and regulating your cognitive processes.

Two components:

1. Metacognitive knowledge: Understanding how you learn, what strategies work, when to use them

2. Metacognitive regulation: Monitoring and controlling your learning

• Planning: Selecting strategies, setting goals
• Monitoring: Tracking comprehension ("Do I actually understand this?")
• Evaluating: Assessing whether strategies are working
• Regulating: Adjusting approach based on monitoring

Why it matters: Metacognition is one of the strongest predictors of learning success—often more important than IQ or prior knowledge.

Common metacognitive failures:

• Illusion of competence: Familiarity feels like knowledge (re-reading produces fluency, not learning)
• Dunning-Kruger: Low performers overestimate ability (they don't know what they don't know)
• Overconfidence: Students feel they've learned more than they have

Building metacognition:

• Self-testing: Reveals what you actually know vs. what feels familiar
• Self-explanation: Explains concepts without notes (exposes gaps)
• Reflection: "What did I learn? What's still unclear? What strategy worked?"
• Error analysis: "Why did I get this wrong? What should I do differently?"

Application: After studying, ask: "Could I teach this to someone else? Could I apply it in a new situation? Do I actually understand, or does it just feel familiar?"

Transfer

Definition: Applying knowledge, skills, or strategies learned in one context to new, different situations.

Types:

Transfer Type	Description	Difficulty	Example
Near transfer	Similar context, similar problem	Easier	Practice problems → exam problems in same course
Far transfer	Different context, different surface features	Harder	Apply statistics from psychology course to business decisions
Positive transfer	Prior learning helps new learning	Beneficial	Spanish → Portuguese (similar grammar)
Negative transfer	Prior learning interferes with new learning	Harmful	QWERTY keyboard → Dvorak keyboard

The challenge: Transfer is hard. Students often fail to apply knowledge outside original learning context.

Why transfer fails:

• Knowledge is context-bound (learned in specific situation, doesn't generalize)
• Surface features distract from deep structure
• Lack of abstract understanding (only know procedures, not principles)

Promoting transfer:

• Variation: Learn concept through multiple examples, contexts
• Abstract principles: Teach underlying structure, not just surface procedures
• Comparison: Compare examples to extract common patterns
• Application practice: Explicitly practice applying knowledge in new contexts

Example - Physics:

Poor transfer: Learn F=ma in physics class, don't apply it to engineering problem (different context)

Better transfer: Learn F=ma through multiple contexts (cars, rockets, falling objects, collisions) + explicit principle extraction → recognize when to apply in new situations

Application: Don't just practice in one context. Deliberately practice applying knowledge in varied, novel situations.

Fluency Illusion

Definition: Mistaking ease of processing (fluency) for depth of learning—feeling you know something because it seems familiar.

The problem: Fluency is a terrible indicator of learning.

What creates fluency:

• Repeated exposure (reading same notes multiple times)
• Recent exposure (just saw it)
• Clear presentation (easy to process)

Why it misleads:

• You confuse "I recognize this" with "I understand this"
• You stop studying too early (it feels mastered)
• You don't realize you couldn't retrieve it without the prompt

Classic example (Karpicke, 2009):

• Students read text, rate understanding as high
• When tested, perform poorly
• They confuse fluency (ease of reading) with comprehension

Contrast - Disfluency can signal deeper learning:

• Struggling to retrieve (retrieval practice) feels hard but produces strong learning
• Interleaving feels difficult but produces better transfer
• Generation (producing answer before seeing it) feels frustrating but strengthens memory

Avoiding fluency illusion:

• Self-test without prompts: Can you explain without notes?
• Delayed testing: Can you recall days later?
• Application: Can you use it in new situations?

Application: If learning feels too easy, you're probably not learning much. Struggle (desirable difficulty) is a better sign of effective learning than fluency.

Learning Strategy Terms

Active Learning

Definition (Proper use): Learning that requires cognitive engagement—actively processing, generating, connecting information (not passive reception).

True active learning:

• Self-explanation
• Problem-solving
• Elaboration
• Retrieval practice
• Teaching others

Not necessarily active learning:

• Listening to lecture (even if attentive)
• Watching video (even if taking notes)
• Reading text (even if highlighting)
• Group discussion (unless genuinely thinking, not just talking)

Common misuse: "Active learning" often means "students are doing something" (talking, moving, using technology). But activity ≠ cognitive engagement.

Key distinction: Physical activity or social activity doesn't guarantee cognitive activity. What matters is whether students are thinking deeply.

Application: Judge learning activities by cognitive engagement, not physical activity. Silent self-testing can be highly active; loud group work can be passive.

Distributed Practice

Definition: Spreading learning over multiple sessions separated by time (synonym for spaced practice).

Contrast - Massed practice: Concentrating practice into single session (cramming).

Why distributed practice works:

• Spacing effect (memory strengthened by retrieval after delay)
• Prevents interference (massed practice causes fatigue, attention lapses)
• Promotes consolidation (sleep between sessions helps memory)

Practical implication: Study in shorter sessions over days/weeks rather than marathon session before exam.

Generation Effect

Definition: Producing information (generating) leads to better retention than passively receiving information.

Examples:

• Attempt to solve problem before seeing solution (even if you fail)
• Fill in missing words in text (rather than reading complete text)
• Predict what happens next (before reading)
• Create your own examples (rather than reading provided examples)

Why it works:

• Engages active processing
• Reveals gaps in knowledge
• Creates stronger encoding
• Builds retrieval routes

Research: Even generating wrong answers (before seeing correct answer) improves learning compared to just seeing correct answer.

Application: Before looking at solution, genuinely attempt to solve/answer/explain. The generation effort—even when unsuccessful—enhances learning.

Encoding Specificity

Definition (Tulving & Thomson, 1973): Memory retrieval is better when conditions at retrieval match conditions at encoding.

Implications:

• Context-dependent memory: Remember better in same physical location
• State-dependent memory: Remember better in same physiological/emotional state
• Encoding-retrieval match: If tested in written format, study by writing (not just reading)

Example:

• Study underwater → recall better underwater (Godden & Baddeley, 1975)
• Study while happy → recall better when happy
• Study with classical music → recall better with same music

Practical application:

1. Study in varied contexts (prevents memory from being too context-bound)
2. Practice in test-like conditions (match encoding to expected retrieval)
3. Use multiple modalities (creates multiple retrieval routes)

Practical Application: Using Learning Science

Diagnosing Learning Problems

Problem: "I studied but still did poorly"

Precise diagnosis:

• Did you re-read (passive) or practice retrieval (active)?
• Did you mass practice (cramming) or space practice (distributed)?
• Did you block by topic or interleave topics?
• Did you judge readiness by fluency (recognition) or retrieval (recall)?
• Was cognitive load extraneous (poor presentation) or germane (productive difficulty)?

Different diagnosis → different solution

Evidence-Based Study Strategy

Based on learning science:

1. Spaced repetition: Study material on day 1, 3, 7, 14, 30...

2. Retrieval practice: Close book, write what you remember. Self-test frequently.

3. Interleaving: Mix topics rather than blocking by chapter.

4. Elaboration: Explain why/how, make connections, create examples.

5. Variation: Study concept through multiple examples/contexts.

6. Test in realistic conditions: Match study format to test format.

7. Monitor metacognition: Ask "Do I truly understand or does it just feel familiar?"

What to avoid:

• ✗ Re-reading notes repeatedly
• ✗ Highlighting (passive)
• ✗ Cramming everything the night before
• ✗ Studying only one topic until mastered, then moving on
• ✗ Judging mastery by familiarity rather than retrieval

Communication with Educators

Imprecise: "Use active learning and reduce cognitive load"

Precise: "Use retrieval practice (active) rather than re-reading (passive), reduce extraneous load (simplify presentation) while maintaining germane load (desirable difficulties), and space practice across multiple sessions"

Different request → different implementation

The Meta-Principle

Learning science terminology exists to distinguish effective strategies from ineffective strategies that feel effective.

Key patterns:

• Effortful ≠ bad: Desirable difficulties, germane load, retrieval practice feel hard but work
• Fluent ≠ learned: Re-reading, recognition, massed practice feel easy but produce weak learning
• Active ≠ moving: Cognitive engagement matters more than physical activity

Understanding these terms helps you:

• Identify what works: Retrieval practice > re-reading
• Embrace productive struggle: Desirable difficulties > illusion of mastery
• Monitor accurately: Test yourself, don't trust fluency
• Design better learning: Apply evidence-based strategies

The vocabulary of learning science is the vocabulary of effective learning.

Use it precisely.

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Essential Readings

Foundational Texts:

• Brown, P. C., Roediger, H. L., & McDaniel, M. A. (2014). Make It Stick: The Science of Successful Learning. Cambridge, MA: Harvard University Press. [Accessible synthesis of key concepts]
• Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). "Improving Students' Learning with Effective Learning Techniques." Psychological Science in the Public Interest, 14(1), 4-58. [Evidence-based strategy review]

Memory and Retention:

• Roediger, H. L., & Karpicke, J. D. (2006). "Test-Enhanced Learning." Psychological Science, 17(3), 249-255. [Retrieval practice research]
• Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T., & Rohrer, D. (2006). "Distributed Practice in Verbal Recall Tasks." Psychological Bulletin, 132(3), 354-380. [Spacing effect meta-analysis]
• Bjork, E. L., & Bjork, R. A. (2011). "Making Things Hard on Yourself, But in a Good Way." In M. A. Gernsbacher et al. (Eds.), Psychology and the Real World (pp. 56-64). [Desirable difficulties]

Cognitive Load Theory:

• Sweller, J. (1988). "Cognitive Load During Problem Solving." Cognitive Science, 12(2), 257-285. [Original CLT paper]
• Sweller, J., van Merriënboer, J. J. G., & Paas, F. (2019). "Cognitive Architecture and Instructional Design: 20 Years Later." Educational Psychology Review, 31(2), 261-292. [Updated review]

Transfer:

• Barnett, S. M., & Ceci, S. J. (2002). "When and Where Do We Apply What We Learn?" Psychological Bulletin, 128(4), 612-637. [Transfer difficulties]
• Perkins, D. N., & Salomon, G. (1992). "Transfer of Learning." International Encyclopedia of Education (2nd ed.). Oxford: Pergamon Press.

Metacognition:

• Schraw, G., & Dennison, R. S. (1994). "Assessing Metacognitive Awareness." Contemporary Educational Psychology, 19(4), 460-475.
• Dunning, D. (2011). "The Dunning–Kruger Effect." Advances in Experimental Social Psychology, 44, 247-296. [Metacognitive failure]

Practical Application:

• Weinstein, Y., Madan, C. R., & Sumeracki, M. A. (2018). "Teaching the Science of Learning." Cognitive Research: Principles and Implications, 3(2). [Evidence-based teaching strategies]
• Bjork, R. A., Dunlosky, J., & Kornell, N. (2013). "Self-Regulated Learning." Journal of Applied Research in Memory and Cognition, 2(3), 173-177.

Comprehensive Resources:

• Bransford, J. D., Brown, A. L., & Cocking, R. R. (Eds.). (2000). How People Learn. Washington, DC: National Academy Press.
• Mayer, R. E. (2011). Applying the Science of Learning. Boston: Pearson.

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