Working Memory Support Strategies¶

Strategy · All strategies
Evidence · 7 claims (4 for, 2 mixed, 1 against) · 20 studies (10 causal, 6 review, 3 quant-synthesis, 1 theoretical), q1–q4 · 4 of 20 report an effect size · 1 claim rests on one study

Description¶

Working memory support strategies are instructional design techniques that manage the severe capacity limits of working memory — roughly three to five novel elements for most adults Cowan's embedded-processes model. [+S]. They work through three mechanisms: reducing intrinsic load (segmenting, Chunking, pretraining), minimizing extraneous load (removing redundancy, integrating sources, clear structure), and externalizing content (graphics, organizers, worked examples) so learners need not hold intermediate states in mind. The approach is grounded in Cognitive Load Theory [+S].

Design Implications¶

Every instructional decision should be evaluated against the question: what must the learner hold in mind right now? Material that exceeds working-memory capacity produces failure to encode regardless of motivation or quality of explanation Cognitive load theory: instructional consequences of limited working memory. [+S]. Supports should be faded as schemas form and load is freed Worked-example guidance becomes less effective as learner expertise increases. [~M].

Context¶

Requirements¶

• Task analysis identifying which elements are essential (intrinsic) and which are design artifacts (extraneous)
• Segmentation of complex material into learner-paced units
• Integration of text and graphics rather than split sources (Dual Coding Theory) [+M]
• A plan for fading support as expertise develops

Constraints¶

• Support becomes harmful for knowledgeable learners: redundant guidance imposes extra load on those who can self-manage, the expertise reversal effect [-S]
• Over-segmenting can fragment a coherent schema; learners may process pieces without integrating them [~M]
• Offloading content to graphics backfires when the graphic is decorative rather than relevant Decorative illustrations do not improve learning. [-M]
• Learners with very low prior knowledge may not benefit from learner-controlled pacing, since they cannot judge when they have understood a segment [~W]

Implementation Variability¶

• Segmenting and pacing: break animation or narration into learner-controlled segments Clark & Mayer's segmenting principle. [+S]
• Pretraining: teach names and characteristics of key concepts before the main task
• Worked examples: Example-problem sequences reduce cognitive load. [+M]
• External representations: Graphic organizers support novice comprehension. [+M]; note that learner-constructed organizers can outperform provided ones once learners are capable Learner-constructed graphic organizers outperform provided. [~M]
• Language simplification: Accessible Vocabulary & Syntax reduces decoding load for language learners

Target Learners¶

• Novices, who lack schemas to chunk incoming information and therefore face the highest element interactivity Chunking reduces working memory load. [+S]
• Learners with working-memory limitations, including many learners with ADHD or specific learning disabilities; see Accommodations
• Less beneficial — potentially harmful — for experts, per the expertise reversal effect [-S]

Target Learning Goals¶

• Complex procedural and conceptual learning with high element interactivity
• Schema construction: consolidating elements into single retrievable units
• Foundational knowledge that later learning will build on

Instructions¶

1. Analyze the task to estimate element interactivity; identify what is essential vs. design-induced load.
2. Segment the material into coherent, learner-paced units (Chunking).
3. Remove extraneous load: eliminate redundancy, integrate text with graphics, apply Clear Structure.
4. Provide external supports — Advance Organizers, worked examples, Analogies — to hold structure the learner cannot yet.
5. Fade supports as performance improves, shifting from examples to problems to independent work Fading support promotes transfer of responsibility. [+M].

Related Strategies¶

• Chunking — the core unit-of-analysis move; grouping elements so they occupy one slot rather than several
• Segmenting — time-based division of dynamic material
• Pretraining — front-loading vocabulary and concepts to lower intrinsic load of the main task

Examples¶

• Khan Academy — short, segmented videos with learner-controlled pacing and immediate practice, embodying segmenting and example-problem sequencing.
• Worked Examples in algebra instruction — Sweller & Cooper's original studies replaced early problem solving with studied solutions, improving novice performance doi:10.1207/s1532690xci0201_3.
• Mayer's multimedia principles — narrated animation with integrated labels rather than on-screen text plus narration, reducing split attention doi:10.1002/9781119239086.

Key Sources¶

• Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. doi:10.1207/s15516709cog1202_4
• Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24(1), 87–114. doi:10.1017/S0140525X01003922
• Sweller, J., & Cooper, G. A. (1985). The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction, 2(1), 59–89. doi:10.1207/s1532690xci0201_3
• Clark, R. C., & Mayer, R. E. (2016). E-Learning and the Science of Instruction (4th ed.). Wiley. doi:10.1002/9781119239086
• Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38(1), 1–4. doi:10.1207/S15326985EP3801_1