Human cognitive architecture and its implications for the design of instruction: Introduction to cognitive load theory Slava Kayuga



Working memory (WM)  Information enters WM once it has been selected by allocating attention to it  We have limited attention because of limitations of WM  Corresponds to consciousness or awareness: we are conscious of everything that is in WM

Repeat a telephone number = ? = ? Working Memory Taking notes – extension of WM What have you been doing just before this?

 Early models of memory referred to STM; it is still commonly used today  STM was thought of in terms of only storing information (temporarily remembering)  Baddeley and Hitch (1974): we not only store information for short periods of time but also process information - hence WM Short-term or working memory?

WM capacity  Miller (1956) demonstrated that we have a short-term memory span of 7 ± 2 units of information – storage capacity  Reconsideration of WM capacity when processing is involved ( Cowan, 2001 )  In terms of processing information, 4 is a more likely number than 7

Suppose 5 days after the day before yesterday is Friday. What day of the week is tomorrow? WM processing capacity

WM duration Brown (1958); Peterson & Peterson (1959): When people are distracted from rehearsing, information is lost rapidly (e.g., after 18 sec – everything was forgotten)

Allocates resources to other systems- governs what enters WM Director of cognitive work- selects strategies Not a store or processor Executive Control System Controls the Operations of Working Memory Phonological Loop Auditory Rehearsal WM Structure Baddeley 1986, 2001 Visual-spatial Sketch Pad Visual Rehearsal Processes visual images Spatial processing Holds acoustic or speech-based information Auditory rehearsal of verbal information

Close your eyes and pick up an object in front of you How many windows are in your house? Working Memory Repeat an unfamiliar foreign word

Long-term memory (LTM)  permanent repository of the lifetime of accumulated information  unconscious component of our memory: we are not conscious of LTM information until it is activated and brought into WM  WM and LTM are two major components of Human cognitive architecture

CIABBCABCBHPAMP Role of LTM

Effective WM capacity Miller (1956): short-term memory span is 7 ± 2 chunks of information What each chunk consists is dependent on our knowledge stored in LTM What is in LTM would affect the way we process information in WM

Information-“rich” chunks Chunking information into meaningful parts has the effect of expanding the capacity of working memory Examples: a Chinese character; a written English word; newspaper vs textbook Effective WM capacity

Chess studies  Compared performance of chess masters and weekend players  Question: Do chess masters look ahead more moves? Consider a greater number of alternative moves?  Answer: verbal protocols showed NO difference between chess masters and weekend players de Groot (1966)

 Investigated: players’ memory of chess boards  Tested: master’s vs. weekend player’s memory for real and random board configurations after brief (5 sec) exposure  Results: masters were superior in reconstructing real game configurations (80-90% correct compared to weekenders’ 30-40%) but NOT random configurations  Conclusion: Superiority was due to greater amount of real-game chunks in master’s LTM Chess studies de Groot (1966); Chase & Simon (1973)

 Grand masters have extensive and better organized LTM knowledge base  thousand configurations, at least 10 years of experience  This study radically changed our view on the role of LTM in human cognition  LTM is not just for memorizing things, but is the most critical component of our cognition (including learning), the source of our intellectual strength Role of LTM

 Grand masters read the chess board the same way you read words in a text  Similar mechanisms for all high-level cognitive skills (e.g., text comprehension)  LTM - not a passive store of information; it is actively used in most of cognitive processes and is central to perception, learning, problem solving LTM in human cognition

“Organized structures that capture knowledge and expectations of some aspect of the world” (Bartlett, 1932) Organized knowledge structures that represent generic concepts and categorize information according to the way in which we use it Schemas (schemata)

table toast chair butter knife jam fork cloth spoon juice cup bowl plate tea What is this list about?

Schemas Examples: a tree schema a face schema reading a page of prose: schemas for letters, words, phrases, sentence structures Restaurant script (procedural schema)

 Schema theory is the most commonly used framework for understanding LTM  Memory is actively constructed using schemas  Pre-existing schemas determine what incoming material is relevant Relevant material processed Irrelevant material discarded Schemas as major building blocks of cognition

Schema automation Schema automation is achieved by practicing skills until they do not require consciously controlled and effortful processing. When basic mental operations occur automatically, resources are available for more sophisticated cognitive operations (e.g., reading, math operations, etc.)

Automation Explains why individuals can conduct difficult tasks simultaneously conduct several tasks read for meaning rather than focus on the individual letters and words be accomplished performers (e.g., musicians) Automation is slow to develop and requires significant practice

Schemas  Schemas affect not only what we memorize, but how we think, reason, solve problems  Intelligence – in number and complexity of acquired schemas  Nature of expertise

Expert characteristics: Domain-specific knowledge  Experts have a large store of domain-specific schemas for problem solving in the domain  Automated schemas reduce WM demands and allow higher order functions (monitoring, evaluating etc.)  Experts deal with problems at a deeper level: categorize according to deep structures (principles) rather than surface structures

Expert characteristics: Treatment of problem Task: categorize the following into 3 groups Soldiers, 1492, discovery, kings & queens, 1914, revolution, sailors, war, Surface structure grouping: 1492, 1914, 1789 Deep structure grouping: 1789, Kings and Queens, revolution (French Revolution) Physics experts classified problems according to the laws of physics rather than surface structures (e.g. Chi, Glaser & Farr, 1988)

Implications for improving problem solving  Acquisition of extensive domain-specific knowledge (schemas) is essential: the only way to be good in problem solving  broken car: we call a mechanic (an expert), not a general “problem solver”  You can become expert problem solver in a specific area, not in every area  Studying expert solutions  emphasising higher-order skills, categorization of problems

Analysis of the task domain to identify core schemas:  After 6 passengers had left the bus, 9 passengers remained. How many passengers were on the bus initially? (Change Schema)  Peter's book contains 50 pages. Peter read 15 pages in the morning. In the afternoon, he read the remaining pages and finished the book. How many pages did Peter read in the afternoon? (Group Schema) etc. Arithmetic word problems ( Marshall, 1995)

Go Solve Word Problems Tom Snyder ProductionsProductions

Do not overload WM! If material is difficult to learn, learner WM is likely to be overloaded Manage information-processing “bottleneck” by chunking information into meaningful groups based on available knowledge Help students to link new information with prior knowledge Instructional implications

Enhance acquisition and automation of knowledge in LTM - a major goal Use dual modality (visual and auditory) Minimise interference /distractions Provide adequate time to enable processing Instruction that requires many inferences (things are not stated explicitly) overloads WM Instructional implications

 Instructional theory that takes into account limitations of learner working memory  Cognitive load (working memory load): working memory capacity required by a particular cognitive task  Cognitive load depends on the level of interactivity between elements of information Cognitive Load Theory Sweller 1999; Sweller, Ayres & Kalyuga, 2011

List of variables: a, x, b Equation: ax=b Names of electrical symbols and what they represent Operation of an electrical circuit LowHigh Element interactivity Learning vocabulary of a foreign language Learning grammar

Objective measures  Task and performance  Secondary task  Psychophysiological Subjective measures  Rating scales Measurement of Cognitive Load

Objective measures  Secondary task Rapid RT Slow RT Cognitive resources to simple primary task Cognitive resources to complex primary task Fixed cognitive capacity Resources to secondary task

very, very low mental effort very, very high mental effort neither low nor high mental effort In solving or studying the preceding problem I invested: Subjective measures  Rating scales

Subjective measures: Rating scales (NASA-TLX)

 Useful, productive load (intrinsic load) – relevant to achieving learning goals  determined by the degree of element interactivity  depends on specific instructional goals and prior knowledge of the learner (chunking!)  Wasteful, unproductive load (extraneous load) - irrelevant to learning, imposed by the manner in which information is presented to learners and the learning activities required of them  dependent on the design of instruction Intrinsic + Extraneous =Total cognitive load Types of cognitive load

Efficient learning  Managing intrinsic (productive) load  Reducing extraneous (wasteful) cognitive load  General rule: Do not do anything that gets in the way of learning!  If intrinsic load is low (simple tasks), there could be no need to reduce extraneous load

References  Sweller, J., van Merriënboer, J. J. G., & Paas, F. G. W. C. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10,  Van Merriënboer, J. J. G., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 17,