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9 How Much and What Type of Guidance is Optimal for Learning ...

9 How Much and What Type of Guidance is Optimal for Learning ...

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<strong>How</strong> <strong>Much</strong> <strong>and</strong> <strong>What</strong> <strong>Type</strong> <strong>of</strong> <strong>Guidance</strong> <strong>is</strong> <strong>Optimal</strong>? 1770123456789012345678901234567890123456problems to choose a solution strategy <strong>and</strong> then reason <strong>for</strong>ward as they use trial<strong>and</strong> error to check successive solutions to solve problems. Th<strong>is</strong> approach <strong>is</strong> veryinefficient <strong>and</strong> not very effective <strong>for</strong> most per<strong>for</strong>mance objectives. Expertsinstantly classify problems into principled categories <strong>and</strong> reason <strong>for</strong>ward as theyimplement effective <strong>and</strong> efficient solution strategies connected to the categories.If some novices can learn to solve problems like experts, why not accelerate th<strong>is</strong>process <strong>for</strong> all novices by showing them how experts solve domain problems <strong>and</strong>give them the necessary declarative <strong>and</strong> procedural knowledge to imitate expertprotocols? I appreciate the opportunity to make clear what I only implied in mychapter—that fully guided instruction based on newer cognitive task analys<strong>is</strong>(CTA) strategies <strong>is</strong> intended to teach novices to reason like domain experts.Question: W<strong>is</strong>e <strong>and</strong> O’Neill. Constructiv<strong>is</strong>ts propose that simplifying a complexdomain <strong>for</strong> the purpose <strong>of</strong> teaching may encourage learners to take inappropriatelysimple approaches to complex problems. True domain experts are aware <strong>of</strong> the limits<strong>of</strong> the knowledge they work with; <strong>is</strong> th<strong>is</strong> not a facet <strong>of</strong> expert<strong>is</strong>e that should be representedin instruction?Reply: Clark. You ask two questions—one about inappropriate simplification<strong>and</strong> another about domain experts. I respect your concern about inappropriatesimplification <strong>of</strong> complex problems. No one wants to make the m<strong>is</strong>take youdescribe. I changed my mind slowly about th<strong>is</strong> <strong>is</strong>sue by looking at evidence fromthe systematic programs <strong>of</strong> inquiry by instructional researchers such as JohnAnderson. Anderson’s ACT- R model includes the cognitive <strong>and</strong> neural mechan<strong>is</strong>msthat support learning from instruction <strong>and</strong> he has applied it to the teaching<strong>of</strong> math, language, problem- solving, reasoning, <strong>and</strong> v<strong>is</strong>ual perception. H<strong>is</strong>model has been translated into instructional programs that successfully supportstudent learning <strong>and</strong> transfer <strong>of</strong> some <strong>of</strong> the most complex instructional tasks<strong>and</strong> problems.Anderson’s view <strong>is</strong> that all complex learning <strong>is</strong> a product <strong>of</strong> the gradual accumulationor “scaling” <strong>of</strong> simple learning into more complex assemblies <strong>of</strong> knowledge.For example, Lee <strong>and</strong> Anderson (2001) re- analyzed learning data collectedfrom the very complex Kanfer–Ackerman Air Traffic Controller Task (Ackerman,1988; Ackerman & Kanfer, 1994) <strong>and</strong> demonstrated convincingly that thelearning in th<strong>is</strong> complex task reflects the gradual build- up <strong>of</strong> small,procedural- knowledge chunks starting at the keystroke level. They also demonstratedthat a large portion <strong>of</strong> the learning at the keystroke level reflected learningat an even lower, attentional level. The overall execution speed <strong>of</strong> the necessarycomplex cognitive skills increased according to Anderson’s power law <strong>of</strong> practice.<strong>What</strong> was most interesting in th<strong>is</strong> (<strong>and</strong> many <strong>of</strong> Anderson’s other studies) <strong>is</strong>that the process by which trainees first learned <strong>and</strong> then assembled the individualsubskills explained more <strong>of</strong> the variance in whole- task per<strong>for</strong>mance than fittingany single subtask to the overall task. So the sequential learning <strong>and</strong> gradualautomating <strong>and</strong> interconnecting <strong>of</strong> larger sets <strong>of</strong> component attention <strong>and</strong> cognitivesubskills was a necessary <strong>and</strong> sufficient condition <strong>for</strong> optimal per<strong>for</strong>manceat a highly complex task.

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