Cognitive theories of instruction have been proliferated in the learning community in recent years and with good reason. Beyond animal research with rats and mazes, researchers now have tools to help them delve deeper into the brain's functioning. With new technologies, methodologies for brain research now include imaging technologies from neuroimaging (recording pictures of blood flow during certain activities) to electrical recording (use of electrodes to record brain wave patterns). Post-mortem and living case studies of people with brain injuries and psychological conditions have also been prominent in recent studies (Ormrod, Schunk, and Gredler, 2009). From this body of research, we know that humans have limited capacity for retaining information in short-term memory (STM) and that information is best learned when it is transferred to long-term memory (LTM) with elaboration and meaningfulness. Elaboration occurs when information is retrieved from the LTM in order to link to new information (Orey, 2001). There is also emerging evidence that mirror neurons fire when we watch someone else perform an action or when we perform it ourselves (Ormrod, Schunk, and Gredler, 2009). These discoveries have spurred the development of information processing theories far and wide, and have tremendous implications relative to experiential or apprentice style learning.
Information processing theories, in and of themselves, focus on how information is attended to, perceived, synthesized, and retrieved. Most use the computer metaphor, likening our sensory registers to input devices, our short-term memories to a central processing unit, and our long-term memories to hard drives (Orey, 2001). Other models discuss the processing of information in terms of physical, acoustic, and semantic levels with the semantic level of processing yielding the greatest learning and recall. In short, we can perceive, rehearse and hear, but we have learned when we understand. The learner attaches meaning to new information at the semantic level (Craik, 1979, Craik & Lockhart, 1972). In processing information, other theorists believe it is helpful to understand the type of knowledge being acquired: episodic, semantic, verbal, visual, declarative or procedural (Tulving 1972, 1983; Gupta & Cohen, 2002). What does this research mean for me as a practitioner?
From my experience, I know that students learn better when we can activate their prior knowledge and provide metacognitive tools to help them think about their learning. When I taught English and business applications, these tools included baseline testing as well is simple verbal checks at the beginning of lessons. For example, use of the prompt, “Tell me everything you know about . . .” could lead to intense discussions that let me know where my students were regarding subject matter as well as their interest level(s). Formal instruments or schemata for activating prior knowledge and guiding learning would include KWL Charts (Ogle, 1986), reading logs, and organizers. With regard to opportunities to practice learning, the recent developments concerning mirror neurons informs us that learning is more meaningful when it is visualized and/or practiced. I know this to be true from working in a project-based learning environment. My students learned business principles, but also practiced them as they created their own business plans and publications.
The goal of learning is to retain it for current or future application. As much as content is important, we have an imperative to help students learn how to learn. Evidence relative to the benefits of metacognitive strategies is rapidly mounting and should give us confidence to spend more time in this area as we plan our instruction. Two studies of note are Houtveen & Van de Grift (2007) and Marshall (2009). The studies are at opposite ends of the spectrum regarding participants, but each offers valuable insight on the benefits of metacognitve tools. Houtveen and Van De Grift studied hundreds of elementary teachers and students over two years with an experimental group being trained to teach and use metacognitive strategies in reading comprehension. The experimental group outperformed the control group and sustained these gains when tested in the same subject matter for the next year. Marshall (2009) explores the compatibility of cognitive and practice-based theories through a case study on an engineering project. He successfully illustrates how the two theories can be integrated in practice.
Reference List:
Craik, F.I.M. (1979), Human Memory, Annual Review of Psychology, 30, 63 – 102
Craik, F.I.M., & Lockhart, R.S. (1972) Levels of processing: A framework for memory research. Journal of Verbal Learning and Verbal Behavior, 11, 671 – 684
Gupta, P. & Cohen, N. J. (2002). Theoretical and computational analysis of skill learning, repetition priming, and procedural memory. Psychological Review, 109, 401-448.
Houtveen A.A.M., Van de Grift, W.J.C. (2007), Effects of Metacognitive Strategy Instruction and Instruction Time on Reading Comprehension, School Effectiveness and School Improvement, Vol. 18, No.2, June, 2007, pp. 173 – 190
Marshall, N. (2009), Cognitive and Practice-based Theories of Organizational Knowledge and Learning: Incompatible or Complementary?, Management Learning April 1, 2009 40: 129-144
Ogle, D. (1986). K-W-L: A teaching model that develops active reading of expository text. The Reading Teacher, 39, 564-571.
Orey, M. (2001). Information processing. In M. Orey (Ed.), Emerging perspectives on learning, teaching, and technology. Retrieved from http://projects.coe.uga.edu/epltt/index.php?title=Information_processing
Ormrod, J., Schunk, D., & Gredler, M. (2009). Learning theories and instruction (Laureate custom edition). New York: Pearson
Tulving, E. (1972). Episodic and semantic memory. In E. Tulving & W. Donaldson (Eds.), Organization of memory, (pp. 381–403). New York: Academic Press.
Tulving, E. (1983). Elements of Episodic Memory. Oxford: Clarendon Press.
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