Generative Learning Processes Of The Brain

Wittrock, M.C. (1992). Generative learning processes of the brain. Educational Psychologist, 27(4), 531-541.

This article presents a functional model of learning from teaching that, in contrast to structural models of schemata and knowledge representation, focuses on the neural and cognitive processes that learners use to generate meaning and understanding from instruction. Wittrock's model of generative learning (Wittrock, 1974a, 1990) consists of four major processes: (a) attention, (b) motivation, (c) knowledge and preconceptions, and (d) generation. Each of these processes involves generative brain functions studied in neural research and generative cognitive functions studied in knowledge-acquisition research. In this model of generative learning, the brain is a model builder. It does not transform input into output. Instead, it actively controls the processes of generating meaning and plans of action that make sense of experience and that respond to perceived realities. Within this framework, teaching becomes the process of leading learners to use their generative processes to construct meanings and plans of action.

If we look at Wittrok's generative model of learning, we can correlate the four major processes of attention, motivation, knowledge and preconceptions, and generation into the Interaction design process. Specifically, designers need to design the interactio within an interface and platform to attract the attention of the end user population. This can be through visual and auditory stimuli (such as an avatar guide and or sound used for feedback on a recently comlpeted action or task). The use of these avitors or goals for a platofrm can enbale the end user to be motiivated. For example, the student is motivated to copmplete a game to achieve some form of goal or prize at the end. Knowledge and Preconceptions are taken into consideration in terms of how an interface is designed from an information and interaction perspective. Ultimately, these interactions will enable the end user to generate leaning and mental models based on their experinces in the platform.

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