Art Graesser , Anne M. Lippert , Keith T. Shubeck
Knowledge Representation and Acquisition
- People construct mental representations when they experience the social, physical, and digital world. Our perceptions are not exact copies of the world, but are simplified with errors and missing information. Learning and performance on tasks are influenced by how our knowledge is represented.
- This chapter has reviewed the different types of representations that have been proposed by researchers in the cognitive and learning sciences who investigate adult learning of different subject matters. The types of representations include (1) ensembles of knowledge components, (2) knowledge structures, (3) associationistic neural networks, (4) embodied perceptions, actions, and emotions, (5) conversation, and (6) distributed cognition with diverse multimedia and technologies.
- Knowledge of a specific subject matter is represented by a set of knowledge components which express ideas relevant to the topic. Knowledge structures consist of nodes, which represent concepts, states, events, goals or processes, and arcs that connect the nodes with different types of relations (e.g., is-a, has-a, contains, causes). Four example knowledge structures were discussed: taxonomic, spatial, causal, and goal-action procedures.
- Neural networks model associationistic representations with neuron nodes connected by associative weights. The strengths of the associations are determined by repetition, similarity, how often nodes co-occur in time, and positive versus negative outcomes.
- Knowledge representations and acquisition are influenced by our human experience and how we interact with our environment. Embodied representations capture perception, action, and emotion. Conversational representations include the social discourse we observe and enact with families, tutors, mentors, and groups.
- Digital technologies will continue to shape and constrain the mental representations and influence how people learn. These technologies are making information about topics more distributed across people, times, locations, and media sources.