Title :
Systems model for learning
Author :
Buriak, Philip ; McNurlen, Brian ; Harper, Joe
Author_Institution :
Dept. of Agric. Eng., Illinois Univ., Urbana, IL, USA
Abstract :
Develops a model of learning that differs greatly from traditional or intuitive models. This hard system is specifically designed for the context of problem-solving/higher-order thinking, rather than automatic learning. Research in educational psychology and cognitive science provides the basis for the model. Learning is the integration of new knowledge/behaviors into a framework, and subsequently recalling what is relevant in the appropriate situation. To understand learning, we must consider how new information is received and the stages through which new information is processed as it progresses from immediate sensory experience to long-term storage. It is also important to understand how novices and experts organize, analyze or encode, and then retrieve necessary information. In this particular case, engineering students are the novices and engineering educators are the experts. Teaching consists of organizing, planning, delivering and evaluating the content of the subject area. Teaching problem-solving in science requires a deep understanding of the subject matter, as well as an appreciation of the characteristics of the students, of presentation skills, and of evaluation techniques. This study presents a soft systems model for the craft of teaching, and develops a hard systems model for the science of learning
Keywords :
education; engineering education; problem solving; psychology; system theory; teaching; cognitive science; educational psychology; engineering educators; engineering student characteristics; evaluation techniques; experts; hard systems model; higher-order thinking; immediate sensory experience; information analysis; information encoding; information organization; information retrieval; learning model; long-term storage; new behaviours; new information; new knowledge integration; novices; presentation skills; problem-solving; relevance recall; soft systems model; subject content evaluation; teaching model; Agricultural engineering; Biological information theory; Biological system modeling; Cognitive science; Education; Engineering students; Humans; Information analysis; Information retrieval; Psychology;
Conference_Titel :
Frontiers in Education Conference, 1995. Proceedings., 1995
Conference_Location :
Atlanta, GA
Print_ISBN :
0-7803-3022-6
DOI :
10.1109/FIE.1995.483022