Abstract
This paper investigates how control theory could be applied to learning processes in engineering education. The initial point for the analysis is White's Double Loop learning model of human automation control modified for the education process where a set of governing principals is chosen, probably by the course designer. After initial training the student decides unknowingly on a mental map or model. After observing how the real world is behaving, a strategy to achieve the governing variables is chosen and a set of actions chosen. This may not be a conscious operation, it maybe completely instinctive. These actions will cause some consequences but not until a certain time delay. The current model is compared with the work of Hollenbeck on goal setting, Nelson's model of self-regulation and that of Abdulwahed, Nagy and Blanchard at Loughborough who investigated control methods applied to the learning process.
Highlights
The role of learning theory in Engineering Higher education in the UK has only been a topic of serious investigation since the 1970’s following the expansion of higher education in the late 1960’s, leading to the integrated use of the student centred project method of teaching
Testing concepts in new situations These learning concepts can be mapped to the SARTOR (Engineering Council [4]) three part scheme: Knowledge; Skills and Understanding
All the aspects of prior education, environment and poor judgement combine to make a given strategy and subsequent decisions. This model shows how difficult it is to isolate the circumstances for a successful learning process
Summary
This content has been downloaded from IOPscience. Please scroll down to see the full text. 65 012033 (http://iopscience.iop.org/1757-899X/65/1/012033) View the table of contents for this issue, or go to the journal homepage for more. Download details: IP Address: 193.60.78.43 This content was downloaded on 19/12/2014 at 17:20 Please note that terms and conditions apply. 27th International Conference on CADCAM, Robotics and Factories of the Future 2014
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