Parsimony Analysis – a Novel Method of Structured Knowledge Representation for Teaching Computer Science

Abstract

Knowledge may be structured in a manner that may either handicap or improve learning outcomes. Knowledge construction is therefore an important aspect of the learning and assessment process. This paper is primarily concerned with structural knowledge (knowing how) of which there are numerous representations and mapping methods each with their associated strengths and weaknesses. This paper presents the novel application of parsimony analysis for organizing and defining structural knowledge within the field of computer science. Work to date has clearly demonstrated there are considerable potential pedagogical advantages to this approach.

Improving Teaching and Learning Outcomes – A Novel Cognitive Science Approach

“…However none of the methods provided rigorous methods for defining concepts and characters. Furthermore none of the methods provided a rigorous, quantifiable mechanism for determining and calculating structured relationships' (S. P. Maj, Veal, D., 2010 ). Maj proposed a new quantitative method, based on a parsimony algorithm that could define a logical learning sequence with the minimum cognitive load.…”

“…The cognitive science approach is based on how people process information based on the interaction between short and long term memory and the development of mental 'maps' called schemata (Rumelhard, 1980). Each theory has advantages and disadvantages but the strength of the cognitive science approach is that it is possible to employ a quantitative approach (S. P. Maj, Veal, D., 2010 ). Maj demonstrated that it was possible to quantitatively evaluate course materials and also optimize the learning sequence.…”