7th EuroVariety 2017
SCIENCE PROBLEM SOLVING AND OTHER HIGHER-ORDER THINKING TASKS: THE ROLE OF SELECTIVE COGNITIVE VARIABLES
Department of Chemistry, University of Ioannina, Ioannina, Greece
A basic feature of problem solving in science is the difference between exercises and realproblems. In an exercise, a well-known and usually practiced procedureis followed(an algorithm). On the contrary, a real or complex problemis not algorithmicand requiresconceptual understanding and the contribution of high-order cognitive skills (HOCS) and a number of mental resources. A considerable difference in student performance on chemistry problems that require algorithmic or conceptual understanding has been demonstrated (Niaz, 1995).In addition, a number of cognitive/psychometric variables have been shown to be important contributors to student high ability and achievement in science problem solving and other higher-order science tasks. Variables such as working-memory capacity, mental-space capacity (M-capacity), disembedding ability (degree of field dependence-independence), developmental level, the mobility-fixity dimension, and convergent/divergent thinking are involved in learning and in the execution of cognitive tasks, and can be predictive of the student performance. Examination of the limitations of the Johnstone & El-Banna model led to the necessary conditions for it to be valid. A study of organic chemical synthesis problems, with a simple logical structure and varying M-demand,showed the pattern of the expected drop in performance, being more striking in the case of the students without previous training. Developmental level played often a role in conceptual understanding and applications, but less so incircumstances involving complex conceptual situations and/or chemical calculations, where disembedding ability appeared to be involved. Examination of the effect of the mobility-fixity dimension showed that in most cases the mobile subjects demonstrated higher mean achievement than the fixed subjects. Convergence-divergence has been studied in relation to student understanding of chemistry and of physical changes. Manipulation of the logical structure as well as of the M-demand of algorithmic chemical equilibrium problems led to the conclusion that all examined cognitive variables were correlated with achievement only when the logical structure was fairly complex and even when the M-demand was relatively low, with working memory maintaining some importanceand developmental level playing the dominant part. Finally, special interest present the non-algorithmic and open-ended problems. In the case of non-algorithmic problems, developmental level showed lack of correlation and working-memory capacity also showed weak correlation, while functional M-capacity and disembedding ability played a very important role. In the case of open-ended problems, a positive correlation between M-capacity and both algorithmic and open-ended problem solving was recorded, while a threshold effect was demonstrated.
Johnstone, A. H. (1984) New stars for the teacher to steer by? Journal of Chemical Education, 61, 847–849.
Johnstone, A.H., & El-Banna, H. (1986). Capacities, demands, and processes - a predictive model for science education. Education in Chemistry, 23, 80-84.
Niaz M. (1995). Progressive transitions from algorithmic to conceptual understanding in student ability to solve chemistry problems: A Lakatosian interpretation. Science Education,79,19-36.