Modeling with Sketchpad to enrich students' concept image of the derivative in introductory calculus : developing domain specific understanding

Abstract

It was the purpose of this design study to explore the Geometer’s Sketchpad dynamic mathematics software as a tool to model the derivative in introductory calculus in a manner that would foster a deeper conceptual understanding of the concept – developing domain specific understanding. Sketchpad’s transformation capabilities have been proved useful in the exploration of mathematical concepts by younger learners, college students and professors. The prospect of an open-ended exploration of mathematical concepts motivated the author to pursue the possibility of representing the concept of derivative in dynamic forms. Contemporary CAS studies have predominantly dwelt on static algebraic, graphical and numeric representations and the connections that students are expected to make between them. The dynamic features of Sketchpad and such like software, have not been elaborately examined in so far as they have the potential to bridge the gap between actions, processes and concepts on the one hand and between representations on the other. In this study Sketchpad model-eliciting activities were designed, piloted and revised before a final implementation phase with undergraduate non-math major science students enrolled for an introductory calculus course. Although most of these students had some pre-calculus and calculus background, their performance in the introductory course remained dismal and their grasp of the derivative slippery. The dual meaning of the derivative as the instantaneous rate of change and as the rate of change function was modeled in Sketchpad’s multiple representational capabilities. Six forms of representation were identified: static symbolic, static graphic, static numeric, dynamic graphic, dynamic numeric and occasionally dynamic symbolic. The activities enabled students to establish conceptual links between these representations. Students were able to switch systematically from one form of (foreground or background) representation to another leading to a unique qualitative understanding of the derivative as the invariant concept across the representations. Experimental students scored significantly higher in the posttest than in the pretest. However, in comparison with control group students the experimental students performed significantly better than control students in non-routine problems. A cyclical model of developing a deeper concept image of the derivative is therefore proposed in this study.