Constructivism in Practice: an Exploratory Study of Teaching Patterns and Student Motivation in Physics Classrooms in Finland, Germany and Switzerland

Abstract

For the last three decades, moderate constructivism has become an increasingly prominent perspective in science education. Researchers have defined characteristics of constructivist-oriented science classrooms, but the implementation of such science teaching in daily classroom practice seems difficult. Against this background, we conducted a sub-study within the tri-national research project Quality of Instruction in Physics (QuIP) analysing 60 videotaped physics classes involving a large sample of students (N = 1192) from Finland, Germany and Switzerland in order to investigate the kinds of constructivist components and teaching patterns that can be found in regular classrooms without any intervention. We applied a newly developed coding scheme to capture constructivist facets of science teaching and conducted principal component and cluster analyses to explore which components and patterns were most prominent in the classes observed. Two underlying components were found, resulting in two scales—Structured Knowledge Acquisition and Fostering Autonomy—which describe key aspects of constructivist teaching. Only the first scale was rather well established in the lessons investigated. Classes were clustered based on these scales. The analysis of the different clusters suggested that teaching physics in a structured way combined with fostering students’ autonomy contributes to students’ motivation. However, our regression models indicated that content knowledge is a more important predictor for students’ motivation, and there was no homogeneous pattern for all gender- and country-specific subgroups investigated. The results are discussed in light of recent discussions on the feasibility of constructivism in practice.

Appendixes

Appendix 1

Table 4 Overview of all facets of the coding scheme, as given in von Arx (2014)

Appendix 2

R script for K-means algorithm (Beerenwinkel 2007, p. 78)

For the K-means clustering algorithm, the best solution was selected by maximising the quotient of between-cluster variation to within-cluster variation (Hand et al. 2001, p. 298):

$$ \mathrm{B}:\mathrm{set}\kern0.5em \mathrm{of}\kern0.5em \mathrm{computed}\kern0.5em \mathrm{cluster}\kern0.5em \mathrm{solutions}\to \mathrm{\mathbb{R}},C\to \frac{\mathrm{bc}(C)}{\mathrm{wc}(C)} $$

with K the number of clusters, r _k(j) the cluster centre of the k(j)th cluster and C _k the kth cluster and where the within-cluster variation, wc, and the between-cluster variation, bc, are defined as follows:

$$ \mathrm{w}\mathrm{c}(C)={\displaystyle {\sum}_{k=1}^K{\displaystyle {\sum}_{x\in {C}_k}{\left\Vert x-{r}_k\right\Vert}^2}} $$

$$ \mathrm{b}\mathrm{c}(C)={\displaystyle {\sum}_{1\le j<k\le K}^K{\left\Vert {r}_j-{r}_k\right\Vert}^2,j\ne k} $$

Homogeneity was tested by calculating the ratio of the variance of each variable i within a particular cluster C _k to the variance of the entire sample $\left(\frac{\mathrm{Var}\left(i,{C}_k\right)}{\mathrm{Var}(i)}\right)$.