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Asia-Pacific
Forum on Science Learning and Teaching, Volume 6, Issue 2, Foreword
(Dec.,
2005) Svein SJØBERG & Camilla SCHREINER How do learners in different cultures relate to science and technology?
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Asia-Pacific
Forum on Science Learning and Teaching, Volume 6, Issue 2, Foreword
(Dec.,
2005) Svein SJØBERG & Camilla SCHREINER How do learners in different cultures relate to science and technology?
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Similarities between countries
In question ACE we request the students to indicate how interested they are in learning about a variety of topics. One underlying hypothesis for this question is that in spite of few students choosing an S&T education and career, and in spite of research finding that many students do not like school science, many young people find aspects of S&T interesting. The ACE question provides empirical data on what topics various groups of students are interested in learning about. This insight can inform our discussions on how S&T curricula can be constructed in order to meet the interests of different groups of learners. Asking the students how interested they are in various topics is one approach for getting in touch with science lessons' potential for engagement.
We do of course not argue that science curricula should be determined from student opinion polls on what they find interesting. But, on the other hand, we believe that the teaching of school science has the potential to enliven, motivate, enrich, engage and inspire the students. To achieve this, we need to be aware of the interests, hopes and priorities of the learners.
For exploring similarities between countries in the ACE items, hierarchal cluster analysis is a useful explorative statistical tool. Results from the hierarchical cluster analysis can be presented in dendrograms. The dendrogram in Figure 1 below shows how similar or close the countries and country clusters are to each other: The branches illustrate how clusters are formed at different stages in the analysis and the distances between the clusters.
Figure 1. Hierarchical cluster analysis of residual ACE mean scores for all countries. Proximity measure: squared Euclidean distance. Clustering method: between-groups linkage. To the left, we have inserted a column showing the national HDI values (UNDP, 2003). (Source: Schreiner, 2006)
The distance along the horizontal axis from the point at which the clusters come into existence to the point at which they aggregate into a larger cluster represents the distinctness of the clusters. The distinctness tells us how different one cluster is from its closest neighbour. The more compact a cluster is, i.e. the further to the left the branches merge, the more similar to each other the countries are.
Annually, the United Nations Development Programme (UNDP) publishes a Human Development Report (HDR) (UNDP, 2003). In each HDR, the countries are ranked according to a Human Development Index (HDI). The index is monitoring average national achievement in three dimensions of human development: income, education and health6 . In this article, HDI will be used as an indicator for the level of development in a country. To the left in Figure 1, we have inserted a column showing the national HDI values.
By reading the dendrogram from the right towards the left, we see that the meta-cluster contains three main clusters: (A) High HDI countries including all the European countries plus Japan and Trinidad and Tobago, (B) Medium HDI Oriental countries and (C) Low HDI African countries. As the length of the branch for all these three clusters are relatively long, they can be perceived as three distinctive clusters of countries. Cluster B is more similar to cluster A than cluster C is.
One noticeable result from the analysis above is that similarities between countries in this part of the questionnaire seem to be determined by two properties: geographical closeness and level of development. The general pattern is that first, the countries merge with geographically neighbouring countries, and next, the group of neighbouring countries merge with groups of countries having a comparable level of development7 . But the unifying effect of geographical closeness only works within a certain limit of diversity in development. For example, Japan is geographically closer to the Philippines and Malaysia than to Europe, but the Japanese students seem to have more interests in common with European students. This may possibly be explained by the relatively high level of development and industrialisation in Japan. The response profiles of students in the Oriental countries (like Malaysia, Philippines, India and Bangladesh) appear as relatively similar to each other. We should note that the Russian students' orientation towards science and science education appear as comparable to the profiles of the students in the Baltic countries (Latvia and Estonia). Keep in mind that the Russian students in ROSE come from Karelia, a region quite close to the Baltic countries and Finland.
6Definition: HDI is a summary measure of human development based on the weighted average of three indices: (1) a long and healthy life, as measured by life expectancy at birth, (2) education, as measured by the adult literacy rate (two-thirds weight) and the combined primary, secondary and tertiary education gross enrolment ratio (one-third weight), and (3) a decent standard of living, as measured by GDP per capita (PPP US$). (For details on how the index is calculated, see e.g. Technical note 1 in UNDP, 2004)
7In spite of non-random sampling procedures, countries that are commonly considered as similar to each other (for example African, Baltic or Asian countries) do in most instances show similar or related response patterns. This can be seen as some validation of the data.
Copyright (C) 2005 HKIEd APFSLT. Volume 6, Issue 2, Foreword (Dec., 2005). All Rights Reserved.
