A Cultural Perspective on the Structure of Student Interest in Science

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A Cultural Perspective on the Structure of Student Interest in ScienceMary Ainley a; John Ainley b

a Psychological Sciences, The University of Melbourne, Melbourne, Australia b Australian Council forEducational Research, Melbourne, Australia

Online publication date: 11 January 2011

To cite this Article Ainley, Mary and Ainley, John(2011) 'A Cultural Perspective on the Structure of Student Interest inScience', International Journal of Science Education, 33: 1, 51 — 71To link to this Article: DOI: 10.1080/09500693.2010.518640URL:http://dx.doi.org/10.1080/09500693.2010.518640

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International Journal of Science EducationVol. 33, No. 1, 1 January 2011, pp. 51–71

ISSN 0950-0693 (print)/ISSN 1464-5289 (online)/11/010051–21© 2011 Taylor & FrancisDOI: 10.1080/09500693.2011.518640

RESEARCH REPORT

A Cultural Perspective on the Structureof Student Interest in Science

Mary Ainley a∗ and John Ainley baPsychological Sciences, The University of Melbourne, Melbourne, Australia; b AustralianCouncil for Educational Research, Melbourne, AustraliaTaylor and FrancisTSED_A_518640.sgm10.1080/09500693.2010.518640International Journalof ScienceE ducation0950-0693 (print)/1464-5289 (online)OriginalArticle2010Taylor &[email protected]

In this paper, we examine the nature of interest in science as represented in the Programme forInternational Student Assessment (PISA) 2006 data. We discuss the interconnections betweenmeasures of knowledge, affect, and value as components of interest in science. Working from aperspective acknowledging that many of the models of motivation represented in the literaturehave been developed in Western countries, we investigated whether the ways that knowledge,affect, and value combine in the structure of students’ interest in science might vary in linewith historical and cultural traditions. Four countries were chosen to represent contrastingcultural values as defined in analyses of the World Values Surveys and the European ValuesSurveys—Colombia, Estonia, USA, and Sweden. Models are described showing variations in fitacross the four countries. Efforts to increase the attractiveness of science to students shouldtake heed of the fact that all models indicated a central role for enjoyment of science in thepaths linking personal value, interest, and current science activities with intentions for futureparticipation in science. Differences in the strength of the associations between science knowl-edge and interest in science support the proposition that the interconnections between knowl-edge, affect, and value need to be understood in relation to students’ broader historical andcultural context.

Keywords: Large-scale studies; Scientific literacy; Structural equation modelling; Interest; Motivation; Cultural context

Introduction

Most aspects of life in developed countries in the twenty-first century have in someway been shaped by scientific knowledge. Career opportunities are significantlyincreased when students graduate from high school with sound science literacy

*Corresponding author. Psychological Sciences, University of Melbourne, Melbourne, Victoria

3010, Australia. Email: [email protected]



52 M. Ainley and J. Ainley

skills. Underpinning the support for international programmes monitoring studentachievement in science (e.g. Programme for International Student Assessment[PISA], 2006) are the concerns of scholars and education researchers in industria-lised, Western societies that a decline in students’ academic motivation for science

studies may be associated with lower levels of science achievement (Eccles, Wigfield,& Schiefelé, 1998; Hidi & Harackiewicz, 2000).

Simultaneously, the Organisation for Economic Cooperation and Develop-ment’s (OECD) educational policy recognises that in addition to achievement, animportant educational outcome for today’s young people is an attitude that givesparticipation in science an important place both in their current life and theirfuture (OECD, 2006). This educational goal is often expressed in terms of commitment to lifelong learning, with the implication that it involves more thanjust the choice of scientific careers or further education, but may also be expressedthrough participation in science activities and projects within students’ commu-nity. Hence, interest in science involves how students view scientific informationand activities, and it is assumed that these reactions underpin students’ choices toparticipate in science activities in both their present and future lives. In this paper,we use measures from the set of interest in science concepts from PISA (2006) toinvestigate how the knowledge, affect, and value components of students’ interestin science are predictive of their current participation in science activities and theirintentions for future participation. Understanding how interest in science is relatedto participation in science, both at the level of current science activities and inten-tions for future participation in science whether through work, study or participa-

tion in science projects, has practical implications for science educators concernedwith students’ participation in both curricular and extracurricular science activi-ties.

However, across OECD and partner countries participating in PISA (2006) thereis wide cultural variation and the implications of findings on students’ interest,achievements and participation in science necessarily depend on understanding howstudents’ responses may be influenced by their culture. Within participating coun-tries there is ample evidence of the influence of socioeconomic factors on students’achievement (OECD, 2007), but this is only one of many perspectives on how

culture might be associated with students’ interest in science. It is also conceivablethat broader cultural factors such as those embodied in historical and traditionalvalues might be influential. Using data from the World Values Surveys and theEuropean Values Surveys, Inglehart and colleagues (Inglehart & Baker, 2000; Ingle-hart & Welzel, 2005) identified two underlying bipolar cultural dimensions consist-ing of traditional versus secular-rational orientations towards authority and survivalversus self-expression values. For this investigation, four countries—Colombia,Estonia, USA, and Sweden—were chosen on the basis that they represented majorcontrasts in the global cultural space defined by Inglehart’s two dimensions; weexamined how affect, knowledge, and value components of students’ interest inscience are predictive of their current participation in science activities and theirintentions for future participation.




propose a distinction between an ‘emerging’ individual interest and a ‘well-developed’ individual interest based on the level of personal commitment andautonomy expressed in the level of engagement and reengagement with the interestdomain. Hidi and Renninger define prototypes of these two phases but as examina-

tion of the defining characteristics of these phases indicates, they represent early(emerging) and later (well-developed) points in a developmental dimension. Notall emerging individual interests become elaborated into well-developed individualinterests, and in some recent writings, the reference to well-developed individualinterest rather than simply individual interest has been used to denote the extremeof the dimension with the caveat that this may only occur rarely (Renninger,2009).

In the current investigation, we are concerned with individual interest as a dimen-sion expressing a general relation with learning science. The PISA (2006) attitudinalmeasures targeted students’ general responsiveness to science. 1 The general interestin learning science measure in PISA (2006) required students to rate the intensity of their interest in learning about a range of topics areas, for example, physics, astron-omy and the biology of plants. The question was expressed as ‘How much interestdo you have in learning about …’ and the four response options ranged from ‘highinterest’ to ‘no interest’. Hence, 15-year-olds with very high scores on the generalinterest in learning science scale indicated a high intensity of interest across a rangeof topics in the domain of science. That is, they showed strong individual interest inscience rather than an interest in any particular branch of science. Equally, studentswith very low scores on this scale were indicating little interest in learning science,

which at the low extreme of the scale was indicative of no individual interest inscience.

Two main lines of research inform our model testing concerning the relationbetween the interest in learning science and the participation in science measures.The first involves propositions concerning the relation between knowledge, affect,and value components of students’ interest in learning science and their participa-tion in science-related activities as expressed in the four-phase model of interestdevelopment (Hidi & Renninger, 2006). The second draws on some longitudinalfindings of the predictive power of students’ intentions (Khoo & Ainley, 2005).

According to the four-phase model of interest development (Hidi & Renninger,2006), what distinguishes situational interest from individual interest is theaccrued knowledge and value components that combine with positive affect tomake up the psychological unit that represents an individual interest. Hence, indi-vidual interest in science has three essential components: stored knowledge orcognitive schema relevant to the domain, positive feelings towards the domain andvalue schema that represent the personal significance of the domain. On this basis,students with individual interest in science will have acquired a reasonable body of scientific knowledge and understanding, enjoy participating in science activitiesand view science and science activities as personally important. By virtue of havingthese characteristics, students will seek to reengage with the interest domainwhenever they have an opportunity. They will generate what Renninger has called



Structure of Interest in Science 55

‘curiosity questions’ and through the process of seeking answers to those ques-tions, expand their involvement with science-related activities and extend theirknowledge. Despite differences in emphasis (see e.g. Krapp, 2000; Schiefelé,1996), the same components are essential characteristics in the structure of the

‘person–object theory of interest’ (POI; Krapp, 2005), where having an establishedindividual interest involves highly differentiated knowledge as well as the centralcriterion of closely related ‘value-oriented and emotional components’ (Krapp,2003, p. 63). Equally critical in these conceptualisations of individual interest isthe expression of individual interest in actions which seek interaction and reen-gagement with the interest domain.

Hence, we can predict that having a coherent body of science knowledge andunderstanding (knowledge), enjoying science (affect), and valuing science (value)will be predictive of the level of general interest in learning science (individual inter-est), which in turn will predict being currently engaged in science activities andhaving the intention to engage with science activities in the future (current andfuture engagements). The PISA (2006) dataset provides an opportunity to test howwell this network of relations holds for 15-year-old students from different culturalcontexts.

Because PISA is essentially a cross-sectional study, the predictive effects of interest in science on actual future participation in science-related activitiescannot be determined. However, recent investigations using longitudinal designshave demonstrated significant influences of current attitudes on future behav-iour. Khoo and Ainley (2005) tested the predictive relation between attitudes to

schooling and intentions to continue beyond the end of compulsory schoolingusing data from Grade 9 students participating in LSAY. Khoo and Ainley’sfindings have been introduced because the PISA (2006) measures of interest inscience are analogous to the attitude to schooling measures in LSAY. For exam-ple, in the structure of the 30-item LSAY attitude to schooling scale, studentsexpressing the strongest agreement with statements such as ‘the work we do isinteresting’, ‘I get excited about the work we do’, ‘I enjoy what I do in class’and ‘I find that learning is a lot of fun’ had the highest overall attitude scores.PISA (2006) measures of interest in learning science and enjoyment of science

have clear similarities with the substance of Khoo and Ainley’s attitude to schoolscale.The LSAY results for the predictive relation between attitudes and intentions at

Grade 9, and participation in further schooling as measured by enrolment in Grade12 and again by enrolment in tertiary education, indicated that attitudes to schoolinginfluence future participation in schooling and tertiary study through their effects onstudents’ intentions. The effect of attitudes was mediated through intentions forfuture participation. Hence, attitudes to schooling affect intentions, and intentionshave an important influence on later behaviour; although cross-sectional in nature,the quality of the PISA (2006) database provides an opportunity to explore relationsbetween interest in science variables and students’ expressions of future involvementin science-related activities.




Cultural Context and Interest in Science

While the research that underpins most models of student motivation and achieve-ment depends heavily on findings from north America and western Europe, the widerange of OECD and partner countries participating in PISA (2006) affords an oppor-tunity to test the generality of these models in a range of countries with different histor-ical, political, and cultural traditions. PISA (2006) measures have been constructedto be accessible to students from all participating countries. Thus, they provide oppor-tunities to test how well models positing a combination of knowledge, affect, and valuecomponents of individual interest reflect the interdependencies between the PISA(2006) interest in science variables and their relations with measures of current andfuture engagement for students from different cultural contexts.

Contemporary perspectives from developmental science provide a useful frame-work for understanding some of the cultural influences on students’ interest and

achievement in science. For example, Bronfenbrenner’s model of ecological devel-opment (Bronfenbrenner, 2004; Bronfenbrenner & Morris, 1998) draws attentionto nested layers of influence on wide-ranging aspects of children’s development. Themicrosystem of children’s immediate environment, including institutions andsystems such as family, school, and neighbourhood, is embedded in the broadermacrosystem of the historical traditions, legal systems, cultural values, and practicesof the community and the nation. At a level equivalent to Bronfenbrenner’s micro-system, detailed analyses of science achievement in relation to students’ socioeco-nomic status have been reported (OECD, 2007, p. 333). As expected, students frommore advantaged socioeconomic backgrounds achieved significantly higher scoreson measures of science literacy than students from less advantaged backgrounds.The strength of this association varied considerably across countries as did the levelof variability within countries (see OECD, 2007, p. 183ff.).

To identify macrosystem influences, it is necessary to examine relations amongthe interests in science variables at the level of individual countries with contrastinghistorical and cultural traditions and values. A number of research programmespoint to the place of macrosystem influences on children’s academic attitudes, inter-ests, and achievements. Larson and Verma (1999) used the findings from an exten-sive review of research on patterns of work and leisure as represented in the time use

of children around the world, to point out that while economic development isassociated with people in communities placing greater value on economic self-sufficiency, there were also important differences in the ways these economic valuesare integrated into social and cultural values. For example, they contrasted the pair-ing of economic self-sufficiency and individualism in the family as regularly observedin Western societies with observations of children and families in Japan, wherepeople ‘have come to value economic self-sufficiency without altering collectivistvalues on emotional interdependency and close-knit family relationships’ (p. 704).

Macrocultural values have been used to explain the well-documented differences

between Asian-American and Caucasian-American adolescents within localcommunities in the USA, highlighting the potential for macrocultural systems to




affect development within multicultural communities within nations. Sometimesreferred to as the ‘Asian-American paradox’, researchers reported that the usualpattern of predictive relationships between authoritarian parenting styles and lowerlevels of school achievement were not observed in research in the USA when the

student samples consisted of Asian-American students (see Steinberg, Lamborn,Dornbusch, & Darling, 1992). More detailed analysis of parenting and schoolingfactors (e.g. Asakawa & Csikszentimihalyi, 1998; Chao, 2001) demonstrated thatthe strong family traditions and cultural values experienced by Asian-Americanstudents were often associated with high achievement. Western interpretations of Asian parenting practices as authoritarian parenting have been shown to misinter-pret the meaning of cultural and family practices. Further complexities have beenintroduced into these interpretations with research findings demonstrating differ-ences in parenting and achievement relations for students from different Asiannations (Chao, 2001).

Macrocultural values have also been linked to the differential effectiveness of choice for enhancing student motivation. As reported by Iyengar and Lepper(1999), intrinsic motivation was lower for Anglo-American children when choiceswere made for them and higher when they were able to choose for themselves. Onthe other hand, intrinsic motivation was highest in Asian-American children ‘whenchoices were made for them by trusted authority figures or peers’ (Iyengar & Lepper,1999, p. 349). Dekker and Fischer (2008) used Schwartz’s (see Smith, Bond, &Kagitcibasi, 2006) dimensions of embeddedness versus autonomy, harmony withthe world versus mastery of the world and hierarchy versus egalitarianism to demon-

strate consistent positive relations between mastery achievement goals and egalitari-anism. Performance-approach achievement goals were likely to be more prominentin societies with stronger embeddedness and in less well-developed societies. Incontrast, performance-avoidance achievement goals did not relate significantly toany of the value dimensions, and were argued to be more likely to be related to indi-vidual difference factors than to cultural values.

Over time, cultural traditions, values, and practices are subject to a range of forcesthat can lead to important changes in patterns of social interaction. Across the twen-tieth century, significant industrial, technological, and economic developments have

increased the global significance of modern science. However, like the disparities insocioeconomic status within communities and nations, there are differences betweennations in their access to and adoption of these developments. Inglehart andcolleagues (Inglehart & Baker, 2000; Inglehart & Welzel, 2005) argue that economicdevelopment over the last century, rather than producing ‘homogenisation’ of cultural values and practices, has been absorbed into patterns of development thatbear the unmistakable imprint of each country’s history and traditions. Theypropose that, within the diversity of cultural patterns, there are two major valuedimensions underpinning the developmental trajectories of modern nations. Usingdata from representative national samples collected in the World Values Surveys andthe European Values Surveys (1981–1982; 1990–1991; 1995–1998), Inglehart andcolleagues have found evidence of coherent value differences between participating




nations. The 65 countries contributing to these databases are claimed to representapproximately 75% of the world’s population (Inglehart & Baker, 2000), and twobroad bipolar value dimensions emerged consistently across these waves of data.

The first dimension is orientation to authority and contrasts traditional values

which emphasise religion and obedience to traditional authorities, with secular-rational values where deference to religious authority is not given a high priority, andwhere family and social values are seen as relative rather than absolute (Inglehart &Baker, 2000; Inglehart & Welzel, 2005). Inglehart and colleagues’ findings link thisdimension to the shift from agrarian to industrial social organisation, which has beenaccompanied by ‘rationalisation of authority, reflected in rising secular-rationalvalues’ (2005, p. 58). It is suggested that the second major dimension emerged withthe rise of ‘service and knowledge sectors’ as industrialised societies became moreaffluent. On one side of this second dimension are survival values, where theeconomic and physical security of the community underpins values and action. Onthe other side are self-expression values where individual autonomy, subjective well-being and personal quality of life concerns are prominent. Findings from the valuessurveys suggest that associated with high self-expression values is a tendency forpeople to express ‘relatively low levels of confidence in technology and scientificdiscoveries as the solution to human problems’ (2005, p. 137). In contrast, it issuggested that members of societies with high survival values express relatively ‘highlevels of faith in science and technology’ (2000, p. 28). Participation in highereducation and the spread of scientific knowledge are associated with both secular-rational and self-expression values. Inglehart and Welzel (2005) also point out that

these relations are connected with economic conditions such that when circum-stances challenge economic security, survival values become more prominent andthere is less emphasis on autonomy and self-expression.

Hence, at the end of the twentieth and the beginning of the twenty-first century,there is ample evidence of important differences between countries in terms of basiccultural values and orientations. It is also clear that these are not absolute differ-ences. Within countries there are often subgroups whose values reflect the cultureand historical traditions of their family origins rather than those of the dominantgroup. While it is not the only system represented in the literature, Inglehart’s

‘global map of cross-cultural variation’ (see Inglehart & Baker, 2000, p. 29) providesa framework to test whether models of interest in science that have been developedin Western societies apply across a range of cultures.

We expected that students’ interest in science will reflect something of the place of science within the contemporary landscape of each country’s cultural traditions andvalues. It is likely that students with different cultural experiences of science mayevidence differences in the way underlying processes combine in the structure of their interest in science. Following Inglehart and colleagues’ (2000, 2005) analysis,different levels of faith and confidence in science and technology as identified inrelation to the survival/self-expression dimension would be expected. Simulta-neously, the changing social and cultural patterns associated with moves fromtraditional to secular-rational social patterns and expanding educational experiences




are likely to bring science and opportunities for participation in science activitiescloser to the typical experience of young adolescents.

Examining our predictions concerning the relations between the components of interest in science and the participation variables across a number of countries with

contrasting profiles according to the Inglehart cultural map will provide furtherinsight into the generality of the relations between the interest variables as presentedin general models of interest processes such as that in Hidi and Renninger’s (2006)four-phase model of interest development.

Results

For this investigation, we chose the countries participating in PISA (2006; eitherOECD or partner country) with the most extreme values from each of the fourquadrants defined by the intersection of Ingleharts’ two macrocultural values dimen-sions (Inglehart & Baker, 2000, p. 29): traditional (T) versus secular-rational (SR),and survival (S) versus self-expression (SE). If macrocultural values are related tointerest in science we would expect to find differences in the levels across the interestin science variables for this set of four countries.

Most of the countries with more extreme scores from the quadrant representing thecombination of traditional and survival values (T_S) were not participants in PISA(2006). However, there were a number of countries more than one standard deviationfrom the mean on the side of traditional values located close to the mean on the survival/self-expression axis (T/S). These included Chile, Mexico, Turkey, Brazil, and Colom-

bia. From these, we selected Colombia. Five Eastern European countries—Estonia,Latvia, Russia, Lithuania, and Bulgaria—were located towards the extreme of thequadrant that combined strong secular-rational values and survival values (SR_S).Estonia was selected as the most extreme within this set. Only three countries, theUSA, Ireland, and Argentina, in PISA (2006) represented a pattern of strong tradi-tional values coupled with high self-expression values (T_SE). The USA was approx-imately 1 standard deviation from the mean on the traditional values axis and morethan 1.5 standard deviations away from the mean on the self-expression axis, and thuswas chosen from this quadrant. Sweden was chosen as the country with the most

extreme position in the quadrant combining secular-rational and self-expression values(SR_SE). Hence, the countries to be used in further analyses are Colombia (T_S),Estonia (S_SR), the USA (T_SE), and Sweden (SR_SE). Recent indicators of educa-tional participation from the United Nations Educational, Scientific and CulturalOrganisation (UNESCO) Institute for Statistics database 2 show that three of thesecountries (Estonia, USA, and Sweden) have very similar and high participation ratesconsistent with their secular-rational and self-expression value orientations. Data from2006 for the upper secondary graduation rate show the rate for Estonia as 78%, theUSA as 77%, and Sweden as 76% (OECD, 2009). Reference to the gross enrolmentratio (GER) at international standard classification of education levels 5 and 6, whichrepresent tertiary enrolments and an index of participation in higher education, showsa ratio for Estonia of 65%, for the USA 81%, and for Sweden 78%. The fourth country,




Colombia, had a slightly lower level of upper secondary graduation (64%) and asubstantially lower level of participation in higher education (GER of 32%).

Interest in Science Measures

When an existing database is used to test propositions derived from theories thatwere not specifically used to guide scale construction, the measures will not neces-sarily be an exact match for the theoretical constructs. However, they do provideproxies for model testing.

As measured in PISA (2006), interest in science records how students respond toscientific information and activities, and it is assumed that this pattern of reactionsunderpins students’ choices to participate in science activities in both their presentand future lives. Hence, in this investigation we focused on the set of measures inPISA (2006) designed to assess interest in science: general interest in learning science ,enjoyment of science , general value of science, personal value of science, participation inscience-related activities , and future-oriented motivation to learn science . Instrumental motivation to learn science was not included, as these items focus on learning sciencein school because it ‘will help me in the work I want to do later’, ‘will help me get ajob’ or ‘what I learn will help my job prospects’. As conveyed by the variable label,this is a pattern of response where learning science is instrumental for achieving aparticular goal that is not necessarily connected with learning science. In contrast,the variable labelled future-oriented motivation to learn science was designed to measurehow many students actually intended to continue their interest in science and items

referred to future science careers, studies in science and participation in scienceprojects. For this variable, the emphasis is more directly on the science itself and anintention to make science part of students’ future. Expectations for a career at 30 wasnot included in our analyses because it was a single item reflecting participation inscience careers that provided a narrower measure of future participation in sciencethan future-oriented motivation to learn science .

In keeping with the objectives of PISA (2006) as an international assessment of science achievement, there is a focus on a strong reliable measure of science knowl-edge and understanding. The database includes five plausible values for this index

generated from Rasch scaling of the science achievement items (OECD, 2007, pp.40–52). For our analyses, we used the first plausible value as our index of scienceknowledge. All interest in science variables are represented as weighted likelihoodestimates (WLEs) of the scale scores (OECD, 2009, pp. 314–327). Table 1 presentsa summary description of the scales.

Following the model of interest processes that posits knowledge, affect, and valueas components of individual interest, and the related proposition that individualinterest influences students’ engagement and reengagement with the interest domain,the following propositions were investigated. We expected that enjoyment of science,science knowledge , and importance of learning science ( general value and personal value )would predict general interest in learning science , which would in turn predict participa-tion in science-related activities and future-oriented motivation to learn science .




The mean WLEs of scale scores indexed as standard scores are presented for eachof the four countries in Figure 1. Colombia, from the traditional/survival quadrant,had the highest mean ratings on all of the interest in science variables while Sweden,from the secular-rational/self-expression quadrant showed exactly the opposite patternwith the lowest mean scores on all the interest variables. The contrasting patterns of mean ratings for these countries confirm our general prediction that responses to the

interest in science variables may reflect the degree to which science and technologyare embedded within the cultural fabric. Here, the comparison is between a country(Sweden) where science and technology are an assumed part of everyday life and acountry (Colombia) where only more recently have science and technology becomepossible career and lifestyle opportunities for large numbers of young people.However, the high ratings for the Colombian students do not simply representresponse bias, as there is clear discrimination between ratings on some of the variables.For example, general and personal values of science scales were subsets of items withinthe same question and the mean rating for general value of science was substantially

lower than the mean rating for personal value of science for Colombian students. Thecontent of the general value of science items refers to outcomes such as improvedliving conditions, improved economic conditions, and general social benefits, that is,what has been achieved for them and their community through science. Personal valueof science items refer to current circumstances comprising both people and things intheir environment as well as personal opportunities for the future through science.Figure1.

Profiles for the USA, from the traditional/self-expression quadrant, and Estonia,from the survival/secular-rational quadrant, ranked between the extremes on all of the variables. The major divergence between these groups was on the participationin current science activities variables, where the Estonian students had higher ratingsthan the USA students. Future participation ratings for these two countries showedthe reverse results.

Table 1. Summary description of PISA scales

Index aNo. of items Example item

Cronbach’s alphaOECD/Partner

General interest in learningscience

8 How much interest do you have inlearning about …The biology of plants

0.85/0.82

Enjoyment of science 5 I am happy doing science problems 0.88/0.91General value of science 5 Advances in science and technology

usually bring social benefits0.75/0.72

Personal value of science 5 I find that science helps me tounderstand things around me

0.75/0.72

Science-related activities 6 Watch TV programmes about science 0.80/0.79Future-oriented motivationto learn science

4 I would like to work on scienceprojects as an adult

0.92/0.90

aNames of scales as reported in PISA (2006, vol. 1, p. 373).




As knowledge is also an important component in the model we are testing, wecalculated each country’s mean science knowledge score using the first plausiblevalue as the index of science knowledge. The order of science knowledge scores isalmost the reverse of levels on the interest profiles. Estonia had the highest scienceknowledge mean (532.0) followed by Sweden (503.41). Colombia had the lowest

science knowledge score (386.68), with the USA (488.89) scoring just below theoverall scale mean of 500. Correlations between the interest in science variables foreach of the four countries are shown in Table 2. Of particular significance for themodel being tested is the variable relation between knowledge and general interest inlearning science across the four countries. The correlation was close to zero forColombia and around 0.3 for the other countries.

Using AMOS 7.0 (see Arbuckle, 2005) for each of the four countries, we tested amodel (Model A) that positioned knowledge, affect, and value (science knowledge,enjoyment of science, personal value of science, general value of science) as predic-tors of general interest in science, which in turn predicted current and future engage-ments with science (participation in science-related activities and future-orientedmotivation to learn science). Because we were dealing with very large samples, we

Figure 1. Profiles of mean weighted likelihood estimates (WLE) of the scale scores on interest inscience variables

Key: Scales: INS = General interest in science; ENJ = Enjoyment of science; GVS = Generalvalue of science; PVS = Personal value of science; ACT = Science-related activities; FUT =

Future-oriented motivation to learn science




focused our test of fit on the Root Mean Square Error of Approximation (RMSEA).A value of 0.05 or less indicates a close fit, values up to 0.08 represent acceptable fitand values greater than 0.10 indicate poor model fit (Byrne, 2001). Model fit canalso be assessed with other indexes such as the Comparative Fit Index (CFI) and theTucker-Lewis Index (TLI), which are relatively independent of sample size andcorrect for model complexity (Bollen & Long, 1993). High values for CFI and TLI(greater than 0.9) indicate satisfactory model fit. In our result tables, we report thesethree fit indexes as well as several others. For our analyses, chi-square statistics forthe null hypotheses are poor fit measures because our sample sizes are so large thateven small differences will appear to be statistically significant. The results for Model

Table 2. Correlations between scales used to test models of the structure of individual interest inscience for Colombia, USA, Estonia, and Sweden

ENJ PVS GVS INS ACT FUT

Colombia:Knowledge (SCK) − 0.05 − 0.07 0.12 − 0.08 − 0.09 − 0.09Enjoyment (ENJ) — 0.54 0.34 0.43 0.47 0.50Personal value (PVS) — 0.52 0.41 0.41 0.45General value (GVS) — 0.28 0.27 0.21Interest (INS) — 0.35 0.37Activities (ACT) — 0.41

USAKnowledge (SCK) 0.28 0.24 0.31 0.14 0.13 0.16Enjoyment (ENJ) — 0.60 0.44 0.63 0.58 0.61Personal value (PVS) — 0.70 0.54 0.47 0.55General value (GVS) — 0.39 0.33 0.31Interest (INS) — 0.50 0.52Activities (ACT) — 0.41

EstoniaKnowledge (SCK) 0.22 0.20 0.33 0.16 0.07 0.06Enjoyment (ENJ) — 0.56 0.33 0.54 0.57 0.53Personal value (PVS) — 0.54 0.48 0.47 0.46General value (GVS) — 0.30 0.23 0.18Interest (INS) — 0.50 0.44Activities (ACT) — 0.50

SwedenKnowledge (SCK) 0.37 0.29 0.34 0.32 0.26 0.27Enjoyment (ENJ) — 0.69 0.53 0.69 0.58 0.65Personal value (PVS) — 0.71 0.59 0.29 0.63General value (GVS) — 0.47 0.40 0.41Interest (INS) — 0.53 0.55Activities (ACT) — 0.49

Note . Knowledge (SCK) = Science knowledge; Enjoyment (ENJ) = Enjoyment of science;Personal (PVS) = Personal value of science; General (GVS) = General value of science; Interest(INS) = General interest in science; Activities (ACT) = Science-related activities; Future Science

(FUT) = Future-oriented motivation to learn science.




A are presented in Table 3 and Figure 2. Only one of the value variables (personalvalue) is shown in the model. There was a very high correlation between thepersonal value of science and general value of science across the four countries, andinclusion of both variables resulted in poorer fit parameters.Figure2. As can be seen from Table 3 and Figure 2, the model was a poor fit for all fourcountries. While there were strong positive correlations among enjoyment of science,personal value of science and general interest in learning science for all four coun-

tries, interest in learning science does not appear to mediate their predictive relationswith current activities and future engagement. As pointed out previously, there wasno consistent pattern of relations between knowledge and the rest of the variables.For Colombia, all of the correlations with knowledge were close to zero, while forEstonia and the USA, knowledge had moderate, positive correlations with enjoy-ment of science and personal value of science. The strongest correlations were forSweden, where knowledge had moderate, positive correlations with all of the vari-ables. Further inspection of the zero-order correlations shown in Table 2 indicatedthat enjoyment of science generally had strong associations with most of the vari-ables for all four countries; an adjusted model (Model B) was tested locating enjoy-ment of science as the mediator rather than interest in learning science. The resultsare shown in Table 4 and Figure 3.

Table 3. Individual interest in science model: Model A and t indices for selected countries

Colombia(T/S)

USA(T/SE)

Estonia(SR/S)

Sweden(SR/SE)

Interest (INS) ← Knowledge (SCK) − 0.05 − 0.07 0.02 0.07Interest (INS) ← Personal value (PVS) 0.26 0.28 0.26 0.23Interest (INS) ← Enjoyment (ENJ) 0.31 0.53 0.43 0.58

Activities (ACT) ← Knowledge (SCK) − 0.06 − 0.04 − 0.09 0.04Activities (ACT) ← Personal value (PVS) 0.22 0.16 0.18 0.21Activities (ACT) ← Enjoyment (ENJ) 0.33 0.40 0.38 0.32Activities (ACT) ← Interest (INS) 0.14 0.17 0.24 0.20Future science (FUT) ← Knowledge (SCK) − 0.06 − 0.03 − 0.09 0.02Future science (FUT) ← Personal value (PVS) 0.19 0.27 0.21 0.35Future science (FUT) ← Enjoyment (ENJ) 0.34 0.40 0.36 0.38

Future science (FUT) ← Interest (INS) 0.14 0.16 0.17 0.13NFI 0.720 0.747 0.733 0.698RFI − 0.472 − 0.329 − 0.404 − 0.583IFI 0.720 0.747 0.733 0.699

TLI − 0.474 − 0.330 − 0.405 − 0.584CFI 0.719 0.747 0.732 0.698

RMSEA 0.303 0.370 0.349 0.445

R2 Interest (INT) 0.16 0.36 0.25 0.40Future science (FUT) 0.24 0.35 0.28 0.37

Activities (ACT) 0.21 0.31 0.34 0.28




Figure3. Inspection of the RMSEA values in Table 4 indicates that of the four countries,only Columbia’s dataset had an acceptable fit for Model B. The other three coun-

tries indicated poor fit for Model B. The strongest paths in Model B for the studentsfrom Colombia link interest in science and personal value of science with currentand future science activities through the mediator of enjoyment of science. Scienceknowledge did not make any substantial contribution to the pattern of relationswithin the network of interest in science variables.

Data from Sweden showed the poorest fit for Model B (Figure 3). In contrast tothe Colombia pattern of correlations, for Sweden there were significant correlationsbetween science knowledge and all of the interest variables, with the highest coefficientlinking science knowledge and enjoyment of science (0.37). We then determined that

the best-fitting model for the Swedish dataset was a model with enjoyment of scienceas a mediator of science knowledge and personal value on general interest in science.This model (Model C) is shown in Table 5 and Figure 4. While enjoyment of sciencemediates the relation of science knowledge and personal value of science for currentand future activities, there are additional significant relations between personal valueand enjoyment of science predictive of current and future activities mediated by inter-est in learning science. However, these paths are not as strong as the paths from enjoy-ment of science to current and future participation. When Model C was tested for theother countries, each produced an acceptable fit (see Table 5).Figure4.

The strongest paths in Model C connect personal value through enjoyment of

science to general interest in science and to current and future participation variables.General interest in science then adds a smaller but still significant contribution to the

Figure 2. Model of individual interest in science: Model A




Table 4. Individual interest in science model: Model B and t indices for selected countries

Colombia(T/S)

USA(T/SE)

Estonia(SR/S)

Sweden(SR/SE)

Enjoyment (ENJ) ← Knowledge (SCK) 0.01 0.15 0.09 0.12Enjoyment (ENJ) ← Personal Value (PVS) 0.44 0.34 0.39 0.43Enjoyment (ENJ) ← Interest (INS) 0.25 0.44 0.35 0.42

Activities (ACT) ← Knowledge (SCK) − 0.05 − 0.04 − 0.09 0.04Activities (ACT) ← Enjoyment (ENJ) 0.31 0.38 0.35 0.29Activities (ACT) ← Personal value (PVS) 0.19 0.15 0.17 0.19Activities (ACT) ← Interest (INS) 0.14 0.18 0.24 0.20Future science (FUT) ← Personal value (PVS) 0.22 0.26 0.20 0.32Future science (FUT) ← Enjoyment (ENJ) 0.32 0.36 0.34 0.34Future science (FUT) ← Interest (INS) 0.14 0.16 0.17 0.13

Future science (FUT) ← Knowledge (SCK) − 0.06 − 0.03 − 0.08 0.01NFI 0.994 0.974 0.975 0.950RFI 0.932 0.725 0.733 0.506IFI 0.994 0.974 0.975 0.953

TLI 0.936 0.726 0.735 0.507CFI 0.994 0.974 0.975 0.953

RMSEA 0.060 0.168 0.151 0.249

R 2 Enjoyment (ENJ) 0.34 0.49 0.41 0.59Future science (FUT) 0.31 0.44 0.35 0.48

Activities (ACT) 0.27 0.37 0.41 0.37

Figure 3. Model of individual interest in science: Model B




Table 5. Individual interest in science and macro culture: Model C t indices

Colombia(T/S)

USA(T/SE)

Estonia(SR/S)

Sweden(SR/SE)

Enjoyment (ENJ) ← Knowledge (SCK) − 0.01 0.15 0.11 0.19Enjoyment (ENJ) ← Personal value (PV) 0.54 0.57 0.54 0.64Interest (INS) ← Enjoyment (ENJ) 0.29 0.48 0.40 0.55Interest (INS) ← Personal value (PVS) 0.25 0.26 0.25 0.21

Activities (ACT) ← Personal value (PVS) 0.19 0.15 0.16 0.19Activities (ACT) ← Interest (INS) 0.14 0.18 0.24 0.20Activities (ACT) ← Enjoyment (ENJ) 0.31 0.37 0.34 0.31Future science (FUT) ← Personal value (PVS) 0.22 0.25 0.19 0.32Future science (FUT) ← Enjoyment (ENJ) 0.32 0.36 0.33 0.35Future science (FUT) ← Interest (INS) 0.14 0.16 0.17 0.13

NFI 0.992 0.996 0.990 0.997RFI 0.946 0.970 0.932 0.976IFI 0.993 0.996 0.991 0.997

TLI 0.950 0.972 0.934 0.978CFI 0.993 0.996 0.991 0.997

RMSEA 0.056 0.054 0.076 0.052R 2

Enjoyment (ENJ) 0.29 0.38 0.33 0.51Interest (INS) 0.23 0.44 0.34 0.50

Activities (ACT) 0.27 0.37 0.39 0.37

Future Science (FUT) 0.31 0.44 0.34 0.50

Figure 4. Model of individual interest in science: Model C showing coefficients for Sweden




prediction of reports of current participation and intentions to participate in sciencein the future.

It is clear from Models B and C that there are some important differences in theway the processes that make up interest in science are connected together for young

adolescent students from different countries. As shown in the parameter estimates forboth Models B and C, there was considerable variation in the contribution of scienceknowledge to the complex of interest variables. Knowledge of science as representedin the PISA index of science knowledge and understanding was largely unrelated toratings of interest in science for Colombia, where participation in higher education isnot the norm for the majority of students, and where science and technology are arelatively recent part of the fabric of careers and future activities. Colombian students’ratings of interest in science were high but were not contingent on their knowledgebase in science. On the other hand, in countries where participation in higher educa-tion is the norm and where science is deeply embedded in the general fabric of thelifestyle, such as Sweden, students’ levels of science knowledge appear to play a moreimportant part in their expression of interest in science across a variety of indicators.

On the basis of these findings it appears that scores on measures of interest inscience such as those included in this investigation need to be interpreted as part of anetwork of related processes. Clearly students who experience enjoyment whenlearning science and are aware that science has personal importance for them havestrong intentions for further participation, and there is evidence that such intentionsinfluence students’ actual behaviour. However, the variable relations with knowl-edge of science within the complex of interest processes suggest that interpretation

of the educational significance of interest in learning about science requires comple-mentary indications of students’ knowledge of science. If a student’s assertion thathe or she is interested in learning about science is based on a strong knowledge of science, this requires a different type of educational provision and support thanrequired for a student without a strong knowledge base who reports the same level of interest in learning science. As Renninger (2009) suggests in her description of awell-developed individual interest, when students express enjoyment and value for adomain in the absence of a strong knowledge base, they are unlikely to engage in thesame depth of self-directed information seeking as students whose enjoyment and

valuing of the domain is underpinned by a strong knowledge base. An earlierdescription of the experience of enjoying and valuing a domain as an ‘attraction’rather than an individual interest (Renninger, 1992) underlines this difference inexperiences that are needed to support, maintain, and deepen that interest.

Conclusion

Over the past two decades, many Western countries have experienced a decliningpercentage of students studying science, technology, engineering, and mathematics(STEM), and in 2005 the OECD Global Science Forum concluded that govern-ments needed to take steps to make science and technology studies more attractive if participation in science was to be increased (OECD, 2006). However, making




science and technology studies more attractive requires a close understanding of theprocesses that contribute to students’ interest in science. Models such as the four-phase model of interest development of Hidi and Renninger (2006) propose thatknowledge, affect, and value are essential components of a strong individual interest

in science. The analyses we have presented here extend the basic model presented byHidi and Renninger to show that there are important differences in the ways that theessential components of interest in science are connected and that these differencescan reflect contrasts in students’ broad cultural backgrounds. Both Models B and Cas described in this paper locate enjoyment of science as a central component of thenetwork of relations within the set of interest in science variables. In addition, thepersonal value students place on science has an important influence on their enjoy-ment of science. Model B was only appropriate for one of the countries, Colombia,while Model C showed an acceptable fit for all four countries and the fit forColombia was marginally better than the fit for Model B.

As Models B and C demonstrate, there are differences in the ways knowledge,affect, and value components are related to each other in data from the countriesselected for comparison. The countries differed in their profiles across the set of interest in science variables; significantly, they also differed on levels of scienceknowledge. In those countries, where the overall level of science knowledge washigher, personal value of science and knowledge were more closely connected. BothModels B and C indicated that enjoyment of science was a central component in thenetwork of relations and personal value was an important factor predicting enjoy-ment. Where the overall level of science knowledge was higher, science knowledge

was also a predictor of enjoyment. As has been shown in research into other areas of children’s development (e.g. Dekker & Fischer, 2008; Larson & Verma, 1999), thehistorical and cultural traditions of countries provide the macrocontext for children’sdevelopment. Our analyses of PISA data indicated parallel findings by identifyingtwo models with different patterns of interconnection between the knowledge, value,and affect variables that constitute interest in science. Models of interest in sciencedeveloped in Western traditions may not always fit the pattern of relations thatdevelop in different cultural contexts.

In addition, having a general interest in learning science predicts both current and

intended future participation in science-related activities. Both Models B and Cdemonstrate the centrality of enjoyment of science for current and intended futureparticipation in science-related activities. We conclude from these data thatprogrammes of science education that are perceived by students to be personallyimportant and that they enjoy doing will be associated with stronger interest inlearning about science. If, as other research has shown (Khoo & Ainley, 2005),intentions are important influences on future educational participation, these find-ings suggest that curricula that recognise the importance of students’ enjoyment of science and sense of its personal importance are likely to increase students’ partici-pation in the science activities immediately available in their environment. In addi-tion, they are likely to maintain their participation whether in further study, careersor involvement in science projects.




Acknowledgements

Earlier versions of this paper were presented in 2009 at the conference of the EuropeanAssociation for Research in Learning and Instruction, Amsterdam, August, 2009, andat the European Commission Conference, ‘Improving Education: Evidence fromSecondary Analysis of International Studies’, Stockholm, November, 2009.

Notes

1. The exception to this general level of response was the set of items requiring students torecord interest and support for science ratings to topics that they worked on as part of themeasure of science achievement. With respect to the interest ratings, at the end of designatedproblems students were asked for ratings of how interested they were in engaging further withthe particular topic they had been working on. These items are referred to as the ‘embeddedinterest items’. These embedded items are not dealt with here but are the subject of anotherinvestigation (Ainley & Ainley, in press).

2. UIS data base accessed from http://www.uis.unesco.org/ev.php?URL_ID=3753&URL_ DO=DO_TOPIC&URL_SECTION=201

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A Cultural Perspective on the Structure of Student Interest in Science

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