Forskningsprojekt Skolans kemiundervisning har flera mål: utveckla kunskaper, motivation och positiva attityder till ämnet. Projektet syftar till att undersöka hur dessa ”färdigheter” hänger ihop med varandra och hur undervisningen bidrar till deras utveckling.
Internationellt så minskar elevers intresse för att lära kemi. Intresset för högre studier i kemi är dock 7 ggr. högre i Tyskland än i Sverige. Vi vill undersöka hur elevers kunskaper och motivationskarakteristika utvecklas över åk. 5-11 i Sverige och Tyskland - och om/hur dessa förändringar hänger ihop med egenskaper hos skolsystemet eller undervisningen. Relationer mellan kognition och motivation studeras genom enkäter, tester, observationer och intervjuer. Multivariata, probabilistiska och ”kvalitativa metoder ” metoder kommer att användas integrerat för att analysera samband mellan variabler.
Vetenskapsrådet, 2014-2017: 6 416 000 kr
Purpose and aims
Learning outcomes in education are multivariate: Students should learn the basic concepts and methods of different domains, develop interests, become motivated to engage in further learning processes and societal discussions, and gain insights into professional career perspectives for their own future. As research shows, outcomes of education do often not fulfil those goals. The goal of this project is therefore to improve the understanding of the characteristics and interplay of variables influencing outcomes of education in chemistry as an exemplary subject area, and to describe changes in students’ knowledge, interest, beliefs and motivational characteristics over time of secondary education.
The subject-area of chemistry was chosen because of its ambivalent character: It is a subject with high relevance in daily-life, with many career perspectives and a scope for inspiring activities in class, such as carrying out investigations or observing surprising phenomena. Nevertheless, research in chemistry education show low values for interest and perceived relevance. Difficulties of students in understanding and applying basic concepts of chemistry and the nature of science have also been identified. Empirical data suggests that highlighting relevance or fostering active engagement of students in class have positive effects on learning outcomes [1, 2].
Other studies also describe correlations between cognitive abilities and interests on the one hand, and achievement on the other . The reported effects are usually small to medium, which may depend on not considering the sub-dimensionality of the constructs or domain-specific influences. Therefore, the results of empirical research are still ambiguous and inconclusive and researchers still highlight the demand for more sophisticated studies on the interplay of variables influencing learning processes and learning outcomes to improve learning environments by differentiated conceptual approaches .
This study aims at 1) analysing the constructs of cognitive learning outcomes, interests, beliefs, motivational characteristics, and the perceived characteristics of the learning environment in chemistry education; 2) to develop a more sophisticated and holistic model describing the interplay of personal variables of students to better understand the effects of authentic school situations on students’ motivation and cognition; and 3) to investigate how outcomes are shaped by these interactions as students move through school years 5-11.
The study will be carried out in two countries – Sweden and Germany – with comparable cultural and socioeconomic characteristics, but differences in the school system and in science / chemistry education. Quantitative and qualitative data will be collected and compared. The model and the set of instruments can be used in future educational research and for the development of approaches fostering the learning of different student profiles more systematically and effectively.
The research questions are:
- Which structures can be identified for the investigated cognitive and affective constructs?
- What are the relationships between the analysed constructs?
- How do the results for the constructs and their relationships develop over time?
- Which differences can be identified for different students, school systems and curricula?
Survey of the field, including our preliminary results
According to frameworks of educational approaches such as PISA, three major goals can be pointed out for science education: the development of a “scientific literacy”, highlighting the interactions of science and society; the foundation for future learning and career perspectives; and the personal development of the students. Based on those directions, the main constructs described below can be derived as expected learning outcomes:
Conceptual understanding in and about science
Large scale studies like TIMSS and PISA as well as a large number of studies on students´ preconceptions have pointed out several problems, e.g. that students often mix daily-life and scientific explanations, have problems to apply concepts in different contexts, or do not separate between different levels of explanation . Most of these studies are based on summative test scores or derive competence levels post-hoc from the data and rarely offer sophisticated models for the identification of levels or forms of explanations and developmental steps. To understand levels of understanding and developmental steps fostered by instruction, a “nucleus” is needed in terms of a learning theory that drives the development of test instruments and the analysis of the obtained results . Such a nucleus can be found in taxonomies of knowledge like the SOLO or Bloom´s taxonomy. For the subject domain of chemistry, hierarchical models like the Model of Hierarchical Complexity (MHC-C)  have been successfully applied. Based on this model, test instruments have been developed for basic concepts and important curriculum areas of chemistry education, such as redox-reactions or acids and bases. The complexity dimension was able to explain up to 60% of students´ variation in performance. Across different age groups (school year 6 - 11) students' performance increased. Relations between the development of the students´ performance and the curricula were hypothesized and will be further investigated .
Studies indicate that students’ interests correlates with their future study choices  as well as with the quality of learning and performance . Therefore, results on interest in chemistry are worrying, as they often report low interest and negative perceptions of relevance . To understand the causes of this trend and the possible interaction of interest, motivation and cognitive outcomes in a better way, more sophisticated studies have been carried out for the construct of interest. The “IPN interest study” identified three dimensions within students´ interests: interests in topics, in contexts of application of knowledge, and in activities . Even more differentiated, the so called RIASEC model  describes six dimensions of areas for personal and professional interests: the realistic, the investigative, the artistic, the social, the enterprising and the conventional profile. Empirical results based on both models show differences between boys and girls, for example, but also correlations with other personal variables, e.g., competence beliefs . In our on-going research, we have successfully adapted the RIASEC model to analyse the students´ interests within science domains, such as chemistry, in a sophisticated way. Preliminary results indicate that students´ interests also show different trends according to scientific activities in the different RIASEC areas, offering a better understanding and an empirical model to design and explain effects of learning environments.
Studies on beliefs in science comprise different aspects, for example, beliefs on the nature of knowledge (epistemological beliefs), on the nature of scientific inquiry as a process, on the personal relevance of science and science professions or about scientists as persons. These aspects have often been identified as insufficient among students . Current knowledge in science is often regarded by students as consisting of a collection of independent facts, as stable and certain, and information is directly assimilated without further interpretation and discussion of, for example, experimental observations. According to the literature, students seem to image scientists as (male) researchers working on their own in a laboratory. However, on-going studies at IPN, applying the RIASEC model to assess students´ beliefs about scientists and their fields of activities, have led to preliminary results showing that the students´ beliefs are much more sophisticated than reported so far. Regarding effects of students’ epistemological beliefs on cognition and motivation (and interest), results are ambiguous, at least in science. Much of this ambiguity might be explained by the difficulties to obtain reliable and valid measures of epistemological beliefs . Our research indicates that students epistemological beliefs do have impact on students’ motivation and cognition – when considered in relation to the features of the learning situation [16, 17]. The match between students’ epistemological beliefs and situational characteristics, rather than the beliefs alone, have indeed been shown to influence students’ emotional experiences, motivation and cognition in other studies .
The construct of motivation has been described by different models. In this project, we will investigate specific sub-constructs related to motivation that have been identified as influencing interest, beliefs and cognitive learning outcomes. Achievement Goal Theory and Self-Determination Theory (SDT) will be central to our study. These are complementary, comprising students’ explicit goals as well as the extent to which students’ engagement in learning is controlled or autonomously regulated, and have been shown to predict different aspects of students’ emotional experiences, value beliefs, behaviour and cognition during learning, and performance on tests. Classroom and school structures, e.g., reward structures, mastery- or performance oriented teaching/assessment or level of autonomy support, have been shown to influence both these aspects of student motivation [4, 19, 20]. Unfortunately, our research indicate that students tend to become more competitive, on behalf of the will to master what is taught, and more externally regulated in their learning over the school years 4-9 . This is in line with other research, indicating that mastery goals alone might not be adaptive in relation to the reward structures in school [20, 22] and that students often do not see the meaning with learning what is taught in school , thus learning for the sake of grades rather than because the subject is perceived as important, interesting or enjoyable. A substantial amount of research has shown that this has negative implications for students learning. Thus, closer investigation of how long term exposure to school affects students motivation type and achievement goals is highly warranted.
We have adapted and validated the Achievement Goal Questionnaire  and the Academic Self-Regulation Questionnaire , which have proven to be important in predicting behaviour, emotions and performance on knowledge tests. However, a close examination of how different achievement goals and regulatory modes can be linked in an integrative fashion with other constructs, such as (inter alia) interest, motives and values is still needed ”…to fully account for behaviour in achievement settings.” (, p.626) Hence, the aim of the present study, to integrate several relevant constructs into a holistic and fine grained model to describe students’ characteristics, is justified.
Several studies have shown that student’s perceptions of their classroom environment correlate with motivation to learn as well as their conceptual development. The questionnaire “What is happening in this class” (WIHIC) has often been used to assess student cohesiveness, teacher support, involvement, task orientation and equity. Our own questionnaire, partly used in the Swedish TIMSS 2011, is used to describe autonomy support, reward structures, and home support. Substantial empirical and theoretical arguments exists for the association between these constructs and (inter alia) student motivation type, attitudes towards science, and conceptual development [e.g., 25, 26]. However, researchers argue there is still a need for more multi-faceted studies on how these relationships develop over time .
In summary, the research foundation for the constructs to be investigated in this study is substantial. However, there are several areas that need further study: Correlations have often been identified for variables considered as one-dimensional constructs (e.g. interest in PISA). Most studies in educational research do not account for the influence of the domain when operationalizing the constructs . Results are still ambiguous and inconclusive and researchers have called for more complex models to better understand the effects of authentic school situations on students’ motivation and cognition . We argue that our proposed study will contribute substantially to the development of such a model within the domain of chemistry. The multifaceted approach covers a wide array of desired learning outcomes that are partially overlapping but have different implications for learning and have (potentially) different interaction patterns with situational features. The conceptualization of the constructs will reflect the characteristics of the domain, and the interplay between the constructs will be analysed in detail on the level of subscales.
Based on findings described above, exemplary hypotheses are as follows:
• Based on the RIASEC model, different profiles of students´ interests will be found for different achievement and cognitive ability levels .
• Students’ progress of conceptual understanding over school years can be described according to the model of complexity of .
• The fine-grained models will describe and/or predict a substantial proportion of students’ development of motivational characteristics and performance . Examples of expected interrelationships are:
o mastery goals are positively related to certain aspects of students' conceptual understanding, differing from the cognitive skills developed by students with performance orientations .
o effects of students’ epistemological beliefs on learning will be mediated by competence and utility appraisals in relation to perceived structure and demands of instruction .
o Students’ beliefs and goal orientations are dependent on, and will vary according to the study context over the school years and between countries .
• Relationships between variables will be similar between comparable groups in the two countries, but students’ characteristics and classroom perceptions will differ.
Operationalization of constructs - instruments
The test of the cognitive abilities will be based on the Model of Hierarchical Complexity . Incorporating facets of inquiry learning, the model focuses on the logical conjunction of facts and processes in order to establish causal and interdependent relations. Hence, the complexity of a task depends on the explanatory power of the expected argumentation for a successful solution. Additionally, a general cognitive ability test will be applied .
The construct of interest has been operationalized by an adapted version of the RIASEC model; resulting instruments have been piloted successfully. The same model structure will be used to analyse beliefs on the nature of scientists, their activities and the relevance of science.
Operationalization of students’ epistemological beliefs are based on the proposed beliefs structure of Hofer  and have been piloted in previous studies .
Instruments on students’ motivational characteristic (i.e., motivation type, achievement goals, value appraisals, self-efficacy, affective experiences and attributions) and classroom perceptions have been compiled, adapted to the context of chemistry, and extensively piloted in adjunction to TIMSS 2011.
The WIHIC instrument and interviews using stimulated recall techniques will be applied to investigate students’ classroom perceptions. While the WIHIC is well validated  (Swedish validation is on-going), interviews will be developed and validated within the project.
Table 1: List of constructs and the relating instruments
DELETED DUE TO INCOMPATIBILITY WITH THE TEXT FORMAT OF THE RESEARCH DATABASE. AVAILABLE ON REQUEST.
Choice of cohorts
To enlarge the area of expected findings, cohorts will be chosen from two countries, Sweden and Germany, which are comparable according to the social status and culture. In both countries, chemistry is learned in an integrated manner with other science subjects up to grade 7, and thereafter mostly as a separate subject. However, there are considerable differences between the two countries in terms of students’ interest for studying chemistry. Although results on international tests like TIMSS and PISA are comparable, the average fraction of the students who started a university program in chemistry between 2000-2009 is almost seven times higher in Germany than in Sweden (1.45 vs. 0.21 %), according to official statistics [34, 35]. We propose that these differences in interest for chemistry can be at least partially explained by differences in the educational systems and teaching in the respective countries. In chemistry, curricula are similar with regard to the content. However, there are differences according to the approach of teaching and learning. While context-based learning has been included in the German National Educational Standards and in most of the state curricula as an approach to foster interests, Swedish chemistry teaching primarily follows the content structure . Furthermore, students in Germany are sent to different tracks of school according to their abilities very early on (in most German states in year 5, at the age of 10), while no such differentiation exists in Sweden. These factors, and others to be measured, can have effects on the students´ understanding (due to differentiated learning opportunities and supporting structures) and their motivational characteristics (e.g., due to competition, feelings of competence, or expectancies of future success or scope for future career choice).
To be able to compare results between school systems, at least 250 students per group per country will be chosen (based on power analysis for ANOVA with repeated measures and an expected effect size of d = 0.15). A representative sampling procedure is not intended, but a stratified selection of classes will account for school districts, socio-economic status, and town/countryside ratio.
For further analyses within a school system resp. within a certain class environment, students for further qualitative investigations will be selected by propensity score matching, according to the multivariate characterization from the questionnaires and test data. Approx. 8 students from 3 classes per system (i.e., 24 students in total) will be chosen and followed throughout a period of three years, starting in grade 5.
Design of the study
The whole study will contain four sub-studies and activities: (1) the adjustment of instruments for different age groups in both countries, (2) a quantitative cross-sectional investigation, (3) a longitudinal study (both quantitative and qualitative), and (4) implementation measures.
(1) The adjustment of the instruments will be carried out in three grades (5, 8, and 11; at least 100 students per group per country) to cover the whole range of the sample and to ensure the appropriateness of all instruments for the intended sample.
(2) Building up on this, we will set up a cross-sectional study in both countries approximately in September 2014 at the beginning of the school year. The choice of grades will involve both transition years (e.g. in year 5 and 10) and a continuous phase of development. Thus, the sample will comprise the year groups 5 to 11. The questionnaires and tests will be carried out in school. Data will be combined for both countries and analysed by the two research groups in close cooperation. To allow the testing of the interplay of the four differentiated constructs in a sophisticated and holistic way, test instruments for all sub-scales have to be implemented. This will take a testing time of at least 3 hours. To ensure that enough students will be involved, co-operation contracts will be negotiated with the participating schools, ascertaining mutual benefit for all parties. Contacts with schools are already established.
(3) The cross-sectional study will be taken as the starting point for longitudinal studies in two cohorts, based on the same instruments (with adaptations between test occasions, to avoid memorization effects). We will follow up students from grade 5 to 7 and from grade 9 to 11 to be able to investigate changes in the early stages of secondary education as well as in the transition phase into upper secondary education. The first is regarded as a phase for manifesting personal attitudes; the second influences the choice of further careers. In addition to this quantitative longitudinal study, we will also carry out qualitative investigations to produce in-depth insights in relation to statistical results. Based on the questionnaire and test data, students will be matched across classes and countries. Approx. 24 students with different profiles will be chosen and interviewed twice a year. The students will be asked about (inter alia) their perceived reward structure and learning processes in chemistry classes as well as own goals and regulatory modes. The interviews will use classroom material and students notes as further stimuli and as triangulation of classroom perceptions.
(4) Results will be implemented into practice and research for further developments. Feedback and discussions of conclusion from the project with teachers, parents and students will be performed after the cross sectional data analysis. This aims at initiating learning communities at the participating schools and guidelines for teacher training. Additionally, an international symposium with outstanding researchers in the field shall be organised, leading to a book or special issue publication.
Methods of data analyses
Three different types of analyses will be applied: classical statistical tests, projection methods such as PCA and OPLS/PLS-DA analyses, and probabilistic analyses based on Rasch models. The projection methods will be used to analyse the dimensionality and the relations between the theoretical sub-dimensions of each constructs and to distinguish differences between year groups and countries. Rasch and Partial Credit models will be used for analyses of results on the conceptual understanding test. All these methods are well suited for analysis of “noisy” data with missing values, both common problems in this type of investigations. The output from these procedures will then be applied to classical analysis to further describe interrelations and interdependencies between the different constructs.
The qualitative data will be categorized deductively and inductively. Network analysis will be used to create (graphical) representations of argumentation patterns from interview data which are validated against results of a narrative analysis performed in parallel and then mirrored against the quantitative data by PLS/OPLS.
Time plan and milestones
The timeline and milestone plan is adjusted to the four sub-studies of the project.
Year 1 (2014)
Adjustment of instruments, set up of the sample for the on-going studies; cross-sectional investigation at the beginning of the school year; first data analyses and propensity score matching for the choice of students for the qualitative studies
Years 2 (2015), 3 (2016) and 4 (2017)
Longitudinal quantitative study (data collection around February / half term), and qualitative study (twice a year around May and November); data analyses; feedback to schools based on cross-sectional data approx. in year 3; first publications
Year 5 (2018)
Final analyses and publications; symposium with outstanding researchers in the field
The project as a combination of cross-sectional and longitudinal data on the one hand and of quantitative and qualitative data on the other will expand our knowledge on how the school system affects students’ beliefs, interests, motivational characteristics, classroom perception and how these in turn are connected to the character of the knowledge students develop. This has not been sufficiently researched before and is important for understanding the still prevailing ambiguity regarding the often reciprocal relationships between interest, beliefs, motivation, cognition, and classroom perception.
The level of detail in the information obtained will provide unique opportunities for researchers and developers within the programme, as well as in the research community in general, to produce suggestions for designs, on classroom as well as system level, to increase students’ motivation to learn science and improve the quality of learning. The implementation both into practice and the scientific community will lead to products that can be used for follow-up studies as well as for school development programs.
National and international collaboration
The program will be conducted in cooperation, on equal basis, between Umeå University, Sweden, and the Leibniz-Institute for Science and Mathematics Education (IPN) in Kiel, Germany. Well-functioning cooperation in research and research education between the two participating departments is already established since spring 2010. On-going PhD, Licentiate and Master studies, piloting and applying the instruments named above, are already being supervised / supported by both partners and will be involved in this project.
Dr. Mikael Winberg will coordinate the Swedish study. He will focus on the analysis of students’ development of motivational characteristics and the multivariate relationships between variables in the fine grained models.
Dr. Madelen Bodin is a physics education researcher. Her expertise is within network analyses of beliefs and knowledge structures. She will focus on analyses of interviews, and associated method development.
The doctoral student will focus the longitudinal aspect of the project, analyzing relationships between students’ perceptions of classroom and school system versus the development of motivational characteristics.
Professor Dr. Ilka Parchmann (IPN and guest professor in Umea) will be in charge for the studies on interest and beliefs, referring to her former and current work on context-based learning and the design of classroom and out-of-school learning environments for different students.
Dr. Sascha Bernholt will be in charge for the test design and analyses investigating the cognitive abilities, especially the understanding of basic concepts like matter or energy.
Dr. Andrea Anschuetz will be in charge for the design and analysis of the affective measures.
One PhD student at IPN will analyse cognitive learning outcomes in both countries and relate them to motivational aspects. The second PhD student will analyse students´ interests and Nature of Science beliefs in both countries and its relation to cognitive learning outcomes and general epistemological beliefs.
Information from students will be collected through questionnaires and knowledge tests that have been previously piloted and used. No effects on participants have been observed during administration of the tests/questionnaires or in the interviews that were performed in adjunction to these in the pilot studies. No stages in the program will aim at influencing participants in any way. The observations and interviews that will be performed in the longitudinal stage of the program will be conducted with caution and data will be blinded before they are reported. The guidelines for ethics in research described in “God forskningssed” (VR 1:2011) and VRFS 2009:1 as well as applicable Swedish, e.g., “etikprövningslagen”, and German legislation will be followed.
1. Gilbert, J., A. Bulte, and A. Pilot, Special Issue on Context-based learning. International Journal of Science Education 2006. 28(9).
2. Fechner, S., Effects of Context-oriented Learning on Student Interest and Achievement in Chemistry Education. 2010, Berlin: Logos.
3. Helmke, A. and S.W. Schrader, eds. Determinanten der Schulleistung [Predictors of school achievement]. 3 ed. Handwörterbuch Pädagogische Psychologie, ed. D. Rost. 2006, Beltz: Weinheim. 83-94.
4. Covington, M.V., Goal theory, motivation, and school achievement: an integrative review. Annual Review of Psychology, 2000. 51(1): p. 171-200.
5. Barker, V., Beyond Appearances: Students’ misconceptions about basic chemical ideas. 2004, Royal Society of Chemistry.
6. Pellegrino, J.W., The Design of an Assessment System Focused on Student Achievement: A Learning Sciences Perspective on Issues of Competence, Growth, and Measurement. , in Making It Tangible - Learning Outcomes in Science Education. , S. Bernholt, K. Neumann, and P. Nentwig, Editors. 2012, Waxmann: Münster. p. (pp. 87–117).
7. Bernholt, S. and I. Parchmann, Assessing the Complexity of Students' Knowledge in Chemistry. Chemistry Education Research and Practice, 2011. 12(2): p. 167-173.
8. Alexander, P.A., et al., Factors Affecting Career Choice: Comparison Between Students from Computer and Other Disciplines. Journal of Science Education and Technology, 2011. 20(3): p. 300-315.
9. Schiefele, U., The role of interest in motivation and learning., in Intelligence and personality, J.M. Collis and S. Messick, Editors. 2001, Erlbaum: Mahwah, NJ. p. 163 -194. .
10. Lindahl, B., Lust att lära naturvetenskap och teknik? En longitudinell studie om vägen till gymnasiet. Acta Universitatis Gothoburgensis. 2003, Göteborg: Göteborgs universitet.
11. Gräber, W., Untersuchungen zum Schülerinteresse an Chemie und Chemieunterricht [Studies of students´ interests towards chemistry and chemistry education]. Chemie in der Schule, 1992. 39: p. 270-273.
12. Holland, J.L. and G.D. Gottfredson, Studies of the hexagonal model: An evaluation of the perils of stalking the perfect hexagon. Journal of Vocational Behaviour, 1992. 40: p. 158-170.
13. Köller, O., et al., Kurswahlen von Mädchen und Jungen im Fach Mathematik: Zur Rolle von fachspezifischem Selbstkonzept und Interesse. . Zeitschrift für Pädagogische Psychologie, 2000. 14: p. 26-37.
14. Lederman, N.G., et al., Views of Nature of Science Questionnaire: Toward Valid and Meaningful Assessment of Learners' Conceptions of Nature of Science. Journal of research in science teaching, 2002. 39(6): p. 497-521.
15. DeBacker, T.K., et al., The Challenge of Measuring Epistemic Beliefs: An Analysis of Three Self-Report Instruments. Journal of Experimental Education, 2008. 76(3): p. 281-312.
16. Bodin, M. and M. Winberg, Role of beliefs and emotions in numerical problem solving in university physics education. Phys. Rev. ST Phys. Educ. Res., 2012. 8: p. 010108.
17. Winberg, T.M. and C.A.R. Berg, Students' cognitive focus during a chemistry laboratory exercise: Effects of a computer simulated prelab. Journal of Research in Science Teaching, 2007. 44(8): p. 1108-1133.
18. Windschitl, M. and T. Andre, Using computer simulations to enhance conceptual change: The roles of constructivist instruction and student epistemological beliefs. Journal of research in science teaching, 1998. 35(2): p. 145-160.
19. Deci, E.L., R. Koestner, and R.M. Ryan, Extrinsic Rewards and Intrinsic Motivation in Education: Reconsidered Once Again. Review of Educational Research, 2001. 71(1): p. 1-27.
20. Giota, Elevers motivation – samband med självuppfattning, välbefinnande och prestationer., in Utvärdering Genom Uppföljning. Longitudinell individforskning under ett halvsekel. 2011, University of Gothenburg: Gothenburg.
21. Vedder-Weiss, D. and D. Fortus, Adolescents' declining motivation to learn science: A follow-up study. Journal of Research in Science Teaching, 2012. 49(9): p. 1057-1095.
22. Lau, K.-L. and J. Lee, Examining Hong Kong students' achievement goals and their relations with students' perceived classroom environment and strategy use. Educational Psychology, 2008. 28(4): p. 357-372.
23. Elliot, A.J. and K. Murayama, On the Measurement of Achievement Goals: Critique, Illustration, and Application. Journal of Educational Psychology, 2008. 100(3): p. 613-628.
24. Ryan, R.M. and J.P. Connell, Perceived locus of causality and internalization: Examining reasons for acting in two domains. Jourmal of personality and social psychology, 1989. 57: p. 749-761.
25. den Brok, P., et al., Californian Science Students' Perceptions of Their Classroom Learning Environments. Educational Research and Evaluation, 2006. 12(1): p. 3-25.
26. Winberg, T.M., T. Palm, and J. Sullivan Hellgren, Elevers motivation i matematik. 2010, Nationellt centrum för matematikutveckling. p. 1-41.
27. Wigfield, A. and J. Cambria, Students' Achievement Values, Goal Orientations, and Interest: Definitions, Development, and Relations to Achievement Outcomes. Developmental Review, 2010. 30(1): p. 1-35.
28. Weinert, F.E., ed. Concept of competence: A conceptual clariﬁcation. Deﬁning and Selecting Key Competencies ed. D.S. Rychen and L.H. Salganik. 2001, Hogrefe & Huber Publishers. 45–65.
29. Vock, M., O. Köller, and G. Nagy, Vocational interests of intellectually gifted and highly achieving young adults. British Journal of Educational Psychology, 2012.
30. Louca, L., et al., Epistemological Resources: Applying a New Epistemological Framework to Science Instruction. Educational Psychologist, 2004. 39(1): p. 57-68.
31. Raven, J., The Raven's Progressive Matrices: Change and Stability over Culture and Time. Cognitive Psychology, 2000. 41(1): p. 1-48.
32. Hofer, B.K., Exploring the dimensions of personal epistemology in differing classroom contexts: Student interpretations during the first year of college. Contemporary Educational Psychology, 2004. 29: p. 129-163.
33. Fraser, B.J., J.M. Aldridge, and F.S.G. Adolphe, A Cross-National Study of Secondary Science Classroom Environments in Australia and Indonesia. Research in Science Education, 2010. 40(4): p. 551-571.
34. GDCh (2011) Chemiestudiengänge in Deutschland [Academic Chemistry programs in Germany].
35. Högskoleverket, Behöriga förstahandssökande och antagna till program efter inriktning höstterminerna 1998–2011. Korrigerad 2011-11-23 2011, Statistics Sweden.
36. Broman, K., M. Ekborg, and D. Johnels, Chemistry in crisis? Perspectives on teaching and learning chemistry in Swedish upper secondary schools. Nordic Journal of Science Education, 2011. 7(1): p. 43-53.