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Attitude, motivation and self-esteem
...
in mathematics, science and technology
researched by the SECURE project
– Polish results against the background in Europe
Dagmara Sokołowska, Job de Meyere, Mateusz Wojtaszek, Witold Zawadzki, Grzegorz BrzezinkaSummary:
The SECURE project was founded under the 7th Framework Programme to provide research results of current mathema-tics, science and technology (MST) curricula for pupils aged 5, 8, 11 and 13 in ten EU member states, including Poland. The curricula have been examined throughout three differ-ent represdiffer-entations, as they are intended by the authorities (in legal documents), implemented by the teachers and perceived by the learners. The research framework at all three levels has been constructed upon the curriculum spider web (van den Akker, 2003) with addition of item “attitude”. The study involved altogether almost 9000 pupils, 1500 teachers and 600 schools in consortium member states. In this
contribu-tion a part of research concerning the average results on (1) learners’ attitude towards MST school subjects, (2) influence of topics, activities and teachers on liking the MST subjects by pupils, (3) learners’ self-esteem in MST subjects and (4) their opinion on the easiness of MST subjects are presented. In par-ticular, we focus on Polish results (regions of Krakow City and Krakow County) and compare them with the European aver-age. The study shows a substantial drop in all the variables measured, mostly evident between ages 8 and 11. In general the trends across ages and subjects in Poland are similar to those in Europe with a few exceptions indicated in the paper.
Key words: mathematics, science and technology, curriculum,
pri-mary education, lower secondary education, attitude
received: 23.12.2014; accepted: 15.02.2015; published: 27.03.2015
dr Dagmara Sokołowska: assistant profesor,
Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Poland
Introduction
The SECURE consortium was established for period 2010-2013 to study MST curricula across Europe and to make a significant contribution to the European knowl-edge on balancing the needs between training for future
scientists and the broader societal needs (SECURE,
2010). The role of the project was to learn what kind of needs, in addition to those expressed by policy mak-ers, (among others in Key Competences, 2007), should be addressed in future MST education in order to en-hance MST literacy and vivid interest towards those subjects within the society. At the same time the con-sortium tried to learn how to encourage children from early age on to undertake future careers in MST. The study took place in ten European countries (regions) of well-defined educational systems: Austria, Belgium (Flanders), Cyprus, Germany (Saxony), Italy, the Neth-erlands, Poland, Slovenia, Sweden and the United King-dom (England). The research involved learners of ages 5, 8, 11 and 13, their MST teachers as well as the MST curricula documents relevant to those ages. One of the main streams of the project was the examination of the role of an affective domain (consisting, among others, of “attitude”, “motivation”, “confidence” and “interest”) in everyday practice.
dr Job de Meyere: Research Coordinator,
Limburg University College, Belgium
mgr Grzegorz Brzezinka: PhD student, Faculty of Physics,
Astronomy and Applied Computer Science, Jagiellonian University
mgr Mateusz Wojtaszek: PhD student, Faculty of Physics,
Astronomy and Applied Computer Science, Jagiellonian University
dr Witold Zawadzki: assistant, Faculty of Physics,
Astronomy and Applied Computer Science, Jagiellonian University
Acknowledgement
This work is based on the SECURE research project
(No SIS-CT-2010-266640), which received funding from the European’s Unions Seventh Framework Program for Research and Development. The authors would like to acknowledge the work on data collection done by other members of the SECURE Consortium: Judith Aldrian, Veronika Rechberger, Lieveke Hellemans, Ann Vereycken, Stefan Haesen, Tom Lambert, Wim Peeters, Ervin Van de Put, Michalis Livitzis, Maria Hadjidimi-tri, Jessie Best, Meike Willeke, Marisa Michelini, Stefano Vercellati, Lorenzo Santi, Elvira Folmer, Marja van Graft, Wout Ottevanger, Barbara Rovsek, Jurij Bajc, Göran Nordström, Edvard Nordlander, Iiris Attorps and Gren Ireson. The authors would like to thank the reviewers for the constructive remarks which helped to improve the paper substantially.
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The role of affective domain in learning has been studied intensively for the last 30-40 years, among oth-ers in MST education (e.g. Middleton & Photini, 1999; Osborne et al., 2003; Logan and Skamp, 2008). Cogni-tive and affecCogni-tive components of learning have been re-cently researched (together or separately) also in a large number of studies, including world-wide studies, such as PISA, 2012 (of 15 year olds), TIMSS, 2011 (of 10 and 14 year olds) and ROSE, 2009, (Sjøberg & Schreiner, 2010; of 15 year olds). Broadly understood “attitude”, “motivation” and “confidence” have found their notice-able place also in the documents establishing the Eu-ropean Union’s vision for the future education (Euro-pean Council, 2006; Key Competences, 2007), where it is mentioned as one of the three main components of, so called, Key Competences
Key competences are those which all individuals need for personal fulfilment and development, active citizenship, social inclusion and employment.
(…) Competences are defined here as a combination of knowledge, skills and attitudes appropriate to the context. (...) Motivation and confidence are crucial to an individual’s competence.
Thus it has been revealed that not only some educa-tors and researchers, but also the politicians found the affective aspect of learning important and indispensa-ble for the future development of the society.
Among eight Key Competences mentioned above, a mathematical competence and basic competences in science and technology have been listed as those “con-tributing to a successful life”. School subjects, such as mathematics, science and technology, become a natu-ral platform for development of those competences. However at the same time they can substantially sup-port progress in other key competences, such as digital competence, learning to learn, social competences and sense of initiative and entrepreneurship, giving them
all a practical context. This means that the affective do-main, neglected in MST education in the past, should in foreseeable future gain much more attention of all actors in the field. Investigation of the state of the art of different aspects of the affective domain in education seems to be a first step on this path.
To our knowledge, apart from research on educa-tional practices, the joint studies on mathematics, sci-ence and technology education barely ever come onto the stage. The SECURE project was established to fill this gap by providing research outcomes on current MST curricula, their implementation and their percep-tion by teachers and learners, among others, also con-cerning the affective domain.
Theoretical Framework
Various meanings of “curriculum” can be found in different contexts of educational research (Jackson, 1992; Pinar et al., 1995; Walker, 2003). Among others it can be considered as a “plan for learning” (Taba, 1962). As such it can be researched from three perspectives: as it is intended by the writers, implemented by the teach-ers and perceived by the learnteach-ers (Goodlad, 1979), which is especially useful in the analysis of the effectiveness of the curricula. In 2003 van den Akker proposed a more detailed approach, in which the curriculum was visual-ized on a spider web (fig. 1), revealing the relationships between different curriculum components – Rationale
Fig. 1. Curriculum spider web
Based on the original work of van den Akker (2003).
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in the centre of the picture, surrounded by nine other aspects of learning, Aims and Objectives, Content, Learning Activities, Teacher Role, Materials and Re-sources, Grouping, Location, Time, and Assessment. In view of the overall focus of the project, yet another aspect, “Attitude and Interest” has been added to our study.
As it is already known from other studies, attitude and interest in MST may consist of a large number of components (Osborne et al., 2003; Kobella 1989) and can be researched from different angles, e.g. “attitude towards MST in general” (Jones et al., 2000; Francis and Greer, 1999), “attitude towards school MST” etc. They may also overlap with each other or even with oth-er constructs, such as ‘motivation’, ‘self-esteem’ and so on (Logan and Scamp, 2008). Different sub-constructs of the attitude have been incorporated in several mod-els of attitudes towards science (Rennie, 1986; Oliver and Simpson, 1988) and researched for mutual correla-tions. It was also proved that the attitude can be influ-enced by many factors, including parents and teachers (Gunderson et al., 2012).
For the purpose of this study all investigated aspects of the affective domain are considered in relation to MST school subjects (Osborne et al., 2003). A part of the study was already presented elsewhere in different form (Sokolowska et al., 2014a, 2014b). In this paper a special attention is drawn to the findings collected in Polish schools and their comparison with average Eu-ropean results.
In particular five research questions have been elab-orated by collecting and analysing evidences in Poland and nine other European countries:
RQ1. What are the learners’ attitudes towards mathematics, science and technology as school subjects across ages 5, 8, 11 and 13?
RQ2. What is the learners’ perception of the influence of to-pics, activities and teachers on a learner positive affective attitude (liking) towards mathematics, science subjects and technology across ages 8, 11 and 13?
RQ3. How high is the learners’ self-esteem in MST subjects across ages 8, 11 and 13?
RQ4. To what extend are MST subjects perceived as “easy” by 8, 11 and 13 year olds?
RQ5. What conclusions can be drawn by comparing Polish and European learners’ answers to RQ1, RQ2, RQ3 and RQ4? The authors would like to point out that in this study the Polish and European results are presented without distinction to gender. The gender aspect of attitude, mo-tivation and self-esteem will be addressed elsewhere.
Methodology
Sample
Data collection has been performed in 15 classes of each researched age group (5, 8, 11 and 13), thus alto-gether in 60 classes in every country. The ages has been chosen to enable investigation of the bridges and the gaps that exist in curricula, on one hand – between kin-dergarten and primary school and, on the other hand – between primary and middle schools. Thus altogether almost 600 classes, 9000 learners and 1500 teachers par-ticipated in the project. In this paper we present only the results based on the answers to the questionnaires for 8, 11 and 13yo learners.
The sample is not totally randomized since only the schools agreeing for taking part in the entire two-year study, which involved particular classes and all MST teachers teaching in those classes could be invited to the project. That is why data have been collected in limited regions (e.g. in Krakow County in Poland). Neverthe-less care was taken to minimize the effect of
conveni-ence sampling, by: (1) contacting not only the schools previously cooperating with the researchers, but also the unknown ones, (2) inviting to the study public, pri-vate and associated schools in proportions reflecting the diversity of a school system in a particular region. In each case, after establishing a contact with a school a class had been pointed out by a school headmaster. It could be selected for the study only after approval of all teachers teaching MST subjects in this class, since all of them were supposed to take part in the research. Each class of the same age belonged to a different school. Care was taken to ensure the highest possible spatial distri-bution of the schools within the limits described above, characteristic for SECURE project. The same rules were implemented by all partners in consortium.
As concerning studies in Poland in 45 classes of learners aged 8, 11 and 13, data collection took place in schools selected from regions limited to Krakow City (28 classes), Krakow County (14 classes) and Ryb-nik city (3 classes). Altogether 36 classes from public schools, 6 classes from private schools and 3 classes from the schools run by the Social Educational Society have been chosen in order to reflect the variety of school categories.
In the entire study in ten countries the class drop off was small (at most 1–2 classes per age, per coun-try), except for the Netherlands and Sweden. Whenever a class withdrew from the project, a new class needed to be contacted, in order to fill the gap, and such rule was obeyed by all members of the consortium, with the exception of the Netherlands and Sweden, which pro-vided data from a lower number of schools (e.g. in case of 11 year olds – from ten classes each), due to difficul-ties encountered in the procedure of class replacement. Even a smaller refusal rate has been observed at the level of teachers, meaning up to 2 teachers of a certain subject at the certain age of learners per country, again
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except for the Netherlands and Sweden, where this rate was comparable to the drop-off rate of the classes.
The learners drop off was very small and manifested just by returning empty questionnaires. In the entire study this means: 4 (out of 2662) cases of 8 year olds, 4 (out of 2779) cases of 11 year olds and 23 (out of 2734) cases of 13 year olds. In many cases however learners chose not to answer the selected questions, nevertheless such ‘blank’ responses never exceeded 10%.
Instruments
The research instruments developed in SECURE project consist of a curriculum screening instrument (CSI), and of several school data collection instruments: teacher questionnaires, learner questionnaires and in-terview protocols for pupils and teachers. All learners’ instruments have been adjusted to the age, whilst only one type of questionnaire and one interview protocol has been designed for all the teachers.
The analysis provided in this contribution is based solely on learners’ questionnaires for 8, 11 and 13 year olds. For 5yo learners data has been collected only through interviews thus it is not included in this study, as the interview analysis is beyond the scope of the paper.
The questionnaires are based on existing scientific literature on science education and science curricu-lum reform (e.g. Atkin and Black, 2003; Black and At-kin, 1996; van den Akker, 1998). Instruments available from previous relevant studies, such as Schreiner and Sjøberg, (2004), TIMSS (1995, 1999, 2003, 2007), and PISA (2000, 2003, 2006, 2009) have all been used as a starting point for the design. All questions in learn-ers’ questionnaires are 2, 3, 4 or 5-point Likert items. In every questionnaire a box for remarks is included to enable expression of the opinions and thoughts not cov-ered by the research instrument. Altogether 35 and 38
questions per each subject have been asked respectively in questionnaires for 8 and 11–13 year olds. Some ques-tions differ a bit for 8 and 11–13 years old learners, and for the latter the questionnaire is slightly longer.
School data collection
Data collection in schools took place in two phases. The drafts of instruments were piloted in DE, IT and NL and revised afterwards on the basis of the preliminary results. The main research took place during the school year 2011/2012. A class could be selected only if on the 1st of September 2011 at least 50% of its pupils was aged
5, 8, 11 or 13, respectively.
The following procedure was implemented for school data collection in each country:
1. In classes participating in research all 8, 11 and 13 year old learners were asked to fill out a relevant questionnaire. Questionnaire part was skipped only in case of 5 year old learners.
2. All MST teachers of 5, 8, 11 and 13 year olds were asked to complete the questionnaire.
3. Four representatives (two girls and two boys) of each class of 5 year olds were interviewed by two researchers at the same time. Pupils were either se-lected blindly by a researcher or chosen by a teach-er on the basis of well-developed communication skills (mostly in case of 5 and 8 year olds).
4. Four representatives from six selected classes of each age: 8, 11 and 13 were interviewed in every country by two researchers at the same time. 5. All MST teachers teaching six selected classes of
each age were interviewed by two researchers at the same time.
Results and data analysis
In this study selected outcomes on (1) learners’ atti-tude towards MST school subjects, (2) influence of top-ics, activities and teachers on liking the MST subjects by pupils, (3) learners’ self-esteem, and (4) their opinion on the easiness of MST subjects are presented. Other re-sults of SECURE project have been included in several articles, e.g. Sokolowska et al. (2014a, 2014b), de Meyere et al. (2014).
Analysis
Questions about attitude towards MST subjects, sources of motivation for those subjects and learners’ self confidence in those subjects are found in the question-naires for 8, 11 and 13 year olds. All answers are given in terms of 2, 3 or 4 ordered categories . After preliminary analysis, the authors decided that some aspects of the affective domain should have been elaborated by taking into account a set of several questions (Liker scale; Lik-ert, 1932), whilst the others – by considering separate questions (Likert items) and reporting them in clusters afterwards. Since different statistical approach should be implemented for Likert-type items and Likert scales (Boone and Boone, 2012; Clason and Dormody, 1994; Cohen et al., 2003), the following analysis procedure was adopted in order to study learners’ answers to vari-ous questions and compare the outcomes across ages.
Likert composite scales
In case of sets of items, for 8yo learners only three levels of agreement have been anticipated for each state-ment, whilst for 11 and 13 year olds a 4-point Likert scale has been attributed to each statement. In order to facilitate a comparison between ages, each answer was scaled as follows.
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• In each item for 8yo ‘no’ has been given a value of ‘1’, ‘a bit’ – a value of ‘2’ and ‘yes’ – a value of ‘3’.
• In each item for 11 and 13yo ‘I completely disagree’ has been given a value of ‘1’, ‘I disagree’ has been equated to ‘2’, ‘I agree’ has been given a value of ‘3’ and ‘I completely agree’ has been equated to ‘4’. Answers given by each pupil to all items in the sub-set were summed up and the sum was in all cases rescaled to a convenient, more intuitive (Johns, 2010) range of values from zero to ten, no matter what kind of the original Likert scale was implemented for this par-ticular set of questions (Norman, 2010; TIMSS, 2011). The procedure of rescaling is justified in itself since any linear transformation of data cannot alter the interpre-tation of the results (King and Minium, 2003). It should be noticed however that in our study such an approach means in fact comparing results based on two scales collapsed from 5-point scale (1 – completely disagree, 2 – disagree, 3 – agree a bit or hesitate, 4 – agree, 5 – completely agree):
• for 8 year olds – a 3-point scale of 1-2|3|4-5
• for 11 and 13 year olds – a 4-point scale of 1|2|4|5 where cutpoints are indicated by vertical bars (see for example Strömberg, 1996). The issue of comparing two Likert scales with different number of responses is not new and was elaborated for example by Wakita, Ueshi-ma and Noguchi (2012) for case of 4-, 5- and 7-point scales, and Attwood et al. (1993) for the case of 2- and 4-point scales, (the letter providing correlations of not less than for both, but at the same time showing supe-riority of a 4-point scale over a 2-point scale in detecting clinical changes).
It is worth to notice that whenever more than one science subject is taught at a certain age in the particular country, all answers collected for different science sub-jects are summed up and jointly rescaled to range 0…10, accordingly.
After checking the internal consistency of the set, e.g. by calculating Cronbach’s alpha, (see for example Croasmun and Ostrom, 2011), the combination of the individual items into composite scales allows for the use of mean, variance, confidence intervals (Cohen, 1994) and a t-test for significance (Boone and Boone, 2012), including power of the test (Cohen, 1992) thus enabling for example the comparison across countries or ages (see Table 4 and the paragraph just below it).
Likert separate items have been all collapsed to
di-chotomous categories, in accordance to the scheme assuring the maximum-possible power effect during collapsing ordered outcomes categories from 5 to 2 re-sponses, (see table 1, binary outcomes with a cutpoint between negative and neutral responses, “1-2|3-5” in Stromberg, 1996). Thus
• for questions with 2-point scale, the values were assigned to the answers as following: 0 – “no”, 1 – “yes”;
• for questions with 3-point scale, the values were assigned to the answers as following: 0 – “no”, 1 – “a bit”, 1 – “yes”;
• for questions with 4-point scale, the values were assigned to the answers as following: 0 – “comple-tely disagree”, 0 – “disagree”, 1 – “agree”, 1 – “com-pletely agree”.
In case of multi-subject responses (i.e. science for all ages in EU and for 13yo learners in Poland), first, all responses were averaged and afterwards 0 – has been assigned to all sums below the midpoint of a scale, and 1 – has been assigned to all sums equal to and above the midpoint of the scale.
Being aware that collapsing the number of catego-ries causes the loss of some information (Atwood et al., 2003) and lowers a bit the power (Strömberg, 1996) such a procedure has been still utilized due to enabling comparison across ages. Yet another argument has been
put forward pro dichotomous treatment of separate items, namely to use logistic regression (Cohen et al., 2003; Wasserman, 1996) in order to analyse the results. We decided not to utilize t-test for significance, since although it is used in a number of studies for analysis of Likert items, we agree that such an approach is not justified, as argued by Jamieson (2004), and Clason and Dormody (1994).
Attitude towards MST
In order to study the aspect of attitude towards MST school subjects a set of items have been examined, com-prising of four exactly the same questions about positive attitude towards each subject (table 1).
Questionnaire for 8, 11 and 13yo
1. I like the things I learn in the subject. 2. I enjoy learning the subject. 3. I would like to do more the subject. 4*. The subject is boring.
* Reversed item
Table 1. A set of items on attitude towards school MST subjects, included in learners’ questionnaires
For 8yo learners only three levels of agreement have been anticipated for each statement (‘yes’, ‘a bit’, ‘no’), whilst for 11 and 13 year olds a 4-point Likert scale (‘completely disagree’, ‘disagree’, ‘agree’ and ‘completely agree’) has been attributed to each statement. We de-cided to include a negatively-worded item since it has been proved that such an inclusion increases reliability of the set (Croasmun and Ostrom, 2011 and references therein).
First, the dimensionality of the test has been checked by using factor analysis and calculating the eigenvalues
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for 18 sets of data separately, combining three ages (8, 11 and 13) , three subjects (mathematics, science and tech-nology) and two groups of responders (learners from Poland and learners from nine other countries under study). In all cases only a single eigenvalue greater than 1.0 has been obtained, proving the unidimensionality of all sets (see table 2). All four loadings have been found to be above 0.7 in each case.
Secondly, an internal consistency reliability test has been utilized for the same 18 sets of data and the Cron-bach’s alpha coefficients have been calculated, in most cases to be above 0.8, thus supporting the hypothesis of internal consistency of the selected set of items (see table 2). It must be noticed that in the questionnaires for 11 and 13 year olds yet another item (‘I like the subject
more than most other subjects’) has been included but it has been checked that its inclusion has not improved Cronbach’s alpha of any set by more than 0.02 in case of all MST subject, so for the sake of uniformity across ages this item has not been selected for the further study.
In order to study the stability of the covariance structure across age groups a statistical test has been
Age Subject Eigenvalue
Standarized Cronbach’s Alpha EU PL EU PL 8 Maths 2.47 2.53 0.79 0.81 Science 2.62 2.75 0.82 0.85 Technology 2.62 2.42 0.82 0.78 11 Maths 2.81 2.40 0.86 0.78 Science 2.91 2.79 0.88 0.85 Technology 2.99 2.97 0.89 0.88 13 Maths 2.68 2.56 0.84 0.81 Science 2.88 3.08 0.87 0.9 Technology 2.96 2.75 0.88 0.85
Table 2. The results of the factor analysis and the internal consistency reliability test for 18 separate data sets, combining three ages, three subjects and two groups of responders EU and PL).
Table 3. The results of statistical test for the stability of covariance structure in mathematics for testing repeatedly H0: ∑i = ∑0 versus H1: ∑i ≠ ∑0, for i = 1, 2, 3.
Age i Statistics Decision
8 1 |Z1| = 0.60 < z0.025 = 1.96 accept H0
11 2 |Z2| = 0.78 < z0.025 = 1.96 accept H0
13 3 |Z3| = 0.85 < z0.025 = 1.96 accept H0
Fig. 2. Attitude towards MST subjects across ages in nine European countries comparing to Poland (Krakow County)
Results for science comprise all science subjects.
8 yo 11yo 13yo 8 yo 11yo 13yo 8 yo 11yo 13yo
Age Subject Mean t Sample sizes p SE 95% confidence intervals
EU PL EU PL EU PL EU PL 8 Maths 7.48 7.27 1.30 2339 274 0.19 0.05 0.16 7.38-7.59 6.96-7.58 Science 7.83 7.90 -0.43 2333 281 0.66 0.05 0.16 7.72-7.93 7.58-8.21 Tech 8.57 8.00 3.72 1784 279 0.0002 0.06 0.15 8.46-8.68 7.72-8.29 11 Maths 5.44 4.89 3.59 2451 260 0.0003 0.05 0.14 5.35-5.54 4.62-5.16 Science 6.32 6.51 -1.30 2440 256 0.19 0.04 0.15 6.24-6.41 6.21-6.82 Tech 7.01 5.90 6.39 2413 253 0.0000 0.05 0.19 6.91-7.12 5.54-6.27 13 Maths 4.93 4.65 2.00 2372 279 0.046 0.05 0.14 4.80-5.02 4.40-4.90 Science 5.80 4.89 7.19 2301 259 0.0000 0.04 0.10 5.71-5.88 4.69-5.09 Tech 5.60 5.50 0.47 2123 179 0.64 0.06 0.19 5.49-5.72 5.12-5.89
Table 4. t-test (two-tailed, unequal variances) parameters for comparison of the learners’ results in Poland (PL) and nine other European countries (EU), α=0.05
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performed as proposed by Yusoff and Djauhari (2013). The covariance matrices for 8, 11 and 13 year olds and for the whole study data have been calculated and the hypothesis versus for has been tested. The exemplary re-sults for mathematics, collected in Table 3, support the hypothesis of a stable construct, despite different num-ber of responses in items for 8 and 11–13yo learners.
Following the procedure of analysis for Likert com-posite scale, described above, the mean scores have been calculated for Poland and for nine other European countries and presented in fig. 2. In order to compare the results for Poland and the rest, a series of t-test (two-tailed, unequal variances) has been carried out and the calculated parameters are listed in table 4.
The results reveal that for all three subject domains the European learners’ attitude decreases with age with the greatest drop between ages 8 and 11 (especially for mathematics, EU and PL, science in EU and technol-ogy in PL). The significant differences of means between two considered groups have been found for technology (8 and 11 year olds) and for mathematics and science at age 13 and in all those cases the means for Poland are lower than for the score of nine other countries. The power of those tests is found to be not less than 0.94, except for mathematics at age of 13 (P=0.48).
It must be noticed that in ten researched countries a variety of science and technology subjects is taught
across ages, so the results in fig. 2 show only a general tendency, averaged by the entire variety. More detailed elaboration of the outcomes with division on separate subjects should be executed, however it is out of the scope of this paper and will be presented elsewhere.
Sources of motivation toward MST subjects
Questionnaires for 8, 11 and 13 year olds contain three clusters of statements (table 5), each comprising three similar questions (Likert items) about sources of positive attitude towards each subject: mathematics, science and technology.
For 8yo learners yet again only three answers (‘yes’, ‘a bit’ and ‘no’) have been anticipated, whilst for 11 and 13 year olds a 4-point Likert scale has been attributed to each statement (‘completely disagree’, ‘disagree’, ‘agree’ and ‘completely agree’). Non-parametric analy-sis, namely logistic regression, has been chosen in or-der to elaborate the Likert items and enable comparison of different sources of motivation across countries and ages. Two exposure variables (age and learner dwelling
location) have been incorporated to the model and the outcome variables were statements about sources of lik-ing particular subject (items in table 5). No interaction between the exposure variables has been taken into ac-count. The parameters of logistic regression and odds ratios are presented in table 6.
Noticeably, the odds ratios for all outcome variables are similar, indicating that for every year the odd for liking MST subjects because of topics, activities and teachers decreases by 0.65-0.79. The influence of the dwelling location in majority of cases is not significant, but whenever it counts, the results show that opinion of Polish learners is lower than perception of pupils from other European countries. There is one exception though, namely science teachers’ impact on liking sci-ence, which in opinion of Polish responders is 1.206 higher than for the rest of the learners under study. The calculated probabilities of liking the subjects because of topics, activities and the teacher with distinction be-tween Poland and nine other European countries are presented in fig. 3.
Table 5. Three separate Likert items describing sources of motivation for school MST subjects, included in learners’ questionnaires
Questionnaire for 8, 11 and 13yo
1. I like the subject because of the topics we study. 2. I like the subject because of the things we do during the lessons.
3. I like the subject because of my teacher.
Table 6. Parameters of logistic regression for three separate Likert items (outcome variables) describing sources of motivation for school MST subjects and their dependence on two exposure variables: (1) age, (2) dwelling location
Logistic regression coefficients (coeff.), standard error (SE), p-value, odds ratio (O.R.) have been listed only if the effect of the exposure vari-able is statistically significant.
Subject Source Exposure variable 1 (age) Exposure variable 2 (dwelling location)
coeff. SE p O.R. coeff. SE p O.R.
Maths topics -0.435 0.013 0.0000 0.647 - - - -activities -0.426 0.014 0.0000 0.653 - - - -teacher -0.310 0.012 0.0000 0.7337 - - - -Science topics -0.276 0.015 0.0000 0.759 -0.252 0.089 0.046 0.771 activities -0.359 0.017 0.0000 0.699 - - - -teacher -0.242 0.013 0.0000 0.785 0.187 0.085 0.024 1.206 Tech topics -0.425 0.018 0.0000 0.654 -0.325 0.090 0.0006 0.723 activities -0.370 0.019 0.0000 0.691 -0.268 0.102 0.0085 0.766 teacher -0.341 0.014 0.0000 0.711 - - -
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In general it is visible that across Europe the impact of all three sources on a positive attitude towards all MST subjects decreases with age. Further differences can be detected between subjects. Across Europe the lowest impact of all sources is reported for mathemat-ics, but at the same time for this subject all three sources seem to have a very similar influence on learners’ posi-tive attitude. Moreover, for both, science and technolo-gy the lowest impact of a teacher on liking the subject is visible across all three ages in Europe. In all three MST subjects in both, Europe and Poland, it seems that in general across three ages the activities have got the big-gest influence on learners’ positive attitude towards any MST subject, except for mathematics at age 13 when the picture is mixed.
As comparing results in Poland and Europe, it can be noticed that the lines representing trends in influence of topics, activities and teachers on liking MST sub-jects hardly ever cross each other, as concerning both dwelling locations separately, except for two trend lines in mathematics representing teacher impact, which both slightly swing up at age 13. The conclusion can be that across Europe 13yo learners do not have much stimulation for positive attitude towards mathematics, but if they have it, the impact of teachers is stronger than at lower ages. At the same time when speaking about technology at age 13, the subject is liked by the learners but the teachers are those who make liking it much less.
Learners’ self-esteem and their opinion of easiness of MST subjects
As concerning their self-esteem in MST subjects and the opinions on easiness of MST at school, the learners have been asked two questions, listed in table 7.
Fig. 3. Sources of positive attitude towards mathematics, science and technology school subjects derived from learners’ questionnaires
8 yo 11yo 13yo 8 yo 11yo 13yo 8 yo 11yo 13yo
Questionnaire for 8, 11 and 13yo
1. I do well in the subject 2. The subject is easy for me
Table 7. Two items describing learners’ self-esteem and their opinion on the easiness of school MST subjects, derived from learners’ questionnaires.
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For 8yo learners only two answers (‘yes’ and ‘no’) have been anticipated, whilst for 11 and 13 year olds a 4-point Likert scale has been attributed to each state-ment. Since this is again the case of Likert-type items the logistic regression with two exposure variables
(age and dwelling location) has been utilized to elabo-rate the results. The model assumes no interaction between the exposure variables. The parameters of logistic regression and odds ratios are presented in table 8.
Noticeably, the odds ratios for all outcome variables are similar, indicating that for every year the odd for learners’ self-esteem and their opinion about easiness of MST subjects decreases by 0.8–0.91. The influence of the dwelling location in most cases is not significant, but whenever it counts, the results show that perception of Polish learners on easiness of the subjects (mathematics and technology) is stronger than opinion of pupils from nine other European countries. The calculated prob-abilities over the entire sample of learners, questioned in Poland vs. nine European countries are presented in fig. 4.
Table 8. Parameters of logistic regression for two separate Likert items (outcome variables) describing learners’ self-esteem and their opinion of the easiness of MST subjects, together with their dependence on two exposure variables: (1) age, (2) dwelling location
Logistic regression coefficients (coeff.), standard error (SE), p-value, odds ratio (O.R.) have been listed only if the effect of the exposure vari-able is statistically significant.
subject Exposure variable 1 (age) Exposure variable 2 (dwelling location)
coeff. SE p O.R. coeff. SE p O.R.
Maths esteem -0.215 0.014 0.0000 0.806 - - - -easiness -0.203 0.012 0.0000 0.816 0.171 0.078 0.030 1.186 Science esteem -0.091 0.015 0.0000 0.913 - - - -easiness -0.097 0.012 0.0000 0.908 - - - -Tech esteem -0.158 0.018 0.0000 0.854 - - - -easiness -0.091 0.014 0.0000 0.913 0.298 0.095 0.0017 1.346
Fig. 4. Learners’ self-esteem and their opinion on the easiness of mathematics, science and technology school subjects derived from learners’ questionnaires
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Decreasing trend with age is visible in average Eu-ropean results for both aspects correspondingly to the picture in fig. 2. It is evident that despite the subject and age, learners always perceive themselves as doing much better in the subject than assessing the subject easiness. However at least for mathematics and science it is vis-ible that the trend lines specific to the dwelling location for both items are almost parallel, suggesting a kind of dependence between both Likert-type items. Indeed, correlations between both statements have been found in most cases to be greater than 0.5 (see Table 9), in-dicating moderate interdependence. When comparing the results for Poland and Europe, it is noticeable that the relevant trends almost overlap, except for ‘easiness’ in mathematics and technology, where the Polish scores are distinctly higher than for the European average.
The question may be raised whether social desirabil-ity bias (Leite and Cooper, 2010, and reference therein) is present in our study or not, anticipating that younger learners could respond to the questionnaire in a more socially desirable way than their older colleagues. In our opinion such social desirability indicators could
be items ‘I like the subject because of my teacher’ and ‘I do well in the subject’. In order to address this issue we have performed a short analysis limited to mathemat-ics and EU on correlation between “overall attitude to-wards mathematics” and, separately, ‘I like mathematics because of my teacher’ and ‘I do well in mathematics’. Correlation coefficients are very low (see table 10) and they increase with age, so we conclude that we cannot find support for supposition about social desirability bias, however more profound analysis would be needed on that issue in order to draw more reliable conclusions.
Conclusions
The study presented a limited selection of the huge set of data collected in SECURE project, researching MST written curricula, their implementation in every-day practice and perception of 5, 8, 11 and 13yo learners and their MST teachers. In this paper we have focused selectively on (1) learners’ attitude towards MST school subjects, (2) influence of topics, activities and teach-ers on liking the MST subjects by pupils, (3) learnteach-ers’ self-esteem in MST subjects and (4) their opinion on the easiness of MST subjects. The main objective was to compare the research findings revealed on the
above-mentioned topics in Poland with the relevant averaged results in nine European countries.
The research findings show a decreasing tendency of all studied factors across ages and across all three MST subjects in Europe, with particularly great drop between ages 8 and 11 (mostly indicated in fig. 2). In general the similar relevant trends have been observed in Poland, however with a few exceptions. First of all in all three figures an unfavourable situation for science at age 13 can be detected. Learners’ attitude toward science subjects in Poland lowers drastically, unlike in Europe, where this negative trend is somehow subsided above the age of 11. Secondly, a quite an opposite situa-tion is observed in Poland exclusively for science, where especially the influence of a teacher shows abnormal be-haviour as compared to other European countries (see Table 6 and fig. 3). This result might be elucidated a little by the fact that by chance among the researched classes of ages 8 and 11 at least eleven took part in the National Science Contest, for Primary Schools, Firefly (Sokolows-ka, 2010), in addition to which at least ten out of all 87 all science teachers participated in inquiry-based science education workshops (Fibonacci project, FP7) prior to the year of study. This indication of a positive influence of such engagement on learners and teachers (as further revealed from teachers’ questionnaires as well) at least requires a special attention and more elaboration, as may become one of the most important evidences pro inquiry-based activities at school. The third unexpected feature is visible in fig. 4 and Table 8 for opinion on easi-ness of technology at age 13. The Polish results are sur-prisingly better that the scores in Europe. It is worth to mention here that first of all in case of technology at age of 13 the Polish statistics was a bit poorer comparing to all other MST subjects and ages, since in lower second-ary school this subject is not obligatory at all. As such it is probably treated more freely by the schools, teachers
Table 9. Correlation coefficients between answers to the questions from Table 7 about learners’ self-esteem in and easiness of MST subjects
Age Subject Correlation
EU PL 8 Maths 0.56 0.45 Science 0.53 0.38 Tech 0.52 0.42 11 Maths 0.43 0.41 Science 0.53 0.51 Tech 0.44 0.59 13 Maths 0.57 0.59 Science 0.61 0.65
Tech 0.57 0.70 Table 10. Correlation coefficients between Likert
composite scale on ‘attitude towards mathematics’ and two Likert-type items ‘I do well in mathematics’ and ‘I like mathematics because of my teacher’, chosen as indicators for social desirability bias
Age Subject
Correlation coefficient I like M because of
my teacher I do well in M
8 overall attitude toward Maths 0.14 0.29 11 0.28 0.39 13 0.24 0.41
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and learners, what seems to have a positive impact at least on the pupils’ perception of the subject.
To conclude the research reveals that except for having good reason to differ in a very few aspects, the affective domain factors in MST education show quite similar tendencies and relations in Poland and in nine other European countries under study. One of the most striking outcome from SECURE research is that despite various schooling systems, curricula and conditions shaped by cultures in different European countries, the overall trend is that general positive attitude and per-ception of MST subjects lowers with age, with a particu-larly huge drop observed between ages 8 and 11. This is in line with other findings, summarizing it as a “fourth grade slump” (see for example Williams 1976; Best, Floyd and McNamara, 2004; Sanacore and Palumbo, 2009; Kim, 2011) and attributing the phenomenon to reading comprehension abilities, development of vocab-ulary and learners’ engagement. In our opinion it can be further explained partially due to social transfer from being concentrated on oneself (self-oriented) at lower ages and becoming more socially-oriented when turn-ing 10, which is also connected to the development of abstract and critical thinking at that age (see for exam-ple Maex et al., 2010 and references therein). No matter what the origin of the phenomenon is, we can conclude that “fourth grade slump” is not addressed properly in the education systems across Europe, at least if concern-ing MST subjects.
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