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14
Section 3. Educational psychology
https://doi.org/10.29013/EJEAP-20-2-18-26
Ma Jiantang,
School: Sandy Spring Friends School, MD Email: jiantang.ma@ssfs.org
A GLOBAL PERSPECTIVE ON ADOLESCENTS' MOTIVATION IN SCIENCE STUDY AND HOW IT IS AFFECTED BY TEACHING METHODS
Abstract. Education of science and technology is critical in modern societies. However, over the past decades there is a growing gap between scientific and technical expertise offered by schools and the social demand in this regard. There are a lot of factors affecting science and technology education, one of which is teaching methods. There are difference in adolescents' motivation in science study across countries. The more critical thinking and creative the teaching methods involves, the higher motivated the adolescents are.
Keywords: global perspective, adolescents, motivation, science study, teaching methods.
1. Background operation and Development (OECD). It is conduct-
Education of science and technology is critical ed every three years to tests 15-year-old students in in modern societies. However, over the past decades reading, mathematics and science. PISA choose the
there is a growing gap between scientific and technical expertise offered by schools and the social demand in this regard. There are a lot of factors affecting science and technology education, one of which is teaching methods [1].
2. Study objectives
To assess adolescents' motivation in learning broad sciences using cross-national data, and examine how teaching methods affect it.
3. Materials and Methods
3.1. Data source and Study sample
This study used data from the 2015 Programme for International Student Assessment (PISA, website: http://www.oecd.org/pisa/) [2]. PISA is the survey of adolescent students around the world, conducted by the Organization for Economic Co-
age of 15 because it is believed that students at this age can decide whether or not they want to continue their education.
The 2015 data is the most recent available PISA data by the time of this study. It (http://www.oecd. org/pisa/data/2015database/) includes five main data files: a student-questionnaire data file, a school-questionnaire data file, a teacher-questionnaire data file, a cognitive item data file and a file with questionnaire timing data. We used the student-questionnaire data.
3.2. Variables
The data includes 928 variables in total. Below are list of variables used and variable coding:
3.2.1. outcome
The following "clusters" ofquestions were asked to students on their interest/perception in broad sciences:
How much do you disagree or agree with the statements about yourself below?
ST094Q01NA I have fun when I am learning <broad science>
ST094Q02NA I like reading about <broad science> topics.
ST094Q03NA I am happy working on <broad science> topics.
ST094Q04NA I enjoy acquiring new knowledge in <broad science>.
ST094Q05NA I am interested in learning about <broad science>.
Responses are:
1 Strongly disagree
2 Disagree
3 Agree
4 Strongly agree
How much do you agree with the statements below?
ST113Q01TA Making an effort in my <school science> subject(s) is worth it because this will help me in the work I want to do later
ST113Q02TA What I learn in my <school science> subject(s) is important for me because I need this for what I want to do later on
ST113Q03TA Studying my <school science> subject(s) is worthwhile for me because what I learn will improve my career prospects.
ST113Q04TA Many things I learn in my <school science> subject(s) will help me to get a job.
Responses are:
1 Strongly disagree
2 Disagree
3 Agree
4 Strongly agree
How often do you do these things?
ST146Q01TA Watch TV programs about <broad science>
ST146Q02TA Borrow or buy books on <broad science> topics
ST146Q03TA Visit web sites about <broad science> topics
ST146Q04TA Read <broad science> magazines or science articles in newspapers
ST146Q05TA Attend a <science club>
ST146Q06NA Simulate natural phenomena in computer programs\virtual labs
ST146Q07NA Simulate technical processes in computer programs\virtual labs
ST146Q08NA Visit web sites of ecology organisations
ST146Q09NA Follow news via blogs and microblogging
Responses are:
1 Very often
2 Regularly
3 Sometimes
4 Never or hardly ever
3.2.2. Teaching methods
This was based on the following question:
"When learning topics at school, how often do the following activities occur?"
ST098Q01TA Students are given opportunities to explain their ideas.
ST098Q02TA Students spend time in the laboratory doing practical experiments.
ST098Q03NA Students are required to argue about science questions.
ST098Q05TA Students are asked to draw conclusions from an experiment they have conducted.
ST098Q06TA The teacher explains <school science> idea can be applied
ST098Q07TA Students are allowed to design their own experiments.
ST098Q08NA There is a class debate about investigations.
ST098Q09TA The teacher clearly explains relevance <broad science> concepts to our lives.
ST098Q10NA Students are asked to do an investigation to test ideas.
Responses are:
1 In all lessons
2 In most lessons
3 In some lessons
4 Never or hardly ever
3.2.3. Other variables:
A score is created based on each question. A higher score indicates lower degree of critical thinking/creativity encouraged by the teaching methods.
CNTRYID_Country Identifier
This is a variable indicating the source country.
#ST001D01T Student International Grade (Derived)
#ST003D03T Student (Standardized) Birth -Year
#ST004D01T Student (Standardized) Gender
Variables of grade, age, and gender are based on the above questions.
ST005Q01TA What is the <highest level of schooling> completed by your mother?
ST007Q01TA What is the <highest level of schooling> completed by your father?
A variable indicating "parent's highest education" was created, based on the higher one of mother's or father's. Responses include: ISCED level 1, ISCED level 2, ISCED level 3B/3C, ISCED level 3A, ISCED level 4, ISCED level 5B, ISCED level 5A, ISCED level 6.
3.3. Data analysis
We first examined the components of adolescents' motivation in science study, by using principal components analysis (PCA). PCA is a popular statistical procedure in variable reduction. It converts a set of (possibly) correlated variables into a set of components. As a result, it is particularly useful in
situations where the dimensionality/component structure of data is to be studied [3].
Then, we looked at adolescents' motivation across countries, gender, and parental education.
Lastly, we used linear regression to evaluate if teaching methods are related with adolescents' motivation in science study. Linear regression is for studying continuous outcome variables, and its general formula is:
y = b0 + b1*X1 + b2*X2 + ... .+ bn*Xn The major output is regression coefficient (b) for all variables in the model, as well as their P-values. If a coefficient has a P-value of below 0.05, it means
that this variable has statistically significant association with the outcome. In this case, we further look at the direction of its regression coefficient (b). If b is above 0, the variable is positively associated with the outcome. If b is below 0, the variable is negatively associated with the outcome.
On the other hand, if the P-value is above 0.05, it means the association is not statistically significant.
In the model, we included age, gender, grade, parental education, and country. These are potentially important variables confounding the relationship between teaching methods and students' motivation. Therefore, by including them in the model, their effect is controlled for.
4. Results
There are 134,436 participants with complete information of the variables in section 3. They are from 40 countries.
Age Proportion
16 years 10%
17 years 90%
■ male "female Grade__Proportion
7 0.5%
8 3.4%
9 33.3%
10 50.7%
11 11.6%
12 0.5%
13 0.0%
Most students are in grade 9 and 10.
4.1. Correlation among all the motivation questions
Results showed that the questions within each "cluster" question are highly correlated.
PCA analysis showed that the 3-component structure works well. Questions within each "cluster" question are highly loaded on one of the 3 components.
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T094Q0 4NA r\l r^ o (M IN o ltl hO O o rH o OO o rn o lti rn o ll-l m o H rn o fM m O O m O Is-fn O rn rn O fM H o to H o Lrt H o H fM o IN rsl o
to
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to
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to
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RC1 RC2 RC3 h2 u2 com
ST094Q01NA 0.08 0.90 -0.01 0.77 0.23 1.0
ST094Q02NA 0.04 0.88 0.03 0.79 0.21 1.0
ST094Q03NA 0.00 0.89 -0.01 0.80 0.20 1.0
ST094Q04NA 0.05 0.93 0.01 0.82 0.18 1.0
ST094Q05NA 0.04 0.91 -0.03 0.82 0.18 1.0
ST113Q01TA 0.00 0.00 0.90 0.80 0.20 1.0
ST113Q02TA 0.01 -0.01 0.92 0.85 0.15 1.0
ST113Q03TA -0.01 -0.02 0.91 0.84 0.16 1.0
ST113Q04TA 0.01 0.02 0.88 0.78 0.22 1.0
ST146Q01TA 0.62 -0.17 -0.01 0.48 0.52 1.2
ST146Q02TA 0.77 -0.07 0.02 0.65 0.35 1.0
ST146Q03TA 0.70 -0.19 0.02 0.63 0.37 1.2
ST146Q04TA 0.73 -0.15 -0.01 0.63 0.37 1.1
ST146Q05TA 0.80 0.16 0.01 0.59 0.41 1.1
ST146Q06NA 0.88 0.14 0.00 0.71 0.29 1.1
ST146Q07NA 0.88 0.15 0.00 0.70 0.30 1.1
ST146Q08NA 0.84 0.05 -0.02 0.67 0.33 1.0
ST146Q09NA 0.76 -0.05 -0.01 0.60 0.40 1.0
Based on the "cluster" questions, we created the following three motivation component variables:
• interest = ST094Q01NA+ ST094Q02NA+ ST094Q03NA+ ST094Q04NA+ ST094Q-05NA
• importance = 20- (ST113Q01TA + ST113Q-02TA + ST113Q03TA + ST113Q04TA)
• daily_involvement = 45- (ST146Q01TA + ST146Q02TA + ST146Q03TA + ST146Q-
04TA + ST146Q05TA + ST146Q06NA + ST146Q07NA + ST146Q08NA + ST146Q-09NA)
they are created so that higher scores indicate higher motivation in each component.
4.2. Scores by gender, parents' highest education, and country
Motivation
interest importance daily involvement
female 13.78 11.65 14.33
male 14.1 11.66 16.18
Parent's highest education level Motivation
interest importance daily involvement
1 2 3 4
did not complete <ISCED level 1> 14.01 11.82 15.77
ISCED level 1 13.89 11.75 15.35
ISCED level 2 13.88 11.78 15.56
ISCED level 3B, 3C 13.21 11.12 14.41
ISCED level 3A 13.63 11.45 14.94
1 2 3 4
ISCED level 4 13.85 11.58 15.16
ISCED level 5B 13.86 11.62 14.89
ISCED level 5A 14.27 11.78 15.12
ISCED level 6 14.26 11.96 16.55
Comparison across 40 countries
country code country name # of participants score Ranking
interest importance daily involvement interest importance daily involvement
1 2 3 4 5 6 7 8 9
970 B-S-J-G (China) 7.007 14.66 12.37 16.81 9 7 9
858 Uruguay 1.882 13.07 11.53 14.92 32 22 21
840 United States 2.968 14.04 11.77 14.27 19 18 26
826 United Kingdom 5.739 14.04 12.1 13.43 18 12 34
788 Tunisia 1.665 15.29 12.9 20.9 1 1 1
784 United Arab Emirates 6.067 14.97 12.7 18.78 5 3 2
756 Switzerland 2.443 13.45 10.02 13.75 28 38 29
703 Slovak Republic 3.263 12.41 10.72 14.44 37 33 25
702 Singapore 4.070 15.21 12.4 15.05 2 6 16
643 Russian Federation 2.875 13.3 11.48 17.26 29 23 7
634 Qatar 4.354 14.72 12.59 18.65 7 4 3
620 Portugal 3.032 15.05 12.86 16.15 4 2 11
616 Poland 3.434 13.21 11.06 15.7 31 30 13
604 Peru 3.237 14.7 12.33 17.57 8 8 6
554 New Zealand 1.861 14.3 12.2 13.47 13 10 33
446 Macao 3.252 14.04 11.53 14.96 17 21 20
442 Luxembourg 2.202 13.73 10.68 14.55 25 34 24
440 Lithuania 3.308 14.32 11.94 15.72 12 16 12
428 Latvia 2.458 13.54 10.89 14.62 27 32 23
410 Korea 3.354 12.76 10.9 13.36 35 31 36
376 Israel 3.248 13.82 11.95 15.46 24 15 15
372 Ireland 2.736 14.18 12.02 12.63 16 14 39
352 Iceland 1.514 13.84 11.47 13.54 23 24 31
348 Hungary 2.546 12.26 10.34 15.05 40 36 18
344 Hong Kong 2.861 14.74 12.05 16.43 6 13 10
300 Greece 2.596 13.9 11.54 15.03 21 20 19
276 Germany 1.338 12.67 9.77 13.68 36 40 30
246 Finland 3.249 12.95 11.24 11.91 34 27 40
233 Estonia 3.265 13.89 11.34 15.05 22 26 17
214 Dominican Republic 1.144 15.08 12.45 18.42 3 5 4
203 Czech Republic 3.742 12.36 10.32 13.35 38 37 37
191 Croatia 3.018 12.96 11.15 14.08 33 28 28
188 Costa Rica 2.498 14.37 11.93 15.64 11 17 14
1 2 3 4 5 6 7 8 9
152 Chile 3.091 13.54 11.77 14.68 26 19 22
124 Canada 9.622 14.58 12.29 14.15 10 9 27
100 Bulgaria 2.344 14.19 11.13 18.23 15 29 5
76 Brazil 5.336 14.22 12.19 16.86 14 11 8
56 Belgium 4.487 13.28 10.53 13.51 30 35 32
40 Austria 2.968 12.31 9.8 13.39 39 39 35
36 Australia 4.362 13.92 11.4 13.24 20 25 38
Total 134.436
4.3. Results from Linear regression analysis
Coefficients: interest importance daily involvement
coefficient estimate Std. Error P-value coefficient estimate Std. Error P-value coefficient estimate Std. Error P-value
1 2 3 4 5 6 7 8 9 10
(Intercept) 10.88 0.70 <0.0001 12.98 0.57 <0.0001 20.03 1.04 <0.0001
age 0.10 0.04 0.02 -0.03 0.03 0.32 0.06 0.06 0.37
gendermale 0.30 0.02 <0.0001 -0.04 0.02 0.01 1.55 0.03 <0.0001
parental education 0.12 0.01 <0.0001 0.05 0.01 <0.0001 0.13 0.01 <0.0001
grade 0.32 0.02 <0.0001 0.11 0.01 <0.0001 -0.14 0.03 <0.0001
ST098Q01TA -0.20 0.01 <0.0001 -0.17 0.01 <0.0001 0.10 0.02 <0.0001
ST098Q02TA -0.04 0.02 0.02 -0.11 0.01 <0.0001 -0.47 0.02 <0.0001
ST098Q03NA -0.08 0.01 <0.0001 -0.08 0.01 <0.0001 -0.32 0.02 <0.0001
ST098Q05TA -0.14 0.01 <0.0001 -0.04 0.01 <0.0001 0.02 0.02 0.47
ST098Q06TA -0.38 0.01 <0.0001 -0.14 0.01 <0.0001 -0.03 0.02 0.11
ST098Q07TA 0.17 0.02 <0.0001 -0.02 0.01 0.06 -0.75 0.02 <0.0001
ST098Q08NA 0.07 0.02 <0.0001 0.01 0.01 0.31 -0.42 0.02 <0.0001
ST098Q09TA -0.56 0.01 <0.0001 -0.29 0.01 <0.0001 -0.17 0.02 <0.0001
ST098Q10NA -0.02 0.01 0.27 -0.12 0.01 <0.0001 -0.45 0.02 <0.0001
country ID: refer-ene 36
40 -1.01 0.09 <0.0001 -1.22 0.07 <0.0001 0.92 0.13 <0.0001
56 -0.19 0.08 0.01 -0.49 0.06 <0.0001 0.95 0.12 <0.0001
76 0.46 0.08 <0.0001 0.94 0.06 <0.0001 3.62 0.11 <0.0001
100 0.69 0.10 <0.0001 -0.06 0.08 0.47 4.72 0.14 <0.0001
124 0.57 0.07 <0.0001 0.86 0.06 <0.0001 0.78 0.10 <0.0001
152 -0.29 0.09 <0.0001 0.43 0.07 <0.0001 1.66 0.13 <0.0001
188 0.78 0.09 <0.0001 0.77 0.08 <0.0001 2.83 0.14 <0.0001
191 -0.31 0.09 <0.0001 0.15 0.07 0.04 1.30 0.13 <0.0001
203 -1.22 0.08 <0.0001 -0.85 0.07 <0.0001 0.51 0.12 <0.0001
1 2 3 4 5 6 7 8 9 10
214 0.84 0.12 <0.0001 0.75 0.10 <0.0001 4.03 0.18 <0.0001
233 0.51 0.09 <0.0001 0.28 0.07 <0.0001 2.14 0.13 <0.0001
246 -0.40 0.09 <0.0001 0.23 0.07 0.00 -0.52 0.13 <0.0001
276 -0.70 0.11 <0.0001 -1.30 0.09 <0.0001 0.64 0.17 <0.0001
300 0.31 0.09 <0.0001 0.40 0.07 <0.0001 2.37 0.13 <0.0001
344 1.38 0.09 <0.0001 0.89 0.07 <0.0001 2.97 0.13 <0.0001
348 -1.12 0.09 <0.0001 -0.68 0.08 <0.0001 2.41 0.14 <0.0001
352 0.14 0.11 0.19 0.25 0.09 0.01 0.67 0.16 <0.0001
372 0.50 0.09 <0.0001 0.78 0.07 <0.0001 -0.36 0.13 0.01
376 0.26 0.09 0.00 0.74 0.07 <0.0001 2.10 0.13 <0.0001
410 -0.49 0.08 <0.0001 0.01 0.07 0.92 1.04 0.12 <0.0001
428 0.00 0.09 0.98 -0.31 0.08 <0.0001 1.31 0.14 <0.0001
440 0.57 0.09 <0.0001 0.62 0.07 <0.0001 2.40 0.13 <0.0001
442 0.15 0.10 0.13 -0.53 0.08 <0.0001 1.34 0.14 <0.0001
446 0.80 0.09 <0.0001 0.54 0.07 <0.0001 2.13 0.13 <0.0001
554 0.04 0.10 0.71 0.69 0.08 <0.0001 0.50 0.15 <0.0001
604 0.50 0.08 <0.0001 0.62 0.07 <0.0001 3.10 0.12 <0.0001
616 -0.07 0.09 0.42 0.07 0.07 0.30 2.78 0.13 <0.0001
620 1.29 0.09 <0.0001 1.50 0.07 <0.0001 2.69 0.13 <0.0001
634 0.70 0.08 <0.0001 1.05 0.06 <0.0001 4.62 0.12 <0.0001
643 -0.53 0.09 <0.0001 0.02 0.07 0.77 2.98 0.13 <0.0001
702 1.53 0.08 <0.0001 1.18 0.07 <0.0001 2.04 0.12 <0.0001
703 -1.00 0.09 <0.0001 -0.32 0.07 <0.0001 1.89 0.13 <0.0001
756 -0.03 0.09 0.71 -1.16 0.08 <0.0001 0.34 0.14 0.01
784 0.91 0.07 <0.0001 1.15 0.06 <0.0001 4.77 0.11 <0.0001
788 1.45 0.11 <0.0001 1.45 0.09 <0.0001 6.85 0.16 <0.0001
826 0.04 0.08 0.57 0.73 0.06 <0.0001 0.79 0.11 <0.0001
840 0.15 0.09 0.08 0.30 0.07 <0.0001 0.61 0.13 <0.0001
858 -0.26 0.10 0.01 0.43 0.08 <0.0001 2.06 0.15 <0.0001
970 1.43 0.07 <0.0001 1.34 0.06 <0.0001 3.85 0.11 <0.0001
5. Interpretation of results
Firstly, we found that adolescents' motivation in science study consists of three components:
• Interest;
• perceived importance in future career;
• daily involvement.
Boys have higher scores than girls. This is not surprising.
Comparison across 40 countries: Tunisia ranks first in all three components. United States ranks 19, 18, and 26, respectively.
Overall, there's a trend that adolescents with higher parental education are more motivated in science study. Previous studies have found that parents' education predicts children's educational outcomes. One possible explanation is that parents' education background influences the ways in which they in-
teract with their children around learning activities both in the home and at school [4].
6. Conclusion
There are difference in adolescents' motivation
Results from linear regression analysis showed in science study across countries. The more critical
that if the teaching methods encourage critical, in- thinking and creative the teaching methods involves,
dependent thinking and creativity, then adolescents the higher motivated the adolescents are. are more likely to show motivation in science study.
1. Hasni A. Science, Student's Interest in Its, and Technology and Teaching, Relationships with Methods, Family Context and Self-Efficacy. Int. J. Environ. Sci. Educ. 10, 2015.- P. 337-366.
2. OECD. Programme for International Student Assessment (PISA) 2015 Database: Student Questionnaire data file, Organization for Economic Co-operation and Development. Paris, France (2016). Available at: URL: http://www.oecd.org/pisa/data/2015database
3. Wikipedia. Principal component analysis. Available at: URL: https://en.wikipedia.org/wiki/Principal_ component_analysis.
4. Eccles J. S. Influences ofparents' education on their children's educational attainments: the role ofparent and child perceptions. London Rev. Educ. 3, 2005.- P. 191-204.
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