JUNIOR STUDENTS’ INTEREST IN SCIENCE COURSES BASED ON HOTS LITERACY PROGRAM: MODELING OF SCIENTIFIC INTEREST AND ITS RELATIONSHIPS WITH OTHER COMPONENTSWawan Bunawan 1, Syamsul Gultom 2, Rismawati 3,
Efa Kristina 3, Fidya Witria’
Ash Suci 4, Anggi Anggana
Josephine 4 1 Physics Education Universitas Negeri Medan, Medan, Indonesia.2 Sport Science Universitas Negeri Medan, Indonesia.3 SMPN 14 Medan, Indonesia.4 Science Education Universitas Negeri Medan, Indonesia. |
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Received 15 October 2021 Accepted 05 November 2021 Published 17 December 2021 Corresponding Author Wawan
Bunawan, wawanbunawan@unimed.ac.id DOI 10.29121/granthaalayah.v9.i11.2021.4409 Funding:
This
research received no specific grant from any funding agency in the public,
commercial, or not-for-profit sectors. Copyright:
© 2021
The Author(s). This is an open access article distributed under the terms of
the Creative Commons Attribution License, which permits unrestricted use, distribution,
and reproduction in any medium, provided the original author and source are
credited. |
ABSTRACT |
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This
study aims to describe the pattern of relationships between several factors
supporting interest (interest) in science subjects. Students get an Exercise
Program to improve scientific literacy. This training program aims to improve
students' scientific literacy skills. The training program is carried out
after the teachers receive the training program. The study of students'
interest in science was revealed by conducting a survey related to several
factors supporting interest in science. Several factors in the study of
supporting scientific interests include the role of teachers, parental
support, school roles, peer support, physics, chemistry and biology
materials. Research participants were taken using random sampling technique,
totaling 39 students. The developed scientific literacy assistance program
seeks to improve the quality of learning outcomes. The success of the
scientific literacy training program is related to students' scientific
interest. The results showed that the multiple linear regression model
provides a descriptive numerical description of the empirical relationship
between the value of interest in science and its supporting components.
Likewise, interest has a role in learning achievement. |
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Keywords: Modelling,
Scientific Interest, Science 1. INTRODUCTION The implementation of
the Literacy Hots Assistance initiated by the Ministry and several LPTKs in
Indonesia currently aims to develop three literacy skills, namely scientific,
mathematical, and language literacy. In the field, especially SMPN 2, 3, and
14 Medan City is a pilot project of the implementation. Activities ranging
from pre-test and post-test to get a picture about the rate Higher Order
Thinking Skills (HOTS) literacy competencies have been carried out. In
addition, the assistance in the implementation of learning and development of
learning tools is also carried out by teachers in the field of hots
literacy-based learning for students of Science Education students during
Training of Trainer (ToT) activities. From the series of activities show the
stage in ways of achieving learning outcomes to the effect of the cognitive
and skills fields of students, teachers, students, and lecturers. However,
one important aspect to fill the empty space is an activity, namely the
interest aspect as requested by the OECD (2015).
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Examining the important role of the interest
aspect is as an aspect of driving academic activities that is very essential to
support the success of activities on the other.
The OECD (2015) mandates the attitude field as an important
part of the PISA program to focus on four aspects, namely: interest in science
(1), valuing scientific approaches to inquiry (3) and environmental awareness
(4). This attitude field directly supports competence in the field of
scientific literacy as shown in Figure 1.
|
Figure 1 Assessment framework based on PISA 2015 OECD
(2015) |
The
latest conception of scientific literacy is built through many emphasizing
aspects. In this sense, scientific literacy includes cognitive as well as
motivational and aspects related to domain-specific competency values Marshall et al. (2002), Millar (2006). Knowledge and attitudes are seen
as relevant components of scientific literacy within the framework of the
Program for International Student Assessment (PISA) 2015. While interest in
science as one aspect of scientific literacy attitudes becomes the focus of
this research. In line with what was conveyed by the OECD (2006), evaluating the important role of interest
in science and technology for the younger generation. The data shows a decline
in interest and attention to science and technology. Consequently, this becomes
an obstacle for the economic development and the nation progress OECD (2006).
The
mentoring program of HOTS literacy learning (science, numeric, language) was
already completed during three months. During the period, the students and
teachers experienced a series of cognitive assessment activities; the pretest,
the assessment process and the posttest carried out by the National Testing
Institute. The success of the mentoring program has not involved an assessment
of the participants' specific interests and interests in the field of science.
Students' interest in science certainly has a very important role in the
present and future for students, schools and the wider community Lavonen et al. (2021).
The
research focuses on describing the support of scientific interest in the
components of learning outcomes. Next, describe mathematically the supporting
factors of interest based on several supporting components. The research
questions are aimed at providing an overview of the main problem:
1)
How
is the interaction model between scientific literacy interest in subject
matters and its content knowledge?
2)
How
is the interaction model between students' scientific literacy interest, school
and family background?
3)
How
is the interaction model of the relationship between science interest and
learning outcomes?
2. LITERATURE REVIEW AND METHODS OF
RESEARCH
2.1. THE ROLE OF SCHOOL TOWARD STUDENTS' SCIENCE INTEREST
Basl (2011) has conducted data analysis based
on PISA 2006 which states that students' interest in science and technology impacts
on the future and for future careers. The achievement of student scientific
literacy is closely related to school background like in several countries,
including the Czech Republic, Germany, Finland and Norway. This fact shows how
important the role of schools in preparing the future of students' educational
programs, and the career opportunities they will pursue. Regression model
analysis was applied to analyze the data. Based on the research results, the
influence of parents is almost negligible in relation to students' interest in
science and technology.
The
school's role has an impact on students' interest in conducting experiments and
designing research. Students' interest in knowledge, values, feelings,
motivation, and self-esteem will shape an individual's personal view of a
particular subject Kind et al. (2007), Van et al. (2012). Science interest can be described
in terms of three components: cognitive, affective and behavioral components Eagly and Chaiken (1993). For example, a person's interest
in science would involve his knowledge of something, how he feels about
science, and how a person is willing to act in a certain way toward science (eg
taking a science course, or being a member of a science club).
2.2. SCIENCE COMPETENCY DOMAIN
According
to Renninger et al (2015), the way to find the answers from questions or
problems is an appropriate activity to support the development of scientific
interest than simply to receive the information. The use of scientific inquiry
through asking questions or providing solutions by modeling (Inkinen et al.,
2020), can contribute to the development of scientific interest. In addition,
the lack of understanding of scientific competence in general (Anderson, 2007;
Minner et al., 2010), makes it difficult to analyze and compare research
results pertaining to interest in the context of inquiry-based science learning
studies.
The
use of scientific inquiry is important in learning science in schools. Krajcik
and Czerniak (2013) argue that students cannot learn scientific content
disciplines alone without engaging in scientific practice and actively building
their understanding through working and using ideas in real-world contexts.
Scientific inquiry can practically be interpreted as the work of a scientist or
an expert in a scientific discipline. A series of inquiries involves asking
questions, planning and conducting investigations, analyzing and interpreting
data, developing explanations and building models based on the data. Scientific
practicum is not the same as inquiry, nor does it replace questions. Scientific
practicum and scientific inquiry are a combination of activities in teaching
and learning situations carried out in the classroom Miller
et al. (2018).
2.3. COMPONENTS OF INTEREST IN SCIENCE BASED ON SCIENTIFIC ATTITUDE
Aalderen-Smeets et
al. (2012) built a framework for defining
attitudes towards science based on the context of primary school teachers. The
framework was adapted from the traditional “tripatrid attitude model” Eagly and Chaiken (1993) by adding a new main category as
control, and sub categories of self-efficacy and context dependence (Figure 2).
A
review of attitudes shows that a person's cognitive, attitudes, behaviors and
beliefs can be successful in the term of performing certain tasks
(self-efficacy, Bandura 1997). Likewise, the learning context such as the
availability of teaching materials and time allocation also play a role in the
construction of attitudes towards science teaching.
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Figure 2 Attitudes towards science learning Aalderen-Smeets et al. (2012) |
The
concept of interest is used in various fields of educational research ranging
from psychology, educational psychology, sociology, science and technology
education Krapp
and Prenzel (2011). The current study focuses on
interest science, the focus is on understanding phenomena related to science
and technology and making a few empirical contributions to the theory of
interest in science.
The
conceptual framework is applied to the interest tools to determine the main
dimensions and indicators that will be used to develop data collection
instrument that refers to Renninger
and Hidi (2011)’s instrument. Such instrument is
needed to emphasize the importance of the construction aspect and to measure
interest and its indicators as the basis for its operations. The synthesis
published by the authors Renninger
and Hidi (2011) clearly shows that there is no
stable and fully agreed theoretical orientation towards the concept of
interest. However, mutual agreement can be found in regarding the central
characteristics of the construct of interest Krapp and
Prenzel (2011).
Three
sets of characteristics can be found in various references and will be used as
the basis for operationalizing the concepts in this study. The characteristics
of interest include: a) attributes of the concept of interest; b) the
dimensions that make up the construction of interest; and c) the analytical
level used. Silvia (2001) describes theoretically how
students' interest is formed as shown in Figure 3 Interest is formed on the basis of
previous activities including the internalization of values, transformation
processes, and other variables. These three main components form an emotional experience
that builds interest and continues to develop according to experience.
|
Figure 3 Construction theory of interest
development Silvia
(2001) |
2.4. CHARACTERISTICS OF THE CONCEPT OF INTEREST
Based
on Gardner (1996, in Krapp (2007)) and other authors (Hidi, Renninger
& Krapp, 2004; Krapp (2007), Krapp
and Prenzel (2011),Renninger and Hidi
(2011) consider that the criteria determining
the construct of interest with the concept of attitude and motivation lies in
the specificity of its content. The concept of interest has a close
relationship with attitude and motivation, but differs in the term of something
specific only the interest part.
One cannot simply have an interest, one must
be interested in something (Gardner, 1996, in Krapp (2007)). The construct of interest is
conceptualized as a relational concept. Interest represents or describes a
specific relationship that will last a long time between a person and an object
in his or her life space (Krapp (2007)). Objects of interest in the field
of science and technology can be in the form of subjects (biology, physics,
chemistry, etc.), certain fields of study (study of energy conservation),
operations or concrete objects (laboratory manipulation), and scientific
research activities (formulating problems or questions). scientific research,
or analyzing data) (Häussler, 1987; Häussler & Hofmann, 2000; Krapp (2007), Krapp
and Prenzel (2011). When discussing science and
technology as objects of interest, it is also important to distinguish the way
science and technology is perceived in society (outside of school), science is
taught and learned in the school context.
2.5. STUDY OF GENDER GROUP DIFFERENCES ON SCIENTIFIC INTEREST
The
difference between the decline in interest in girls and boys is not well
understood. For very young male students aged 4-7 years, empirical data
revealed a significant decrease, but not a significant decrease for girls.” Alexander
et al. (2012). Another study reported that the
gender factor did not decrease inters among Chinese students related to
chemistry lessons Cheung (2007).
In other
contexts, such as in the UK, a decline in interest in science in general has
been reported, this decline being more pronounced for female students (Barmby
et al., 2008). Another study in England among students from years 9 to 11 found
a similar trend Francis and Greer (2001). Most of the articles dealing with
this issue report very small or insignificant differences in the interest of
boys and girls in science and technology. When general differences were
reported for the primary school level, it was mostly favorable for boys with a
few exceptions Potvin and Hasni (2014).
2.6. RESEARCH METHODOLOGY
The
HOTS literacy learning mentoring program is implemented in collaboration
between higher education institutions, 3 schools and from government agencies.
Representatives from the University involved 9 lecturers from the Department of
Science, Mathematics, Biology, Physics and Language Education. The number of
students participating in this program are 18 students and 18 teachers as well
as 120 junior high school students.
The
mentoring program is carried out through joint planning in the form of
workshops to provide learning tools, each candidate works together to produce
training program materials including instructional design, teaching materials,
assessment systems, learning media and components of science laboratory
practicums.
The
implementation stage is carried out based on the documents that have been
produced previously. Students are guided to carry out HOTS literacy learning,
carry out the process of scientific inquiry in the laboratory. Students
intensively learn to know science more deeply, inspire thoughts, actions, and
attitudes how scientists work. Based on observations, there were changes in
students such as seriousness, integrity, and patterns of carrying out work
based on science inquiry. The assessment system is developed at an early stage
through pre-test, process assessment (rubric), and product assessment or
posttest. The role of students' scientific interest is measured towards the end
of the activity (Likert scale) and during the process for context interest by
observation and interviews (personal and situational interest).
3. RESULT AND DISCUSSION
3.1. INSTRUMENT OF INTEREST SCIENCE
Instrument
interest in science involves 20 questions with 5 components of interest
(factors of teachers, friends, family, school, and environment). Instrument
reliability was calculated using the Cronbach Alpha formula with an index of
0.70 in Table 1 and Table 2. The instrument was developed using
a Likert scale (5 intervals). The reliability index of the scientific interest
instrument of 0.70 meets a reliable instrument. The average of the scientific
interest instrument items is 3.77 with a variance of 0.20.
Table 1 Instrument Reliability |
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Cronbach’s Alpha |
Cronbach's Alpha Based on Standardized Items |
N of Items |
.70 |
.69 |
20 |
Table 2 Descriptive instrument of Interest Science |
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|
Mean |
Minimum |
Maximum |
Range |
Maximum / Minimum |
Variance |
N of Items |
Item Means |
3.767 |
2.949 |
4.308 |
1.359 |
1.461 |
.200 |
20 |
Item Variances |
.423 |
.167 |
.993 |
.826 |
5.935 |
.043 |
20 |
3.2. INTEREST RELATED TO THE FORMING FACTOR
The
student interest factor in science learning after receiving the mentoring
program can be described in Table 3. The average scientific interest
score is 70.02 with a standard deviation of 4.72. The number of students who
responded amounted to 39 students. The science interest component that is
calculated based on family background has an average that is not much different
from the school factor. The biggest student interest in science comes from the
teacher factor, this is in accordance with the intrinsic motivation and
aspirations of students after receiving treatment Narangodaa
et al. (2021), Lavonen et al. (2021). The lowest factor in building
students' interest in science comes from factors around students' lives.
Table 3 Descriptive Data of Interest |
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Component |
Means |
Std. Deviation |
N |
Interest |
70.02 |
4.72 |
39 |
Family |
14.58 |
1.09 |
39 |
Friends |
11.4103 |
1.69702 |
39 |
Teacher |
15.7436 |
1.84559 |
39 |
Around |
7.7692 |
1.15762 |
39 |
School |
14.0769 |
2.47498 |
39 |
Table 4 Correlation between Component of Interest |
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Pearson Corr |
Interest |
Family |
Friends |
Teacher |
Around |
School |
Interest |
1.00 |
0.29 |
0.43 |
0.67 |
0.67 |
0.65 |
Sig.(1-tailed) N |
|
0.03 39 |
0.00 39 |
0.00 39 |
0.00 39 |
0.00 39 |
The
relationship between the student's scientific interest factor and the
supporting components that make up the interest can be expressed by the
correlation between the components as shown in Table 4. The student's family factor in
building interest (r = 0.29, sig. 0.03 < 0.05) significantly supports
students' interests. The teacher factor has the biggest role in building
student interest as well as the learning environment provided by the school (r
= 0.67, sig. 0.00 < 0.05). Factors from schools in building student interest
have a very strong impact such as teacher factors and the learning environment
(r = 0.65, sig. 0.00 < 0.05). the student community in student life plays an
active role in supporting science interest with a fairly good correlation (r =
0.43, sig. 0.00 < 0.05). These four factors outside the family have a high
correlation in shaping students' interest in science, with good family support,
of course, students' interest in learning science will be higher.
Table 5 Anovab model of regression interest function |
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Model |
Sum of Squares |
Df |
Mean Square |
F |
Sig. |
1.
Regression Residual Total |
829.08 17.89 846.97 |
5 33 38 |
165.82 0.54 |
305.77 |
0.00a |
a. Predictors:
(constant), School (x1), Friends (x2), Family (x3), Teacher (x4), Around (x5) b. Dependent Variable: Interest (y) |
Table 6 Coefficients; dependent variable: interest |
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Model |
Unstandardized Coefficients |
Standardized
coefficients |
T |
Sig |
|
|
B |
Std error |
Beta |
|
|
1 (Constant) |
6.07 |
2.06 |
|
2.94 |
0.01 |
Family |
1.02 |
0.114 |
0.23 |
8.98 |
0.00 |
Friends |
1.03 |
0.07 |
0.37 |
14.50 |
0.00 |
Teacher |
1.08 |
0.07 |
0.42 |
14.75 |
0.00 |
Around |
0.91 |
0.12 |
0.22 |
7.43 |
0.00 |
School |
0.93 |
0.05 |
0.49 |
18.08 |
0.00 |
Based
on Table 5. and Table 6 can be built a mathematical model
that describes the prediction through the regression equation (1). The equation
has met the multiple linear regression model with a significance of 0.00 <
0.05 with all coefficients meeting a significance of 0.00 < 0.05 (Table 6).
Based
on Table 7 it can be concluded that the
regression model equation (1) has a very high determination of 0.98. This shows
that students' interest in science is strongly influenced by factors such as
teachers, schools, friends, environment and family, which are very dominant in
generating student interest in science lessons after completing the HOTS
literacy mentoring program.
Table 7 Model summary of regression interest |
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R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
Change Statistics |
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|
|
|
|
R Square Change |
F Change |
df1 |
df2 |
Sig.F
change |
Durbin-Watson |
.989a |
.979 |
.976 |
.73641 |
.979 |
305.767 |
5 |
33 |
.000 |
2.051 |
a. Predictors: constant, school, friends, family, teacher,
around b. Dependent variable: interest |
3.3. INTEREST RELATED TO CONTENT OF SCIENCE
From
the Table 8, it can be seen that the regression
has a sum of square of 295.32 and a df of 4 and the mean square has a value of
73.83 and an F value of 5.03 and a sig of 0.003 <0.05. For residuals, the
sum of squares has a value of 499.35 and df has a value of 34 and the mean
square has a value of 14.68. So, it has a total value of 794.66 and a df of 38.
It can be concluded that it meets the linear regression model in equation (2).
Table 8 Anovab Interest Related to Content of Science |
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Model |
Sum of Square |
Df |
Mean Square |
F |
Sig |
Regression |
295.32 |
4 |
73.83 |
5.03 |
0.003a |
Residual |
499.35 |
34 |
14.68 |
|
|
Total |
794.66 |
38 |
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a. Predictors: constant, Biology (x1), Interest
(x4), Chemistry (x2), Physics (x3) b. Dependent
Variable: Science (y) |
Table 9 Coefficients of predictor and dependent variable (science score) |
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Model |
|
|
Standardized Coefficients |
t |
Sig |
|
B |
Std.error |
Beta |
|
|
Constant |
29.557 |
14.206 |
|
2.081 |
.045 |
Interest |
.037 |
.137 |
.038 |
.271 |
.788 |
Physics |
.365 |
.224 |
.358 |
1.629 |
.113 |
Chemistry |
.133 |
.185 |
.149 |
.721 |
.476 |
Biology |
.146 |
.174 |
.161 |
.838 |
.408 |
Based
on Table 9, equation (2) can be generated. The
formula provides predictions for the acquisition of science lesson scores ( ) based on the predictors variables: constant,
Biology (x1), Chemistry (x2), Physics (x3) and
interest (x4). The model is in accordance with Alhadabi's, the
results highlighted science interest and science utility positively
influencing science achievement through a sequential pathway of
mediators, including science self-efficacy and science identity Alhadabi (2021). The coefficient is not significant
in building a predictive equation model, it can be happened because student
interest varies based on the difficulty of the subject matter faced.
4. CONCLUSIONS AND RECOMMENDATIONS
Students'
interest in science can be built based on the factors of teachers, schools,
students' environment, friends and family. Multiple regression equations can
describe mathematically each component supporting interest with coefficients
that meet the requirements of statistical testing at a significance level of
0.05.
The
learning achievement score for science subjects can be described by a
regression equation from the supporting factors, including physics, biology,
chemistry and inters science student scores. The coefficient of the regression
model does not meet the test requirements statistically, even though the
regression model meets the test requirements. This can happen because the score
of science subjects is dynamic following the rhythm of changes in the scores of
the supporting lessons, and is strongly influenced by the level of difficulty
of the subject matter of each sub-material studied by students.
ACKNOWLEDGMENT
Acknowledgement is conveyed to the Indonesian Ministry of Education, Culture, Research and Technology through the LPTK 2021 revitalization program, UPI Bandung, through the UNIMED Research and Community Service Institute (LPPM) which has provided research funding support. Research policies are carried out by university leaders, namely the rector, head of research and service institutions, deans, heads of departments. Then, thanks were also conveyed to all fellow lecturers and teachers through the mechanism for proposing proposals, evaluating proposals, and determining the feasibility of proposals by reviewers from both internal and external UNIMED.
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