Data Literacy of Prospective Physics Teacher Students in STEM

Learning

Eko Sujarwanto

Physics Education Department, Siliwangi University, Siliwangi Street 24, Indonesia

Keywords: Data Literacy, Prospective Physics Teacher, STEM Learning.

Abstract: The solution to a problem, among others, requires identifying and collecting, processing, and interpreting

data. This process involves data literacy skills. Data literacy is one of the hidden variables in the physics

learning process, and only a few are analysed. This study aimed to assess the data literacy possessed by

prospective physics teacher students. The research uses a case study research design. The scope of the research

is on prospective physics teacher students taking Electromagnetic courses at the Physics Education

Department, Siliwangi University, in the 2022/2023 academic year. Data analysis used descriptive statistical

analysis. Data Literacy in this research consists of data introduction, data collection and recording, data

analysis and interpretation, data communication, and data use. The context of this data literacy study is the

search for a solution to the problem of contamination of combustion smoke in the chimney by applying the

concept of physics. The findings are that students' data literacy is still at the primary and intermediate data

literacy level. The remarkable thing that needs attention is data literacy in the aspects of data recognition, data

interpretation, data collection, and data use. These results mean that students still need to be more to identify

data according to the problem, categorize data according to its use to solve problems, make connections

between the results of data analysis, and use data to provide arguments. The findings of this study can be the

basis for developing a learning program that involves data literacy training.

1 INTRODUCTION

Data literacy is a hidden variable/hidden curriculum

in physics learning. Science process skills may have

become a general topic/study in Physics learning.

Data literacy also intersects with Science Process

Skills, especially regarding data collection, analysis,

visualization, and interpretation. Data literacy

requires meaning and the impact of using data on

people’s lives, both from natural and social

knowledge. It is the meaning and use of data that

differentiate Data Literacy and Science Process

Skills. Data literacy must be identified as a hidden

variable/hidden curriculum at the university student

level.

Identification of data literacy at the university

student level needs to be done because students are

expected to be able to solve problems, think critically,

and make decisions based on valid and reliable data.

These abilities need to be possessed in an

interdisciplinary context and able to be applied in the

https://orcid.org/0000-0003-2535-3112

context of social life. This data literacy is helpful as a

basis for building a data-based society. Recognizing

misinformation and disinformation becomes more

complicated if the data literacy level of students is

unknown, especially during a post-pandemic

situation(Schreiter et al., 2022). In addition, data

literacy can also be the basis for other literacy, i.e.,

scientific literacy. Students with data literacy skills

will have more potential to understand data patterns

presented in scientific studies and be able to predict

and utilize scientific knowledge. Thus, it is necessary

to do research related to student data literacy.

Data literacy research is more commonly carried

out in studies of Computer Science and Informatics,

Mass Media, Environmental Science, and Library

Metadata(Wolff, Gooch, Montaner, Rashid, &

Kortuem, 2016, Wolff, Wermelinger, & Petre, 2019,

Pangrazio & Sefton-green, 2020, Deahl, 2014). Wolff

researched data literacy to uncover learning

principles that train data literacy. Wolff uses the

context of the position of solar panels in each house

Sujarwanto, E.

Data Literacy of Prospective Physics Teacher Students in STEM Learning.

DOI: 10.5220/0012199800003738

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 4th International Conference on Innovation in Education (ICoIE 4 2022) - Digital Era Education After the Pandemic, pages 287-291

ISBN: 978-989-758-669-9; ISSN: 2975-9676

287

in a residential area on energy consumption. The

actual results from Wolff are the principles of data

literacy learning, including learning that needs to

involve complex data, interesting teaching materials,

and STEM learning situations. Research by

Mandinach & Gummer (2013) and Schildkamp, Lai,

& Earl (2013) states that good data literacy helps

educators plan and implement learning. Gasevic,

Dawson, & Siemens (2015) added that if used

effectively and ethically, data is an essential part of

education, for example, in giving feedback.

Pangrazio & Sefton-green (2020) and Bhargava et al.

(2015) also emphasized that data literacy will impact

technological innovation and positive societal

changes.

Research on data literacy in physics is still less

significant than in metadata and informatics. In

addition to these gaps, data literacy in the context of

STEM education, especially at the university student

level, has yet to be explored too much. This research

will enrich data literacy research in education,

especially physics education in the STEM context.

The STEM context in this study was that students

were asked to provide alternative solutions with

physics concepts for air pollution from combustion

chimneys. Students are asked to provide a solution so

that the solid particles in the smoke are not released

freely with the smoke gas phase. After getting a

potential solution, students are asked to expand the

effect of using data on society.

This study aims to reveal the data literacy of

prospective physics teacher students. This research

will see to what extent physics education students

utilize the concepts and laws of physics to solve

problems in the context of air pollution from

chimneys. Knowing the position of university

students’ data literacy can help design learning

programs, media, teaching materials, or learning

models that support data literacy.

2 METHOD

These case studies reveal the data literacy of physics

teacher candidates at the Department of Physics

Education, Siliwangi University, who are taking the

Electromagnetics course for the 2022/2023 academic

year. The case study method was chosen because it

wanted in-depth data about data literacy for a certain

period. This research can be called a one-shot case

study because the timeframe is only two weeks and

only one problem context. Case studies are also

helpful when the group being studied has a different

context from others' research. The context of this

research is the problem of air pollution originating

from chimneys. Students are asked to provide a

solution so that the solid particles in the smoke do not

spread freely along with the smoke gas phase. The

collection uses task assignments related to the

problems given earlier. In addition, questions and

answers were conducted regarding the problem-

solving process. Data analysis used descriptive

qualitative, which was carried out on the problem-

solving process by students.

3 RESULT AND DISCUSSION

This case study has studied the data literacy of

prospective physics teacher students at Siliwangi

University. The prospective physics teacher students

in this case study emphasized that the physics

concepts learned can and must be used to solve

problems. From the problems given to students, they

initially worked in groups to brainstorm about

separating the solid and gas phases in the smoke

coming out of the chimney. Then the students do

independent work to solve problems through task

assignments.

Student data literacy is seen in Data Recognition,

Data Collection and Recording, Data Analysis and

Interpretation, Data Communication, and Data Use(

Sujarwanto, Madlazim, & Ibrahim, 2022). The

remarkable thing that needs attention in this case

study is data literacy in the aspects of data

recognition, data interpretation, and data use. Data

literacy owned by students has varying levels for each

component.

Data recognition is crucial before going further in

problem-solving. Data recognition is also a starting

point for data literacy. The introduction phase is

essential in problem-solving(Sujarwanto, Hidayat, &

Wartono, 2014; Fakcharoenphol, Morphew, &

Mestre, 2015; Good, Marshman, Yerushalmi, &

Singh, 2018; Good, Marshman, Yerushalmi, &

Singh, 2020) and in data literacy (Wolff et al., 2019;

Gibson & Mourad, 2018). Students, in this case, study

have a Data Introduction level at the Intermediate

level. It is characterized by being able to predict the

variation of available data, being able to identify

appropriate data for solving problems, but not being

specific about the type of data, for example, related to

specific pollutant sources of Ozone, SO

, or CO.

Data introduction by students is shown in Figure 1.

ICoIE 4 2022 - The Fourth International Conference on Innovation in Education

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(a)

(b)

(c)

Figure 1: Data Recognition by Students.

Figure 1 shows students can predict and identify

the data needed to solve problems. Students propose

the data about environmental air quality data around

settlements, sources of air pollution (Figure 1a),

chemicals in smoke, and the distance of pollution

sources from settlements (Figure 1b and Figure 1c).

Data collection is characterized by knowing how

to use tools and technology to collect, store, integrate,

manage, and check the correctness of data. From a

series of these characteristics, aspects of data

collection owned by students are at the primary level.

It was indicated by mentioning the tools and their

functions used to obtain data sources but did not

specifically mention the working principles of the

tools. Figure 2 shows the data collection aspect of the

task assignment. Students have been able to name

tools that support data collection to solve chimney air

pollution problems, for example, opacity meters to

measure smoke density (Figure 2a), utilizing sensors

on smartphones and impingers (Figure 2b), and a

spectrophotometer (Figure 2c).

(a)

(b)

(c)

Figure 2: Description of Data Collection by Students.

Data interpretation is a challenging aspect of data

literacy(Glazer, 2011;Edwards et al., 2017). Incorrect

interpretation of data can lead to errors in decision-

making. Interpretation of data owned by students is

still at the primary level. The characteristics shown at

the basic level of interpretation are that the majority

have not been able to interpret in-depth but only

explain/re-describe the data obtained and cannot

provide models or compare analysis results. The

results of data interpretation are shown in Figure 3.

(a)

(b)

Figure 3: Data Interpretation by Students.

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The use of data is not only limited to scientific

inquiry and giving arguments but also connecting

data with scientific or social issues and knowing the

impact of using data on society(Gibson & Mourad,

2018; Sujarwanto et al., 2022). In the aspect of data

usage, students are at the intermediate level. It was

marked by being able to describe data presentations

(tables and graphs), providing/suggesting problem-

solving using physics concepts, relating to science but

not yet to social utilities, and the solutions put

forward by the majority of students using a

centrifugal and electrostatic deposition. The results of

using data from students are shown in Figure 4.

(a)

(b)

Figure 4: Description of Data Usage by Students.

Recognizing data and its sources is an essential

aspect of data literacy. Students are still at the primary

and intermediate levels when data literacy is

associated with the chimney problem project because

the problem is less contextual. This results because

students’ prior knowledge needs to be improved to

carry out the process. Thus, if a learning process aims

to increase data literacy, the data context must be

close to students. It follows the suggestion by Wolff

et al. (2019) and Sujarwanto et al. (2022). In

sustainable development, data literacy results in

pollution through chimneys are supported by the

research of Ridwan, Kaniawati, Suhandi, Samsudin,

& Rizal (2020). The research stated that students are

aware of sustainable development but are less

effective in practical skills related to sustainable

development.

Interest affects the components of data usage.

Because the student’s background is physics and

science, students associate the use of data and

problem solutions with the science field, namely

environmental pollution, the greenhouse effect, and

health problems. Nothing has yet been linked to

economic and policy aspects. It is supported by

cognitive theory for situational interest type

motivation Moreno (2010) and the research results by

Wolff et al. (2019).

4 CONCLUSIONS

The results and discussion show that students still

need to be improved in identifying data according to

the problem, categorizing data according to its use to

solve problems, making connections between the

results of data analysis, and using data to provide

arguments. The findings of this research can be the

basis for developing learning programs that involve

data literacy training.

ACKNOWLEDGEMENTS

This research is supported and funded by LPPM-PMP

Siliwangi University.

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