Project-based and Collaborative Learning Approach in

Data Journalism Classes for College University Students

Adi Wibowo Octavianto

Journalism Department, Universitas Multimedia Nusantara, Indonesia

Keywords: Data Journalism, Collaborative Learning, Experiential Learning, Educational Strategy.

Abstract. This article describes ongoing data journalism education's action research in Universitas Multimedia

Nusantara (UMN). Data journalism has become an increasingly needed skill to offset the tendency of

reporting that relies solely on people's statements. However, formal training at the university level on this

subject is almost non-existent. UMN tried to pioneer data journalism training for journalistic students. The

implementation of these courses in the journalism education curriculum at the same time becomes action

research to explore appropriate training and education methods on this subject. In this data journalism

course tested two approaches strategies, namely collaborative learning and experiential project-based

learning. Several teaching team members, college participants, and data journalism practitioners were

interviewed and discussed in the focus group discussion forum to provide evaluations and insights regarding

the implementation of data journalism courses with the two mentioned approaches above.

1 INTRODUCTION

Aliansi Jurnalis Independen (AJI) states that data

journalism is an important skill, and quite a lot of

journalists need this skill (Astuti, 2019). The

development of data journalism in Indonesia can be

said to be slower compared to European and

American countries. Media such as The New York

Times in the United States and The Guardian in the

UK have previously produced editorial projects

involving the use of large datasets and impressive

data visualization (Splendor et al., 2016). Along

with the increasing role of data journalism for the

news industry, the need for data journalism skills

training has also increased.

Splendor et al. (2016) state that in Europe, such

training can be held by three sources, namely

academic, vocational education, and professional

institutions. Meanwhile, in Indonesia itself, based on

pre-research observations, shows that such training

is still minimal. AJI, as a professional journalist

association, organizes training aimed explicitly at

practitioners. From the academic world, there are at

least two universities that hold data journalism

training as part of the curriculum. The two

universities are Multimedia Nusantara University

and the University of Indonesia. In addition to the

two Universities, Gajah Mada University also began

exploring this field through extra-curricular

activities. Other parties who participated in

conducting data journalism training were the

Indonesian Journocoders community.

The field of data journalism expertise is indeed

entirely new. Therefore it is not surprising that this

field has not yet been included in the journalistic

education curriculum in universities. However, at

least 25 universities around the world offer degrees

or special programs dedicated to data journalism

(Vallance-Jones and McKie, 2017). Several

journalistic higher education programs in the United

States include training in data journalism in their

curriculum. However, few provide advanced skills

training, which provides expertise in usage

spreadsheets, statistical software, relational

databases, or programming (Berret and Phillips,

2016).

The entry of subjects in data journalism training

in the journalistic higher education curriculum has

some obstacles. One of the essential things is the

area of mathematics, statistics and programming is

not a common thing in journalistic study programs

((Berret and Phillips, 2016)Barret & Phillips, 2016;

(Splendor et al., 2016)Splendor et al., 2016; (Davies

and Cullen, 2016)Davies & Cullen, 2016). This

research is action research where the challenges in

190

Octavianto, A.

Project-based and Collaborative Learning Approach in Data Journalism Classes For College University Students.

DOI: 10.5220/0010511100002967

In Proceedings of the 4th International Conference of Vocational Higher Education (ICVHE 2019) - Empowering Human Capital Towards Sustainable 4.0 Industry, pages 190-194

ISBN: 978-989-758-530-2; ISSN: 2184-9870

conducting data journalism training for journalistic

students will be answered through the

implementation of training in particular subjects in

the UMN Journalism Study Program through

experiential and collaborative learning approaches.

2 THEORETICAL

FRAMEWORKS

2.1 Data Journalism

The term data journalism has some definitions that

are not the same (Davies and Cullen, 2016). Jones &

McKie (2017) mention that data journalism is rooted

in activities called "computer-assisted-reporting"

(CAR). The rapid development of computer

technology in the 80s made it easy for journalists to

use software such as FoxPro and Microsoft Excel to

read trends and patterns in government databases.

Berret & Phillips (2016) quotes a more practical

definition of data journalism describes by Alexander

Howard. Howard explains that "data journalism is

gathering, cleaning, organizing, analyzing,

visualizing, and publishing data to support the

creation of acts of journalism." Data journalism is

the application of data science in the field of

journalism (Berret and Phillips, 2016). Meanwhile,

Splendor et al. (2016) offer another definition of

data journalism, " it is a matter of collecting,

processing, analyzing, and essential data of

information using computer technology." Although

the term data journalism has a variety of definitions,

there are several things that, in principle, the same.

Data journalism is always associated with the

development of computing technology, and

journalist analyzes data through a statistical

approach, then from the data, the journalist seeks for

a storyline that has news value.

2.1.1 Data Journalism Education

The attention to education in data journalism

throughout the world is increasing. However, some

studies on data journalism training indicate a similar

problem, namely; who will teach, the difficulties in

meeting demands of technical expertise and

statistics, and how to attract students who are not

familiar with the concept of data journalism (Mair et

al., 2017) (Alves et al., 2014).

As part of data science, data journalism training

needs to reach three main areas of expertise, namely,

journalism as the primary domain, applied

mathematics (statistics), as well as coding and

programming. The demands of the second and third

mastery of expertise usually become obstacles

because, as Berret & Phillips (2016) say, journalistic

schools and their practitioners tend to avoid

quantitative skills training.

Charles Berret & Cheryl Phillips (2016) offer

five curriculum models that universities can use as a

data journalism education strategy. The first two

models are more suitable when applied to the level

of undergraduate education, while the other three

models are more flexible can be used both at the

level of undergraduate and graduate (Heravi, 2019).

The first model, Integrating data journalism as a

core class. This model put a data journalism course

as a part of the core curriculum of journalistic

education at the undergraduate level. The course

serves as a basic introduction to data and computing

journalism skills (Heravi, 2019).

The second model is in the form of integrating

data and computation subjects into existing courses

and concentrations. The implementation of this

model is to insert discussions related to data

journalism and computation in pre-existing classes.

The analysis of data journalism is broken down and

disseminated in various subjects that are considered

relevant to each discussion (Heravi, 2019).

The third model put data & computational

journalism course as a concentration program that is

part of a journalistic study. Students were choosing

several elective courses that have been prepared to

achieve specific expertise in the field of data

journalism (Heravi, 2019).

The fourth model is for the graduate degree level

in journalism. This model is intended for journalistic

practitioners who want to learn new particular skills

in data and computational journalism. Participants

who already have an understanding and experience

as a journalistic practitioner can be directed to

achieve a level of expertise that is more focused and

in-depth compared to if the students come from

undergraduate programs (Heravi, 2019).

The fifth model, in principle, is to insert the

theme of data and computational journalism as part

of the graduate degree program with specific

emerging journalistic techniques and technologies.

Programs like this are a vehicle for exploring new

approaches and technologies in the field of

journalism, and for now, data, computational

journalism, machine learning, drones, and virtual

reality are still areas of study that need to be

explored further(Heravi, 2019).

This action research examines the first model,

namely by developing a specialized course that

teaches conceptual understanding and mastery of

Project-based and Collaborative Learning Approach in Data Journalism Classes For College University Students

191

essential technical skills in data journalism. This

course has been going on for two years.

2.2 Learning Model

2.2.1 Collaborative Learning

Collaborative learning is a learning approach that

focuses on the collective knowledge and thinking.

Through this approach, students have to deal with

problems and assignments to real-world problem-

solving. This kind of learning strategy requires

effective communication between students,

lecturers, and other relevant parties.

The collaborative classroom has four general

characteristics. The first two characteristics are

related to changes in relations between lecturers and

students. The third characteristic describes a new

approach for lecturers in giving instructions. Fourth,

discuss the collaborative composition of the

classroom. The four components are:

1. Shared knowledge among teachers and

students

2. Shared authority among teachers and

students

3. Teachers as mediators

4. Heterogenous grouping of students

The collaborative learning approach demands

changes in instruction from traditional patterns to

patterns of education that are suitable for this

approach. Changes like this have some challenges

that must be considered.

Those challenges are:

1. Classroom control. Collaborative

classroom tends to be more crowded

than traditional classes.

2. Preparation time for collaborative

learning. Lesson plans need to be made

but must accommodate flexibility to

build the desired collaboration.

3. Individual differences among students.

This difference can be useful for

collaboration, but it also has potential

problems when the difference lies in

the gap in expertise and motivation.

4. Individual responsibility for learning.

The traditional pattern of relying on its

own assessment while on the

collaborative classroom is complicated.

Participants need a sense of personal

responsibility for self-development

rather than just pursuing value.

5. Conflict values. Collaboration requires

a lot of communication and discussion.

The potential for conflict arises when

there are differences in personal values

between students and lecturers

involved.

2.2.2 Experiential Learning

Clark in Burns (2017) states, "the experiential

learning has a deserved place in journalism

education." The underpinning concept of

experiential learning is mostly "learning by doing."

Students gain knowledge and understanding through

a reflective process of experience (Mair et al., 2017).

Experiential learning strategies contain four

stages of the process, namely; (1) concrete

experience, (2) reflective observation, (3) abstract

conceptualization, and (4) active experimentation

(Mair et al., 2017).

Burns (2017) also proposed that experiential

learning be combined with Team-Based Learning

(TBL). This TBL model is a method of learning and

teaching based on "small group interaction" (Mair et

al., 2017). This approach has four fundamental

principles: (1) adequately formed and managed

groups; (2) accountability for quality of students'

work; (3) frequent and timely feedback; and (4)

group assignment to promote learning and team

development (Stein, et all in Burns, 2017) (Mair et

al., 2017).

3 METHODOLOGY

3.1 Research Approach and Design

Brydon-Miller, Greenwood, and Maguire (2003)

state that action research is participatory research,

where researchers are an active member of the

process or action under study. Meanwhile, Ivankova

and Winggo (2018) stated that action research is "a

cross-disciplinary methodological approach that

focuses on learning about practical issues to improve

or change them."

This paper is written based on the action

research approach, where the author is a part of a

team who designs, implement, and evaluate the

Interactive Data Journalism course at Universitas

Multimedia Nusantara.

In this research, the author use document study

and in-depth interview as data collection techniques.

The documents referred to in this research are course

assignments, midterm results, final semester exam

ICVHE 2019 - The International Conference of Vocational Higher Education (ICVHE) “Empowering Human Capital Towards Sustainable

4.0 Industry”

192

results, and other documents relating to this course

— more in-depth information gathered by

interviewing several informants.

3.2 Informants

The three categories of informants interviewed were

1. Journalistic students were participating

in the Interactive Data Journalism

course in the Journalistic Study

Program, Multimedia Nusantara

University.

2. Teaching lecturers in the Interactive

Data Journalism course in the

Journalistic Study Program,

Multimedia Nusantara University

3. Data journalist practitioners involve as

mentors and guest lecturers in the

Interactive Data Journalism course,

Multimedia Nusantara University.

3.3 Data Analysis Techniques

The purpose of action research is to learn practical

issues by doing and analyze the process in order to

improve or change them. In this case, the problem is

the implementation of the Interactive Data

Journalism course at the college level through a

collaborative approach and experience-based

learning.

The data obtained becomes material for

evaluative analysis. The learning process occurs

compared to student achievement in mastering the

ability of data journalism, which includes

"gathering, cleaning, organizing, analyzing,

visualizing, and publishing data to support the

creation of acts of journalism."

The score and quality of student work in

assignments and examinations are indicators of

achievement, while in-depth interviews try to find

out what is right and what needs to improve in the

learning process.

4 RESULT AND DISCUSSION

The Interactive Data Journalism subject course

consists of 14 face-to-face class meetings, one

midterm exam, and one final exam. Participants of

this course work in groups of a maximum of 5

members. The five members have their respective

roles, which include: News Producer, Data

Journalist, Data Visualizer, and Researcher &

Writer. Except for the role of the News Producer,

two students can pick the same roles if they think it

is necessary.

During the first seven meetings, all groups were

given skills training to search, clean, analyze,

visualize, and write a data-driven story. The midterm

test then measured the results of the training. Each

student was asked to complete a mission-based data-

driven journalism project using and the data

provided. At this stage, each student must be able to

demonstrate the skill of cleaning data, analyzing,

making visualization, and finding patterns of data

that can be a focus of data-based stories.

Entering the 8th meeting, participants began

working intensively in groups to work on data-based

journalism projects. The teams must submit the

completed final assignment at the 13th and 14th

meetings. The final project begins with a public

lecture given by a journalist from beritagar.id. This

public lecture provides an overview of the best

practice process of data journalism in the real world.

Then the guest lecturer gave several project themes

that had real news value to be done by existing

groups.

Of the seven classes of this course, 4 of the best

proposals were chosen to get mentoring/mentoring

from news journalists in the process of completing

their final project.

There are three checkpoints to test the mastery of

student's data journalism skills. The first checkpoint

is the midterm exam. Students are given several

2018 DKI Jakarta APBD datasets, and they must

build a data-driven story based on those datasets.

The DKI Jakarta APBD dataset and the intended

realization consists of 13 files, and each contains

around 50,000 rows of data, they can choose how

many datasets need to be used. Scoring is given

based on each stage of data journalism workflow,

from cleaning to writing the story. At this

checkpoint, the average score of students from 226

participants was 60. The highest score was 95, while

the lowest score of test participants was 5. There are

several challenges that students have during the

exam. Students were not familiar with the issue, so

it requires quite a lot of time to study it before they

can get the story idea. Students have difficulty

analyzing an extensive dataset while the software

used is still limited to Microsoft excel. The other

factor is technical errors in the laboratory used for

this exam.

Overall there are some critical findings related to

obstacles faced by students in the learning process of

data journalism in a collaborative and project-based

manner. These findings are:

Project-based and Collaborative Learning Approach in Data Journalism Classes For College University Students

193

1. Students, lecturers, and mentors agree

that the most critical and problematic

skills are finding and developing story

ideas.

2. Students have difficulty finding the

right dataset due to lack of

understanding of the issue and location

of relevant data sources.

3. Another challenge is the weak

statistical ability and mastery of

computer software that makes data

processing easier.

4. Learning motivation is not the same.

5. The assumption that mathematics

(statistics) and programming (coding)

are outside the realm of journalism

6. The previous learning culture becomes

an obstacle in sharing knowledge and

authority process among students and

lecturers.

7. Mentors and real-world problems to

solve are still lacking.

8. Learning new skills that are quite

complex becomes more manageable

when done with a team with varied

abilities.

9. The side effect of this experiential and

collaborative learning approach is an

opportunity for shaping students' soft-

skill in collaborative problem-solving.

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