10.06.2021
| Inside Data Science |
Juliane Jarke on Data Science in Education and the Public Sector
DSC co-founder Juliane Jarke talks about digitization and datafication of education and public administration as well as the growing relevance of data for society.
What topics are you currently working on in your research?
I am currently working on a number of projects that examine the increasing importance of data and data science for decision- and meaning-making in education and the public sector. For example, in the public sector data science is used for prediction, pattern recognition, and scoring across a variety of domains such as social benefit fraud, social work, taxation, policing, job seeker training or immigration. In education, data science is used in educational data mining and learning analytics to predict learning outcomes and student retention. It is important to examine how systems based on data science are embedded in decision-making processes because they co-construct educational futures or the social realities that the public sector seeks to govern and administer.
How important is data to your research?
The increasing importance of data (or so-called datafication of social life) has fuelled utopian visions about societal progress featuring open and transparent societies that strengthen grassroots movements and democratic processes. At the same time, fears associated with increased surveillance and control as well as reinforced inequalities and systemic discrimination have emerged. Our research demonstrates that it is important to pay attention to the ways in which the data that decision-support systems use are produced, interpreted, used, and visualised.
What role does data science play in your research? Do you see yourself more as a user, a method developer, a basic researcher, or perhaps something completely different?
I am conducting basic and design-oriented research on the use and application of data science in different social domains. Therefore, I am not so much a user or method developer, but in the tradition of science and technology studies, a researcher who is interested in the ways in which data science is practiced, its imaginaries and the ways in which data science may be used to create more inclusive digital futures.
Which data science methods and technologies are in the focus of your research or could also become interesting in the future?
In one of my next projects, we will use data science methods for participatory urban planning. Text summarisation, argumentation mining, machine translation or clustering will be used for structuring and organising textual input and proposals from citizens to make them available for further collaboration.
What are your main challenges in dealing with data?
My interest, also methodologically, is to research how data move between and across different organisations and contexts. For example, educational data that are produced in schools travel to school authorities, ministries, and may end up in international organisations that compare educational systems. Another example from my research is data on deforestation in the Amazon that is used by environmental NGOs but also different governments to determine funding or trade agreements. In order for these data to travel, they need to shape shift and transform, they need to be translated from one context to another. One methodological challenge in my research is to trace those “data journeys” and make them visible.
And finally, what is your personal motivation for joining the Data Science Center?
The Data Science Center offers the opportunity to examine and challenge some of the basic assumptions of and about data science across disciplinary boundaries and research foci.
You can learn more about Juliane’s activities in her talk
“Cui bono, Data Science? Four questions from Science and Technology Studies“ in the Data Science Forum on 17.06.2021.
« back
10.06.2021 | Inside Data Science
Juliane Jarke on Data Science in Education and the Public Sector
DSC co-founder Juliane Jarke talks about digitization and datafication of education and public administration as well as the growing relevance of data for society.
What topics are you currently working on in your research?
I am currently working on a number of projects that examine the increasing importance of data and data science for decision- and meaning-making in education and the public sector. For example, in the public sector data science is used for prediction, pattern recognition, and scoring across a variety of domains such as social benefit fraud, social work, taxation, policing, job seeker training or immigration. In education, data science is used in educational data mining and learning analytics to predict learning outcomes and student retention. It is important to examine how systems based on data science are embedded in decision-making processes because they co-construct educational futures or the social realities that the public sector seeks to govern and administer.
How important is data to your research?
The increasing importance of data (or so-called datafication of social life) has fuelled utopian visions about societal progress featuring open and transparent societies that strengthen grassroots movements and democratic processes. At the same time, fears associated with increased surveillance and control as well as reinforced inequalities and systemic discrimination have emerged. Our research demonstrates that it is important to pay attention to the ways in which the data that decision-support systems use are produced, interpreted, used, and visualised.
What role does data science play in your research? Do you see yourself more as a user, a method developer, a basic researcher, or perhaps something completely different?
I am conducting basic and design-oriented research on the use and application of data science in different social domains. Therefore, I am not so much a user or method developer, but in the tradition of science and technology studies, a researcher who is interested in the ways in which data science is practiced, its imaginaries and the ways in which data science may be used to create more inclusive digital futures.
Which data science methods and technologies are in the focus of your research or could also become interesting in the future?
In one of my next projects, we will use data science methods for participatory urban planning. Text summarisation, argumentation mining, machine translation or clustering will be used for structuring and organising textual input and proposals from citizens to make them available for further collaboration.
What are your main challenges in dealing with data?
My interest, also methodologically, is to research how data move between and across different organisations and contexts. For example, educational data that are produced in schools travel to school authorities, ministries, and may end up in international organisations that compare educational systems. Another example from my research is data on deforestation in the Amazon that is used by environmental NGOs but also different governments to determine funding or trade agreements. In order for these data to travel, they need to shape shift and transform, they need to be translated from one context to another. One methodological challenge in my research is to trace those “data journeys” and make them visible.
And finally, what is your personal motivation for joining the Data Science Center?
The Data Science Center offers the opportunity to examine and challenge some of the basic assumptions of and about data science across disciplinary boundaries and research foci.
You can learn more about Juliane’s activities in her talk
“Cui bono, Data Science? Four questions from Science and Technology Studies“ in the Data Science Forum on 17.06.2021.
Interviewee:
Dr. Juliane Jarke
Senior researcher at the Institute for Information Management Bremen (ifib)
FB 03 – Mathematics and Computer Science
jarke@uni-bremen.de
« back