Statistical and data literacy in policy-making

Umbach, Gaby

doi:10.3233/sji-220962

Cited by 6 publications

(5 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence, students had difficulty understanding, analyzing, and interpreting data. In the opinion of researchers, this difficulty is a serious problem, considering that in the current era, it is expected that humans can adapt to information/data that is massively spread (Batur & Baki, 2022;Budgett & Renelle, 2023;Koga, 2022;McMaster, 2022;Rizou et al, 2022;Sutherland et al, 2022), then all aspects currently work with data such as decision making (Spartanburg Comunity College Library, 2023; Umbach, 2022). Furthermore, from the results of digging deeper information, the researcher also found that students who obtained the maximum score (Score 20 and Score 10) because of the Indonesian Education issues that the author presented in the research instrument really helped students understand the problem (Phadke et al, 2022).…”

Section: Resultsmentioning

confidence: 99%

Analysis of Student Statistical literacy Using Test Questions integrated with Indonesian Education Issues as a form of Contextual approach

Habibie,

Kartono,

Kartono

et al. 2023

Polyhedron Int. J. Math. Educ.

View full text Add to dashboard Cite

This study aims to analyze the ability of SL using statistical literacy ability test questions integrated with Indonesian education issues through a quantitative descriptive research approach to 116 students of the Elementary School Teacher Education Study Program at Muhammadiyah University Muara Bungo. The results showed that students were still low in Statistical Literacy skills. These results can be seen from 75% of students who scored below 60 and 25% of students who scored above 60 on the Statistical Literacy ability test. Then, it can be seen from the average ability of students to solve SL test questions based on Indicators, namely 71.33% in indicator 1, 53.16% in indicator 2, and 57.18% in indicator 3. At the same time, the average student who is able to solve SL questions is 28.67% in indicator 1, 46.84% in indicator 2, and 42.82% in indicator 3. Based on in-depth interviews, researchers see the main factor in the low SL ability of students being that they have not mastered descriptive statistics. The main factor that helps students solve SL questions is the Indonesian Education Issue approach. Researcher suggest that further research needs to be done that can improve students' SL skills through the application of learning models, the development of learning models, or the application of learning strategies to solve SL problems.

show abstract

Section: Resultsmentioning

confidence: 99%

Analysis of Student Statistical literacy Using Test Questions integrated with Indonesian Education Issues as a form of Contextual approach

Habibie,

Kartono,

Kartono

et al. 2023

Polyhedron Int. J. Math. Educ.

View full text Add to dashboard Cite

show abstract

“…Knowledge sharing and access to quality information are safeguards for resilient participatory democracies. An essential bridge to a desired and desirable future for collaborative and engaged data-driven research is offered by research data literacy [65,66,67,68]. It responds to the Council of the EU's 2015 request for enhanced data skills for academia, researchers and the wider community by developing the ability not only to use data, but also to understand and assess the value of data use for academic and societal progress.…”

Section: Discussionmentioning

confidence: 99%

Open Science and the impact of Open Access, Open Data, and FAIR publishing principles on data-driven academic research: Towards ever more transparent, accessible, and reproducible academic output?

Umbach

2024

SJI

Self Cite

View full text Add to dashboard Cite

Contemporary evidence-informed policy-making (EIPM) and societies require openly accessible high-quality knowledge as input into transparent and accountable decision-making and informed societal action. Open Science1 supports this requirement. As both enablers and logical consequences of the paradigm of Open Science, the ideas of Open Access, Open Data, and FAIR publishing principles revolutionise how academic research needs to be conceptualised, conducted, disseminated, published, and used. This ‘academic openness quartet’ is especially relevant for the ways in which research data are created, annotated, curated, managed, shared, reproduced, (re-)used, and further developed in academia. Greater accessibility of scientific output and scholarly data also aims at increasing the transparency and reproducibility of research results and the quality of research itself. In the applied ‘academic openness quartet’ perspective, they also function as remedies for academic malaises, like missing replicability of results or secrecy around research data. Against this backdrop, the present article offers a conceptual discussion on the four academic openness paradigms, their meanings, interrelations, as well as potential benefits and challenges arising from their application in data-driven research.

show abstract

“…Within it, data literacy is the main criterion to assess stakeholders' readiness and capabilities, and an essential part of teaching necessary skills for EIPM. For the purpose of this article, we understand data literacy in a narrow sense that focuses on skills (e.g., reading, analysing, managing, and arguing with data) as opposed to a broader understanding that entails societal and political functions of data, the role of data citizenship and corresponding rights, and the political economy of digital platforms (see Data-Pop Alliance & Internews, 2015;Pangrazio & Sefton-Green, 2020;and Umbach, 2022).…”

Section: Evidence-informed Policymaking (Eipm)mentioning

confidence: 99%

Data Use in Evidence-Informed Policymaking (EIPM) as a Teaching Subject: Challenges and Pitfalls

Tkalec¹,

Umbach²

2022

Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. Proceedings of the Eleventh International Conference

View full text Add to dashboard Cite

This article examines evidence-informed policymaking from a teaching perspective. It especially focuses on data use within it. Based on the authors’ practical experience, the article proposes a conceptual frame for designing teaching materials anchored in three dimensions: acquiring/possessing (data literacy) skills, understanding the benefits of evidence-informed policymaking, and creating channels for evidence-informed policymaking implementation. Moreover, it outlines challenges and pitfalls in developing such learning materials for data-driven evidence-informed policymaking. The findings suggest that the inherent weakness of evidence-informed policymaking understood as an approach to policymaking, i.e., its conceptual ambiguity, also influences the development of data-focused evidence-informed policymaking learning materials.

show abstract

Statistical and data literacy in policy-making

Cited by 6 publications

References 21 publications

Analysis of Student Statistical literacy Using Test Questions integrated with Indonesian Education Issues as a form of Contextual approach

Analysis of Student Statistical literacy Using Test Questions integrated with Indonesian Education Issues as a form of Contextual approach

Open Science and the impact of Open Access, Open Data, and FAIR publishing principles on data-driven academic research: Towards ever more transparent, accessible, and reproducible academic output?

Data Use in Evidence-Informed Policymaking (EIPM) as a Teaching Subject: Challenges and Pitfalls

Contact Info

Product

Resources

About