Incorporating Open Data Into Introductory Courses in Statistics

International Journal of Research & Method in Education, 2011

Increasing volumes of statistical data are being made available on the open web, including from the World Bank. This 'data deluge' provides both opportunities and challenges. Good use of these data requires statistical literacy. This paper presents results from a project that set out to better understand how socioeconomic secondary data are being used in helping to develop quantitative skills in UK university social sciences, and focuses on World Bank data in support of this. Evidence of real world data use to develop statistically literate social science students is presented. This underpins capacity building in UK social sciences, a need recognized by research councils, statistical organizations and other bodies. The work has helped uncover evidence of good practice in teaching and research to inform others who wish to support their students in becoming statistically literate using real world data. The challenge is to share and replicate this good practice (mainly in economics and econometrics) in other areas of social and educational research which are less well served.

2014

Statistics students need to develop the capacity to make sense of the staggering amount of information collected in our increasingly data-centered world. Data science is an important part of modern statistics, but our introductory and second statistics courses often neglect this fact. This paper discusses ways to provide a practical foundation for students to learn to "compute with data" as defined by Nolan and Temple Lang (2010), as well as develop "data habits of mind" (Finzer, 2013). We describe how introductory and second courses can integrate two key precursors to data science: the use of reproducible analysis tools and access to large databases. By introducing students to commonplace tools for data management, visualization, and reproducible analysis in data science and applying these to real-world scenarios, we prepare them to think statistically in the era of big data.

Journal of Statistics Education

This article describes a collaborative project across three institutions to develop, implement, and evaluate a series of tutorials and case studies that highlight fundamental tools of data science-such as visualization, data manipulation, and database usage-that instructors at a wide-range of institutions can incorporate into existing statistics courses. The resulting materials are flexible enough to serve both introductory and advanced students, and aim to provide students with the skills to experiment with data, find their own patterns, and ask their own questions. In this article, we discuss a tutorial on data visualization and a case study synthesizing data wrangling and visualization skills in detail, and provide references to additional class-tested materials. R and R Markdown are used for all of the activities.

In this paper we review the implementation of the international CensusAtSchool (www.censusatschool.ntu.ac.uk) and related projects, and suggest lessons that could be learned from them to better promote statistical education at all ages. We emphasise how the project's success has been due, in no small part, to the involvement and support of various levels of each country's government statistical services. These include the UK Office of National Statistics, Statistics South Africa, The Office of Economic and Statistical Research of the Queensland Treasury, the Australian Bureau of Statistics, Statistics New Zealand and Statistics Canada. Lastly, we suggest that the project has important lessons of good practice for learning statistics, not just in schools, but also in universities and the workplace.

Statistics Education Research Journal, 2017

Data are abundant, quantitative information about the state of society and the wider world is around us more than ever. Paradoxically, recent trends in the public discourse point towards a post-factual world that seems content to ignore or misrepresent empirical evidence. As statistics educators we are challenged to promote understanding of statistics about society. In order to re-root public debate to be based on facts instead of emotions and to promote evidence-based policy decisions, statistics education needs to embrace two areas widely neglected in secondary and tertiary education: understanding of multivariate phenomena and the thinking with and learning from complex data.

Recent work has shown that using real-life, regularly updated data to teach econometrics and related social science statistical skills has a number of benefits over using fictional or pre-configured datasets that have been developed purely for specific learning activities. The use of actual data adds interest and relevance to courses, helps to prepare students for using statistics in the real world and allows them to explore recent events. Based on case studies collected through the "Real World: Real Data: Real Stories project", ESDS International at Mimas has developed the multimedia, Teaching Tools elearning resource. This web-based resource is designed for social science lecturers who are using, or plan to use, real data for the first time in their teaching. It is intended to support and encourage lecturers' use of real world data in their courses -by presenting examples of how this is already done and hearing actual lecturers speak about their experience of using this type of 'messy' real-world data in their teaching.

Teaching Statistics, 2021

The ubiquitous acquisition and generation of data require a reworking of curricula in introductory statistics in tertiary education. We present a renewed curriculum that focuses on scientific thinking, modeling, and simulation-based inference, utilizing R and various R tools such as shiny and learnr apps. We teach statistics from a data-centric perspective, enabling the students to become data literate. Initial feedback of students and educators at our university of applied sciences shows that students' conceptual understanding improved and they understood the practical applicability of statistics better.

Proceedings of the International Conference on Applied Statistics, 2019

This paper presents the perceptions of social science students about the use of official statistical data, in the context of active learning of Statistics, and other topics related to Applied Statistics. In order to make these courses more attractive, and to challenge and stimulate statistical education, our students work on projects in which they use official statistical data to explore practical, real-life issues. Their attitudes and perceptions regarding official statistical data sources are very important, both for acquisition of statistical analysis skills, essential for their future professional life, and for improvement of the official data sources. Therefore, we conducted a custom-made survey among students from Romanian higher education institutions (HEIs) and gathered a database with 334 responses, which allowed us to identify the main characteristics, problems and solutions concerning the use of statistical official data sources by university students.

New Skills in the Changing World of Statistics Education, 2020

Large data repositories or database management still remain a mirage and tough challenge to accomplish by most developing countries and establishments around the globe. This necessitates the need to improvise on the gathering of suitable data with a good spread to serve as a complement, in the absence of sufficient real-life data. Statisticians are increasingly posed with thought-provoking and even paradoxical questions, challenging our qualifications for entering the statistical paradises created by Big Data. Through classroom activities that involved both sourced real-life and simulated big data in R-environment, models were built and estimates obtained from the adopted techniques revealed the robustness of simulated datasets in a unified observation with improved significant values as reflected in the results. Students appreciated the use of such big data as it enhances their machine learning ability and the availability of sufficient data without delay.