Education policy research in the big data era: Methodological frontiers, misconceptions, and challenges (original) (raw)
Related papers
Big Data and Educational Research
The BERA/SAGE Handbook of Educational Research: Two Volume Set
Big data and data analytics offer the promise to enhance teaching and learning, improve educational research and progress education governance. This chapter aims to contribute to the conceptual and methodological understanding of big data and analytics within educational research. It describes the opportunities and challenges that big data and analytics bring to education as well as critically explore the perils of applying a data driven approach to education. Despite the claimed value of the increasing amounts of large and complex data sets and the growing interest in making sense of them there is still limited knowledge on big data and educational research. Over the last decades, the developments on information and communication technologies are reshaping teaching and learning and the governance of education. A broad variety of online behaviours and transactional data is (or can be) now stored and tracked. Its analysis could provide meaningful insights to enhance teaching and learning processes, to make better management decisions and to evaluate progresses-of individuals and education systems. This chapter starts by defining big data and the sources and artefacts collect, generate and display data. In doing so it explores aspects related to data ownership and researchers' access to big data. It then assesses the value of big data for educational research by critically considering the stages involved in the use of big data, providing examples of recent educational research using big data.
Big Data Comes to School: Implications for Learning, Assessment, and Research
Big data has become a much-used phrase in public discourse, optimistically as well as controversially. In more optimistic moments, big data heralds " a revolution that will transform how we live, work, and think " (Mayer-Schönberger & Cukier, 2013), changing the way we do business, participate in government, and manage our personal lives. In moments of anxiety, we worry about the effects upon our lives of surveillance by corporations and governments (Podesta, Pritzker, Moniz, Holdern, & Zients, 2014). In education, we have witnessed a similar range of promises and anxieties about the coming era of big data. On the one hand, it is claimed that big data promises teachers and learners a new era of personalized instruction, responsive formative assessment, actively engaged pedagogy, and collaborative learning. On the other hand, critics worry about issues such as student privacy, the effects of profiling learners, the intensification of didactic pedagogies, test-driven teaching, and invasive teacher-accountability regimes. Whether one's orientation is optimistic or anxious, all agree that the changes are substantial and that we educators have yet barely explored the implications. This article maps the nature and consequences of big data in education. We set out to provide a theoretical overview of new sources of evidence of learning in the era of big data in education, highlighting the continuities and differences between these sources and traditional sources, such as standardized , summative assessments. These sources also suggest new kinds of research methodology that supplement and in some cases displace traditional observational and experimental processes. We ground this overview in the field of writing because it offers a particularly interesting case of big data in education, and it happens to be the area of our own research (Cope & Kalantzis, 2009; Kalantzis & Cope, 2012, 2015b). 1 Not only is writing an element of " literacy " as a discipline area in schools; it is also a medium of for knowledge representation, offering evidence of learning across a wide range of curriculum areas. This evidence has greater depth than other forms of assessment, such item-based assessments, which elicit learner response in the form of right and wrong answers. Writing, in contrast, captures the complex epistemic performance that The prospect of " big data " at once evokes optimistic views of an information-rich future and concerns about surveillance that adversely impacts our personal and private lives. This overview article explores the implications of big data in education, focusing by way of example on data generated by student writing. We have chosen writing because it presents particular complexities, highlighting the range of processes for collecting and interpreting evidence of learning in the era of computer-mediated instruction and assessment as well as the challenges. Writing is significant not only because it is central to the core subject area of literacy; it is also an ideal medium for the representation of deep disciplinary knowledge across a number of subject areas. After defining what big data entails in education, we map emerging sources of evidence of learning that separately and together have the potential to generate unprecedented amounts of data: machine assessments, structured data embedded in learning, and unstructured data collected incidental to learning activity. Our case is that these emerging sources of evidence of learning have significant implications for the traditional relationships between assessment and instruction. Moreover, for educational researchers, these data are in some senses quite different from traditional evidentiary sources, and this raises a number of methodological questions. The final part of the article discusses implications for practice in an emerging field of education data science, including publication of data, data standards, and research ethics.
Big data in education: a state of the art, limitations, and future research directions
International Journal of Educational Technology in Higher Education, 2020
Big data is an essential aspect of innovation which has recently gained major attention from both academics and practitioners. Considering the importance of the education sector, the current tendency is moving towards examining the role of big data in this sector. So far, many studies have been conducted to comprehend the application of big data in different fields for various purposes. However, a comprehensive review is still lacking in big data in education. Thus, this study aims to conduct a systematic review on big data in education in order to explore the trends, classify the research themes, and highlight the limitations and provide possible future directions in the domain. Following a systematic review procedure, 40 primary studies published from 2014 to 2019 were utilized and related information extracted. The findings showed that there is an increase in the number of studies that address big data in education during the last 2 years. It has been found that the current studi...
Understanding Education through Big Data
2013
The seduction of 'Big Data' lies in its promise of greater knowledge. The large amounts of data created as a by-product of our digital interactions, and the increased computing capacity to analyse it offer the possibility of knowing more about ourselves and the world around us. It promises to make the world less mysterious and more predictable. This is not the first time that new technologies of data have changed our view of the world. In the nineteenth century, statistical 'objective knowledge' supplanted the personal knowledge of upperclass educated gentlemen as the main way in which governments came to know about those they governed. In our own time we seem to be facing a new revolution in which the basis of how we come to 'know' something -our epistemological foundations -is becoming reliant on big data analysis. From the perspective of this new epistemological turn, our knowledge -from the performance of healthcare staff to how we choose a romantic partner -rests on the extent to which it is known through big data analysis. But what does it mean for education if the way that we know about it is governed by big data? Here, I sketch out some of the questions raised by the turn to a 'big data epistemology' in education.
Big Data Analytics National Educational System Monitoring and Decision Making
2016
This paper reviews the applications of big data in supporting monitoring and decision making in the National Educational System. It describes different types of monitoring methodologies and explores the opportunities, challenges and benefits of incorporating big data applications in order to study the National Educational System. This approach allows to analyze schools as entities, which included in a local context with specific social, economic, and cultural development features. In addition, the paper attempts to identify the prerequisites that support the implementation of data analysis in the national educational system. This review reveals that there are several opportunities for using big data (structured and unstructured information) in the educational system, in order to improve strategic multidimensional knowledge for decision making and developing educational policies; however, there are still many issues and challenges that need to be addressed so as to achieve a better use of this technology. (World Journal of Social Science Research, Vol. 3, No. 2, 2016, 219-242)
Proceedings of the 6th International Conference on Humanities and Social Science Research (ICHSSR2020), 2020
Artificial intelligence (AI) and big data projects have been the focus of discussion to improve the learning experiences and outcomes. Based on a mapping of 980 articles, this article conducts a systematic review of educational research related to big data and AI by using VOSviewer. This article mainly examines three aspects: main sources, disciplines, and keywords. It identifies several research clusters (e.g. multidisciplinary, education technology, and information) and main research topics such as learning analytics, intelligent tutoring systems, and collaborative learning, higher education, etc. The systematic mapping of the literature contributes to the groundwork for educators, researchers, and policymakers for further research, curriculum and policy work.
Big Data and the Liberal Conception of Education
Theory & Research in Education 15(3) (2017): 290-305, 2017
This article develops a perspective on big data in education, drawing on a broadly liberal conception of education's primary purpose. We focus especially on the rise of so-called learning analytics and the associated rise of digitization, which we evaluate according to the liberal view that education should seek to cultivate individuality and proceed partly by way of experimentation and with an emphasis on civic education. Our argument is not that the use of big data is wholly out of place in education. Indeed, it might have significant value in pursuit of certain educational aims. Nevertheless, the liberal conception shows how education is distinct from other domains in which big data are being applied, in ways that suggest that considerable caution must be exercised when they are used in educational contexts.
2016
The term ‘Big Data’ is often misunderstood or poorly defined, especially in the public sector. Ines Mergel, R. Karl Rethemeyer, and Kimberley R. Isett provide a definition that adequately encompasses the scale, collection processes, and sources of Big Data. However, while recognising its immense potential it is also important to consider the limitations when using Big Data as a policymaking tool. Using this data for purposes not previously envisioned can be problematic, researchers may encounter ethical issues, and certain demographics are often not captured or represented.