Big data in language education 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.

2019

The educational data revolution has empowered universities and educational institutes with rich data on their students, including information on their academic data (e.g., program completion, course enrolment, grades), learning activities (e.g., learning materials reviewed, discussion forum interactions, learning videos watched, projects conducted), learning process (i.e., time, place, path or pace of learning activities), learning experience (e.g., reflections, views, preferences) and assessment results. In this paper, we apply clustering to profile students from one of the largest Massive Open Online Courses (MOOCs) in the field of Second Language Learning. We first analyse the profiles, revealing the diversity among students taking the same course. We then, referring to the results of our analysis, discuss how profiling as a tool can be utilised to identify at-risk students, improve course design and delivery, provide targeted teaching practices, compare and contrast different offerings to evaluate interventions, develop policy, and improve self-regulation in students. The findings have implications for the fields of personalised learning and differentiated instruction.

Journal of Research on Educational Effectiveness, 2019

2019

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Data-driven learning for younger learners: Obstacles and optimism. Alex Boulton

Language Learning Journal, 2010

To develop a new learning culture, it is essential to help people meet challenges they now face in the twenty-first century. This review article attempts to present a clear concept of lifelong learning, while describing major challenges toward establishing it in a society. Lifelong learning is viewed as involving all strategies that are put in place to created opportunities for people to learn throughout life. It is about learning of what , how, when and where one wants to learn. Various challenges are to be found to many spheres of life including: financial, demographic, technological, social, environmental and democratic challenges.

These are remarks made at an AERA Open Forum focused on an upcoming National Research Council effort to update the groundbreaking 1999 consensus volume, How People Learn. In the remarks, I highlight the importance of attending to how people learn culturally across settings, the integrity of learning and identity, the importance of drawing on research from science studies to gain insight into mature disciplinary practice, and viewing the challenge of implementation as joint work between research and practice.

Lifelong learning is an essential challenge for inventing the future of our societies; it is a necessity rather than a possibility or a luxury to be considered. Lifelong learning is more than adult education or training -it is a mindset and a habit for people to acquire. It creates the challenge to understand, explore, and support new essential dimensions of learning such as: (1) selfdirected learning, (2) learning on demand, (3) informal learning, and (4) collaborative and organizational learning.

HAL (Le Centre pour la Communication Scientifique Directe), 2015