This report aims to understand the state of the art in the implementation of learning analytics for education and trainingin both formal and informal settings. It also aims to understand the potential for European policy to be used to guide and support the take-up and adaptation of learning analytics to enhance education in Europe. This study, called the Implications and Opportunities of Learning Analytics for European Educational Policy (henceforwardthe Study), therefore has an international scope, although the policy perspectives are discussed from the point of view of the EU. The research was conducted between September 2015 and June 2016.The key findings seek to inform, guide and inspire practitioners, researchers and policy makers at all levels (institutional, local, regional, national, international) inimplementing learning analytics in European education and training.
"If you think data—in education, or any field—is cut and dry, think again. Working with data in the classroom, especially, can be either exhausting or exhilarating—depending on your fitness level. Data can be big, but also quite small. It’s often quantitative, but is increasingly qualitative."
If you’re an educator, chances are you’ve heard the phrase “data-driven instruction,” where you’re asked to constantly assess, analyze, and adjust how you teach students.But one word in that phrase often raises a host of questions: What counts as “data”? How do you collect it? And what types of data
Next time users search for a college or university in Google, they’ll get more than a map and logo for the institution in the right-hand side of their browser. Alongside the address and brief Wikipedia synopsis they’ll find information related to student outcomes, such as the graduation rate and the
Earlier this week my Ithaka S+R colleagues and I published “Student Data in the Digital Era: An Overview of Current Practices,” in which we review how institutions of higher education are currently using student data, and some of the practical and ethical challenges they face in doing so. As we conducted research for this report, part of our Responsible Use of Student Data in Higher Education project with Stanford University, we heard recurring concerns about the growing role of for-profit vendors in learning analytics. These third-party vendors, the argument goes, operate without the ethical obligations to students that institutions have, and design their products at a remove from the spaces where learning happens.
If you think data—in education, or any field—is cut and dry, think again. Working with data in the classroom, especially, can be either exhausting or exhilarating—depending on your fitness level. Data can be big, but also quite small. It’s often quantitative, but is increasingly qualitative. It’s pr
Higher education data professionals need to work to take the complexity out of institutional data, making it easily accessible, understandable, and actionable. Institutions must take fundamental steps to successfully implement an effective, institution-wide business intelligence and analytics capability.
In building this capability, institutions must provide greater visibility into the critical connections between enrollment, student outcomes, and financial data.
Technology alone isn't the answer — investing in data science and storytelling expertise is critical to glean insights that lead to action.
Four years after the launch of edX, the data generated by massive open online courses still mystifies many institutions. Could inter-university collaboration unlock the secrets to better course delivery?
In short, we want educational predictions to be wrong. If our predictive model can tell that a student is going to fail, we want that to be true only in the absence of intervention. If the student does in fact fail, that should be seen as a failure of the system. A predictive model should be part of a prediction-and-response system that (1) makes predictions that would be accurate in the absence of a response and (2) enables a response that renders the prediction incorrect (e.g., to accurately predict that, given a specific intervention, the student will succeed). In a good prediction-and-response system, all predictions would ultimately be negatively biased. The best way to empirically demonstrate this is to exploit random variation in the assignment of the system—for example, random assignment of the prediction-and-response system to some students but not all. This approach is rarely used in residential higher education but is newly enabled by digital data.The grand challenge in data-intensive research and analysis in higher education is to find the means to extract knowledge from the extremely rich data sets being generated today and to distill this into usable information for students, instructors, and the public.
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