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Natercia Valle tells a cautionary tale about the use of learning analytics dashboards to increase student motivation, and the challenges of translating theory into design solutions. Natercia Valle is…
Every institution has to decide when the importance of using analytics becomes bigger than the doubts and hesitations.
A largely tacit redefinition of enterprise data prompted by the emergence of learning analytics is, in combination with new cloud technologies, openin
The increasing use of technology for teaching and learning has led to a significant amount of data being collected on how learning occurs in today’s world. To capitalize on this rich data, Learning Analytics (LA) has emerged in recent years as an important field that enables the measurement, collection, analysis and reporting of educational data for the purpose of understanding and optimizing learning.
Here are some tips on how you can track the impact of learning activities.
Implementing a learning analytics system across all higher education institutions in Wales provides a model for the opportunities for such services at
The rise of analytics in higher education raises questions about the responsible use of student data. Here are some of the issues and how institutions are dealing with them.
Since the 2015 release of the EDUCAUSE report The Next Generation Digital Learning Environment: A Report on Research, many articles have been written on various aspects of the NGDLE. In this post, we'll focus on the need for a central repository for learning analytics (see figure 1). Without a central repository, the learning environment — including LMS data and activity records for videos, e-portfolios, and third-party tools — becomes compartmentalized and stove-piped, making it difficult to provide a 360-degree view of the learner and learning environment. This makes it much more difficult to have a combined view of student learning activities that can allow instructors and program managers to ascertain the effectiveness of teaching and learning.
In addition to quantitative accuracy, it is critical for learning analytics to consider design principles and methods of persuasion that convince educ
Had enough of election talk from FiveThirtyEight? These lesser-known analytics blogs offer fresh topics.
Understanding Learning and Learning Design in MOOCs: A Measurement-Based Interpretation
From online forum debates to predictive essay writing software, data showing how students learn can help universities adapt their teaching
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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An interdisciplinary team conducted an interview-based study to explore the perceptions of stakeholders who generate, collect, and use learning and le
There's growing evidence that educators benefit from some training in order to make the most of learning analytics dashboards. Linda Corrin shares insights from her extensive experience - and it's about more than "data literacy"…
I have now covered all five main articles in the special August issue of the journal Distance Education on learning analytics (because it is not an open publication). These are:
1- analytics and learning design at the UKOU.
2- analytics and personality traits in a high school in China
3- analytics and gamification in an undergraduate course at a Hong Kong university
4- learning analytics to predict mastery using an online mathematics tutoring system
5- MOOCs and student data privacy: a sentiment analysis based on the terms of service of three MOOC providers.
However, there are also two commentaries on the five articles in this issue, one by George Siemens and the other by Michael Jacobson. I will briefly discuss these commentaries then give my own overview of what the five papers suggest to me about the current use of learning analytics in online learning and distance education.
The Canvas Network offers free online courses and classes from the world's leading universities. Find a MOOC and enroll now to get started on your new journey!
Your university’s new learning analytics service has flagged that a student is disengaging – what now?
As the field of learning analytics matures we are starting to see a shift in the way researchers and institutions are talking about learning analytics in the context of learning and teaching. From its roots in using student data to address retention, the field is expanding and becoming truly transdisciplinary – drawing on methodologies and analytics techniques from multiple disciplines. This provides exciting possibilities for new ways that learning analytics can be utilised in higher education.
In order not to fail, it is necessary to have a clear vision of what you want to achieve with learning analytics, a vision that closely aligns with in
Listen to Beyond the Lectern Podcast episodes free, on demand. In this episode, Rachel and Jason speak with Professor Dragan Gašević from The University of Edinburgh about the future of learning analytics.
We discussed ideas presented in: Gašević, D., Dawson, S., & Siemens, G. (2015). Let's not forget: Learning analytics are about learning. TechTrends, 51(1), 64–71. Listen to over 65,000+ radio shows, podcasts and live radio stations for free on your iPhone, iPad, Android and PC. Discover the best of news, entertainment, comedy, sports and talk radio on demand with Stitcher Radio.
New research at The University of Texas at Arlington will analyze massive scale data traces from online work and learning communities to create new designs for networked learning and next generation knowledge building on the internet.
The internet is today characterized by the convergence of ubiquitous connectivity, networked computing, and more intelligence through machine learning and artificial intelligence. The data sets used include social networking sites, medical devices, telescopes and satellites to emails, streaming data, financial and commercial transactions.
Glenda Morgan talks about the current and future state of learning analytics.
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.
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