"Learning Analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs."
Today, technology is allowing us to capture unprecedented data on our daily activities. Wearables like Fitbit track movement, calories burned—even sleep. GPS enabled mobile devices capture my car’s every turn. The Internet of Things helps us understand temperature preferences to optimize energy
The University of Wisconsin-Green Bay's CIO is pursuing a track that marries institutional data, predictions and business rules to create prescriptions for student and campus success. Here are his 12 best practices for prescriptive analytics.
I chose “data” as one of the top trends of 2011, and the opening line of that article reads “If data was an important trend for 2011, I predict it will be even more so in 2012.” Indeed. There’s a great deal that happened in 2012 that’s a continuation of what we saw last year — enough that I could probably just copy-and-paste from the article I wrote back then:
The State of Learning Analytics in 2012: A Review and Future Challenges, The Open University, March 2012. Author Rebecca Ferguson from the Open University's Knowledge Media Institute, provides a detailed look at the history of Learning Analytics research and what the future may hold.
In today’s complex world, post-secondary institutions are under competitive, customer, and regulatory pressures to account for our business performance, our contribution to the societies we serve, our research productivity, and most important, our students’ success. Learning Analytics is seen as a key tool to enable us to do so.
It was a sentiment repeated several times throughout the Learning Analytics and Knowledge 2012 conference: this could the beginning of a new discipline. It’s something I’ve heard at recent big data events too (namely at O’Reilly Media’s Strata conferences): we are forming a new field, one with new tools and new practices and new objects of inquiry.
A key next step in this research programme is to harvest and analyse data from the traces that learners leave as they engage in social digital spaces, and to explore its relationship to other data sets and data streams. This PhD will therefore fund a technically strong candidate to design, implement and evaluate “Learning Analytics for Learning Power”. The project will deploy iterative prototypes in authentic use contexts, considering OU platforms as a starting point (e.g. SocialLearn ; Cohere ; EnquiryBlogger ), but open to data streams from new kinds of digitally instrumented interactions (e.g. from Pervasive Computing; Augmented Reality; Quantified Self). A possible outcome is an analytics architecture open to diverse forms of input, grounded in a theoretically robust framework, generating visual analytics with transformative power for reflective learners.
In their discussions over the past year, the EDUCAUSE IT Issues Panel touched several times on the issue of analytics and strategies on how best to implement analytics in higher education. Discussions ranged from implementing analytics to improve core administrative and business functions to how best to implement analytics in a predictive sense to ensure student success in academic programs.
In answering five questions, the following IT Issues panelists provided their thoughts on how analytics can improve higher education and how best to implement analytics:
Learning analytics holds increasing potential for student agency and autonomy, highlighting a need for ethical discourse at all levels of higher education institutions. Topics central to this dialogue include student awareness of analytics, the future of algorithms and learning analytics, and the redefinition of failure.
"This course will provide a generally non-technical introduction to learning analytics and how they are being deployed in various contexts in the education field. Additionally, the tools and methods, ethics and privacy, and systemic impact of analytics will be explored, presenting a broad overview of the current state and possible future directions of the field.
Capturing and analyzing data has changed how decisions are made and resources are allocated in the fields of business, journalism, government, military, and intelligence. Through better use of data, leaders are able to plan and enact strategies with greater clarity and confidence. Data is a value point that drives increased organizational efficiency and a competitive advantage. Analytics provide new insight and actionable intelligence. Companies such as Microsoft, IBM, Google, and Amazon are investing heavily in technologies and techniques to help individuals and organizations make sense of and unlock the value within big data..."
Learning analytics tools can track far more data than an instructor can alone. For high-enrollment classes in particular, LA provides a support structure that monitors student progress and offers timely guidance to students whose academic under- performance might otherwise go quietly unnoticed. At their best, LA applications can identify factors that are unexpectedly associated with student learning and course completion. This information can be valuable both to students, in how they approach learning, and to faculty, in how they structure curricula.
"A number of experiments are using new kinds of data – such as how many times a student has clicked on an e-textbook or logged in to a class Web page – to measure and guide learning in new ways. That could improve the student experience, but it could also end up dumbing down college, argues Gardner Campbell, director of professional development and innovative initiatives at Virginia Tech."
I suppose it begs the question - what are effective ways of utilising the availability of these new data? - my first thought is that they may assist in identifying students who are finding difficulty.
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