Group items tagged

Learning analytics is the use of intelligent data, learner-produced data, and analysis models to discover information and social connections, and to predict and advise on learning. EDUCAUSE's Next Generation learning initiative offers a slightly different definition "the use of data and models to predict student progress and performance, and the ability to act on that information". Their definition is cleaner than the one I offer, but, as I'll detail below, is intended to work within the existing educational system, rather than to modify it. I'm interested in how learning analytics can restructure the process of teaching, learning, and administration.

Academic analytics helps address the public's desire for institutional accountability with regard to student success, given the widespread concern over the cost of higher education and the difficult economic and budgetary conditions prevailing worldwide. Purdue University's Signals project applies the principles of analytics widely used in business intelligence circles to the problem of improving student success within a course and, hence, improving the institution's retention and graduation rates over time.

Singapore's government has also provided substantial support for the Living Analytics Research Centre. The Centre, a research partnership between Carnegie Mellon and Singapore Management University, "seeks to make Singapore one of the world's premier locations for the development and applied use of real-time consumer and social analytics, as well as one of the world's leading centres for computational social science related R&D and education."

My goal with practical learning analytics is to provide access to data in ways that an average teacher, with no special training, can leverage in order to help her students succeed. This is, of course, an extremely tall order

. I think the interesting thing about Learning Analytics is it's one of those areas we think will be important, but we're not quite sure what it is yet, so there's room to explore

I think there is a potential to inject a feedback mechanism in this course, and any MOOC for that matter, with regard to the helpfulness of reading material in the Syllabus.  I think that would especially suit the Learning & Knowledge Analytics course.

A webwalk by Bert DeCoutere talking about the three principles of analytics (in a humourous way!).

For the past several years, EDUCAUSE publications have described the emergence of two complementary forces: (1) the growth of "academic analytics" in higher education and the knowledge services needed to support seamless sharing and leveraging of contextualized data/information; and (2) the escalating accountability demands that are driving performance measurement and improvement initiatives. These forces converged in the July/August 2007 issue of EDUCAUSE Review, which showcased their potentially transformative impacts on higher education

Slides from Feb 18, 2011 web conference Learning Analytics: Notes on the Future Simon Buckingham Shum, Knowledge Media Institute Open University UK

conduct research on behavioural and social network analytics and behavioural experiments so as to discover and harness the laws of information network evolution for networks of people, organisations

Papamitsiou, Z., & Economides, A. (2014). Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence. Educational Technology & Society, 17 (4), 49-64. This paper aims to provide the reader with a comprehensive background for understanding current knowledge on Learning Analytics (LA) and Educational Data Mining (EDM) and its impact on adaptive learning. It constitutes an overview of empirical evidence behind key objectives of the potential adoption of LA/EDM in generic educational strategic planning. We examined the literature on experimental case studies conducted in the domain during the past six years (2008-2013). Search terms identified 209 mature pieces of research work, but inclusion criteria limited the key studies to 40. We analyzed the research questions, methodology and findings of these published papers and categorized them accordingly. We used non-statistical methods to evaluate and interpret findings of the collected studies. The results have highlighted four distinct major directions of the LA/EDM empirical research. We discuss on the emerged added value of LA/EDM research and highlight the significance of further implications. Finally, we set our thoughts on possible uncharted key questions to investigate both from pedagogical and technical considerations.

Data analytics is shorthand for the method of warehousing, organizing, and interpreting the massive amounts of data accrued by online learning platforms and student information systems - now as elemental to higher education as classrooms and filing cabinets - in hopes of learning more about what makes students successful, then giving instructors (and the platforms themselves) the chance to nudge those students accordingly.

Have been listening to a Dave Snowden podcast of a "101 organic KM course". Amongst many familiar themes is the mention of the pitfalls of too much transparency hurting innovation.

Wikipedia page on learning analytics. Note: "please help improve this article by expanding it"

Is Goodhart's law relevant when thinking about Learning Analytics

My take on this week's readings and webinar