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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.

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

data was hard to come by in some districts, it was untrustworthy in others and controlling for all relevant variables when comparing school districts is really, really difficult.

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.

Here we've got a classmate PC demo that shows off Richard Beckwith shows off Classmate Assist which supports users seated or standing at the desk or table. This is an example of context aware computing. There are visual sensors that recognize the items on the table and instruct and walk the students through a series of educational tasks. The goal is to support existing curriculum and practices of teachers.

The plan unveiled on Thursday designates money to create a "dashboard"-an interactive, online database-to give students, parents, and legislators access to detailed measures of departments' and colleges' productivity and efficiency. Data on individual professors will probably also be included, although Dr. Cigarroa stressed that each campus would be able to develop its own system of metrics and the details have not yet been worked out

"This study analyzes the outcomes at more than 380 higher education institutions. It looks at what the chosen strategies have accomplished, in what ways institutions use the data they collect, whether institutions are investing more resources in tools that enable them to collect and manipulate management information, and the degree to which information and analysis are being used to support institutional decision making."

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.

One of the less successful experiments seems to be attempts to integrate VLEs, especially Moodle, within MOOCs.(was My experience with LAK11)

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."

The Bill & Melinda Gates Foundation, the world's largest philanthropy, and the foundation associated with Pearson, the giant textbook and school technology company, announced a partnership on Wednesday to create online reading and math courses aligned with the new academic standards that some 40 states have adopted in recent months.

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Helping words from the "ombudsman" of the course