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December 22, 2011 IBM Solidifies Academic Analytics Investments Datanami Staff As their own detailed report in conjunction with MIT Sloan made clear, IBM is keenly aware of the dramatic talent shortfall that could keep the future of big data analytics in check. Accordingly, the company is stepping in to boost analytics-driven programs at universities around the world. A report out of India this week indicated that Big Blue is firming up its investments at a number of academic institutions worldwide in the hopes of readying a new generation of analytics graduates. This effort springs from the company's Academic Initiative, which is the IBM-led effort to partner with universities to extend the capabilities of institutions to provide functional IT training and research opportunities.

Satisfaction and its discontents 8 March 2012 The National Student Survey puts pressure on lecturers to provide 'enhanced' experiences. But, argues Frank Furedi, the results do not measure educational quality and the process infantilises students and corrodes academic integrity One of the striking features of a highly centralised system of higher education, such as that of the UK, is that the introduction of new targets and modifications to the quality assurance framework can have a dramatic impact in a very short space of time. When the National Student Survey was introduced in 2005, few colleagues imagined that, just several years down the road, finessing and managing its implementation would require the employment of an entirely new group of quality-assurance operatives. At the time, the NSS was seen by many as a relatively pointless public-relations exercise that would have only a minimal effect on academics' lives. It is unlikely that even its advocates would have expected the NSS to acquire a life of its own and become one of the most powerful influences on the form and nature of the work done in universities.

Assessing learning dispositions/academic mindsets Mar 01 2014 2 A few years ago Ruth and I spent a couple of days with the remarkable Larry Rosenstock at High Tech High, and were blown away by the creativity and passion that he and his team bring to authentic learning. At that point they were just beginning to conceive the idea of a Graduate School of Education (er… run by a high school?!). Yes indeed. Screen Shot 2014-02-28 at 16.56.56Now they're flying, running the Deeper Learning conference in a few weeks, and right now, the Deeper Learning MOOC [DLMOOC] is doing a great job of bringing practitioners and researchers together, and that's just from the perspective of someone on the edge who has only managed to replay the late night (in the UK) Hangouts and post a couple of stories. Huge thanks and congratulations to Larry, Rob Riordan and everyone else at High Tech High Grad School of Education, plus of course the other supporting organisations and funders who are making this happen. Here are two of my favourite sessions, in which we hear from students what it's like to be in schools where mindsets and authentic learning are taken seriously, and a panel of researcher/practitioners

Analytics in Higher Education: Establishing a Common Language Title: Analytics in Higher Education: Establishing a Common Language (ID: ELI3026) Author(s): Angela van Barneveld (Purdue University), Kimberly Arnold (Purdue University) and John P. Campbell (Purdue University) Topics: Academic Analytics, Action Analytics, Analytics, Business Analytics, Decision Support Systems, Learning Analytics, Predictive Analytics, Scholarship of Teaching and Learning Origin: ELI White Papers, EDUCAUSE Learning Initiative (ELI) (01/24/2012) Type: Articles, Briefs, Papers, and Reports

Attempts to imagine the future of education often emphasize new technologies-ubiquitous computing devices, flexible classroom designs, and innovative visual displays. But the most dramatic factor shaping the future of higher education is something that we can't actually touch or see: big data and analytics. Basing decisions on data and evidence seems stunningly obvious, and indeed, research indicates that data-driven decision-making improves organizational output and productivity.1 For many leaders in higher education, however, experience and "gut instinct" have a stronger pull.

"Using Assessment for Academic Program Improvement Revised April 2009 "

"Introduction In this discussion paper, we define learning process analytics as a collection of methods that allow teachers and learners to understand what is going on in a' 'learning scenario, i.e. what participants work(ed) on, how they interact(ed), what they produced(ed), what tools they use(ed), in which physical and virtual location, etc. Learning analytics is most often aimed at generating predictive models of general student behavior. So-called academic analytics even aims to improve the system. We are trying to find a solution to a somewhat different problem. In this paper we will focus on improving project-oriented learner-centered designs, i.e. a family of educational designs that include any or some of knowledge-building, writing-to-learn, project-based learning, inquiry learning, problem-based learning and so forth. We will first provide a short literature review of learning process analytics and related frameworks that can help improve the quality of educational scenarios. We will then describe a few project-oriented educational scenarios that are implemented in various programs at the University of Geneva. These examples illustrate the kind of learning scenarios we have in mind and help define the different types of analytics both learners and teachers need. Finally, we present a provisional list of analytics desiderata divided into "wanted tomorrow" and "nice to have in the future"."

Simon Shum - Introducing Analytics at UTS - Academic Writing Analytics (slideshare)

New students are more likely to drop out of online colleges if they take full courseloads than if they enroll part time, according to findings from a research project that is challenging conventional wisdom about student success. But perhaps more important than that potentially game-changing nugget, researchers said, is how the project has chipped away at skepticism in higher education about the power of "big data." Researchers have created a database that measures 33 variables for the online coursework of 640,000 students - a whopping 3 million course-level records. While the work is far from complete, the variables help track student performance and retention across a broad range of demographic factors. The data can show what works at a specific type of institution, and what doesn't. That sort of predictive analytics has long been embraced by corporations, but not so much by the academy. The ongoing data-mining effort, which was kicked off last year with a $1 million grant from the Bill and Melinda Gates Foundation, is being led by WCET, the WICHE Cooperative for Educational Technologies.

July 18, 2012 College Degrees, Designed by the Numbers By Marc Parry Illustration by Randy Lyhus for The Chronicle Campuses are places of intuition and serendipity: A professor senses confusion on a student's face and repeats his point; a student majors in psychology after a roommate takes a course; two freshmen meet on the quad and eventually become husband and wife. Now imagine hard data substituting for happenstance. As Katye Allisone, a freshman at Arizona State University, hunkers down in a computer lab for an 8:35 a.m. math class, the Web-based course watches her back. Answers, scores, pace, click paths-it hoovers up information, like Google. But rather than personalizing search results, data shape Ms. Allisone's class according to her understanding of the material.

Australasian Survey of Student Engagement (AUSSE) Areas measured by the AUSSE The survey instruments used in the AUSSE collect information on around 100 specific learning activities and conditions along with information on individual demographics and educational contexts.The instruments contain items that map onto six student engagement scales: Academic Challenge - the extent to which expectations and assessments challenge students to learn; Active Learning - students' efforts to actively construct knowledge; Student and Staff Interactions - the level and nature of students' contact and interaction with teaching staff; Enriching Educational Experiences - students' participation in broadening educational activities; Supportive Learning Environment - students' feelings of support within the university community; and Work Integrated Learning - integration of employment-focused work experiences into study. The instruments also contain items that map onto seven outcome measures. Average overall grade is captured in a single item, and the other six are composite measures which reflect responses to several items: Higher-Order Thinking - participation in higher-order forms of thinking; General Learning Outcomes - development of general competencies; General Development Outcomes - development of general forms of individual and social development; Career Readiness - preparation for participation in the professional workforce; Average Overall Grade - average overall grade so far in course; Departure Intention - non-graduating students' intentions on not returning to study in the following year; and Overall Satisfaction - students' overall satisfaction with their educational experience.