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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.

Assessment and analytics, supported by information technology, can change institutional culture and drive the transformation in student retention, graduation, and success. U.S. higher education has an extraordinary record of accomplishment in preparing students for leadership, in serving as a wellspring of research and creative endeavor, and in providing public service. Despite this success, colleges and universities are facing an unprecedented set of challenges. To maintain the country's global preeminence, those of us in higher education are being called on to expand the number of students we educate, increase the proportion of students in science, technology, engineering, and mathematics (STEM), and address the pervasive and long-standing underrepresentation of minorities who earn college degrees-all at a time when budgets are being reduced and questions about institutional efficiency and effectiveness are being raised.

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.

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

Jacqueline Bichsel - 2012 EDUCAUSE Many colleges and universities have demonstrated that analytics can help significantly advance an institution in such strategic areas as resource allocation, student success, and finance. Higher education leaders hear about these transformations occurring at other institutions and wonder how their institutions can initiate or build upon their own analytics programs. Some question whether they have the resources, infrastructure, processes, or data for analytics. Some wonder whether their institutions are on par with other in their analytics endeavors. It is within that context that this study set out to assess the current state of analytics in higher education, outline the challenges and barriers to analytics, and provide a basis for benchmarking progress in analytics.

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.

Abstract Few traditions in higher education evoke more controversy, ambivalence, criticism, and, at the same time, support than student evaluation of instruction (SEI). Ostensibly, results from these end-of-course survey instruments serve two main functions: they provide instructors with formative input for improving their teaching, and they serve as the basis for summative profiles of professors' effectiveness through the eyes of their students. In the academy, instructor evaluations also can play out in the high-stakes environments of tenure, promotion, and merit salary increases, making this information particularly important to the professional lives of faculty members. At the research level, the volume of the literature for student ratings impresses even the most casual observer with well over 2,000 studies referenced in the Education Resources Information Center (ERIC) alone (Centra, 2003) and an untold number of additional studies published in educational, psychological, psychometric, and discipline-related journals. There have been numerous attempts at summarizing this work (Algozzine et al., 2004; Gump, 2007; Marsh & Roche, 1997; Pounder, 2007; Wachtel, 1998). Student ratings gained such notoriety that in November 1997 the American Psychologist devoted an entire issue to the topic (Greenwald, 1997). The issue included student ratings articles focusing on stability and reliability, validity, dimensionality, usefulness for improving teaching and learning, and sensitivity to biasing factors, such as the Dr. Fox phenomenon that describes eliciting high student ratings with strategies that reflect little or no relationship to effective teaching practice (Ware & Williams, 1975; Williams & Ware, 1976, 1977).

"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"."

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.

First, the ongoing exploration of the reliability and validity of the psychometric assessment instrument designed to measure and stimulate change in learning power, for which I was one of three originators between 2000 and 2002. To date I have been able to collect large data sets (n=>50,000) and have published reliability and validity statistics in four  peer reviewed journal articles. Second, the application of the concept and assessment of learning power in pedagogy in school, community and corporate sectors, and in particular its contribution to personalisation of learning through authentic enquiry. Third, the contribution of learning power and enquiry to what we know about complexity in education, particularly through the development of systems learning and leadership as a vehicle for organisational transformation. Finally, the application of learning power assessment strategies to the emerging field of learning analytics and agent-based modelling.

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

"SYDNEY LEARNING ANALYTICS RESEARCH GROUP About The Sydney Learning Analytics Research Group (LARG) is a joint venture of the newly established Quality and Analytics Group within the Education Portfolio, and the new Centre for Research on Learning and Innovation connected to the Faculty of Education and Social Work. The key purposes in establishing the new research group are: Capacity building in learning analytics for the benefit of the institution, its students and staff To generate interest and expertise in learning analytics at the University, and build a new network of research colleagues To build a profile for the University of Sydney as a national and international leader in learning analytics LARG was launched at ALASI in late November 2015. The leadership team is actively planning now for the 2016 calendar year and beyond, with several community-building initiatives already in the pipeline, the first being a lecture by George Siemens, and the second is a new conference travel grant (see details below)."

"Connected Learning is a modern pedagogical approach holding that knowledge and learning is distributed across a social, conceptual network. It holds that when people forge, negotiate and nurture connections for themselves (between people, information, knowledge, ideas and concepts), learning is more powerful and sustainable. Ideally, such learning could happen anywhere. People would create Personal Learning Networks within a Community of Inquiry. They would use whatever tools they consider relevant to this process, and connect with whoever they consider relevant to their network... However, this open connectivism is difficult to achieve in our current educational paradigms. How can we help people to teach "in the wild"? Learning Management Systems maintain a dominant position in the education sector, which means that technical support is generally provided only for those teachers who choose safety over openness."

This paper proposes that Social Learning Analytics (SLA) can be usefully thought of as a subset of learning analytics approaches. SLA focuses on how learners build knowledge together in their cultural and social settings. In the context of online social learning, it takes into account both formal and informal educational environments, including networks and communities. The paper introduces the broad rationale for SLA by reviewing some of the key drivers that make social learning so important today. Five forms of SLA are identified, including those which are inherently social, and others which have social dimensions. The paper goes on to describe early work towards implementing these analytics on SocialLearn, an online learning space in use at the UK's Open University, and the challenges that this is raising. This work takes an iterative approach to analytics, encouraging learners to respond to and help to shape not only the analytics but also their associated recommendations.

Flare at Purdue in October    Hi everyone. Can someone provide more information for the upcoming SoLAR FLARE event at Purdue in October? Thanks, Kelvin Bentley By Kelvin Bentley  - May 14 - 2 new of 2 messages - Report as spam     EDUCAUSE Survey on Analytics - Looking for International Input    Colleagues, EDUCAUSE is soliciting input on analytics in higher education. They have currently sent email to their current members, but are looking for additional participation from the international community. We would greatly appreciate if you could complete the survey below. -- john... more » By John Campbell - Purdue  - May 11 - 2 new of 2 messages - Report as spam     CFP: #Influence12: Symposium & Workshop on Measuring Influence on Social Media    Hi Everyone, If you are interested in Learning Analytics and Social Media, I invite you to submit a short position paper or poster to the Symposium & Workshop on Measuring Influence on Social Media. The event is set for September 28-29, 2012 in beautiful Halifax, Nova Scotia, Canada. All submissions are due *June 15, 2012*.... more » By Anatoliy Gruzd  - May 11 - 2 new of 2 messages - Report as spam     LA beginnings    Learning Analytics isn't really new, it is just getting more publicity now as a result of the buzz word name change. Institutions have been collecting data about students for a long time, but only a few people dealt with the data. Instructors kept gradebooks and many tracked student progress locally - by hand. What's new about Learning... more »

The SEI program is working with the teaching and learning community to gather evidence of the impact of instructional innovations and current practices and to help evaluate the results. The calls for more accountability in higher education, the shrinking budgets that often force larger class sizes, and the pressures to increase degree-completion rates are all raising the stakes for colleges and universities today, especially with respect to the instructional enterprise. As resources shrink, teaching and learning is becoming the key point of accountability. The evaluation of instructional practice would thus seem to be an obvious response to such pressures, with institutions implementing systematic programs of evaluation in teaching and learning, especially of instructional innovations.

National Survey of Student Engagement What is student engagement? Student engagement represents two critical features of collegiate quality. The first is the amount of time and effort students put into their studies and other educationally purposeful activities. The second is how the institution deploys its resources and organizes the curriculum and other learning opportunities to get students to participate in activities that decades of research studies show are linked to student learning. What does NSSE do? Through its student survey, The College Student Report, NSSE annually collects information at hundreds of four-year colleges and universities about student participation in programs and activities that institutions provide for their learning and personal development. The results provide an estimate of how undergraduates spend their time and what they gain from attending college. NSSE provides participating institutions a variety of reports that compare their students' responses with those of students at self-selected groups of comparison institutions. Comparisons are available for individual survey questions and the five NSSE Benchmarks of Effective Educational Practice. Each November, NSSE also publishes its Annual Results, which reports topical research and trends in student engagement results. NSSE researchers also present and publish research findings throughout the year.

What is LOCO-Analyst? LOCO-Analyst is an educational tool aimed at providing teachers with feedback on the relevant aspects of the learning process taking place in a web-based learning environment, and thus helps them improve the content and the structure of their web-based courses. LOCO-Analyst aims at providing teachers with feedback regarding: *  all kinds of activities their students performed and/or took part in during the learning process, *  the usage and the comprehensibility of the learning content they had prepared and deployed in the LCMS, *  contextualized social interactions among students (i.e., social networking) in the virtual learning environment. This Web site provides some basic information about LOCO-Analyst, its functionalities and implementation. In addition, you can watch videos illustrating the tool's functionalities. You can also learn about the LOCO (Learning Object Context Ontologies) ontological framework that lies beneath the LOCO-Analyst tool and download the ontologies of this framework.

Information graphics or infographics are graphic visual representations of information, data or knowledge. These graphics present complex information quickly and clearly,[1] such as in signs, maps, journalism, technical writing, and education. With an information graphic, computer scientists, mathematicians, and statisticians develop and communicate concepts using a single symbol to process information.

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.