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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

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.

Recommended for You March 16, 2012 - 3:00am By Steve Kolowich Completing assignments and sitting through exams can be stressful. But when it comes to being graded the waiting is often the hardest part. This is perhaps most true at the end of a semester, as students wait for their instructors to reduce months of work into a series of letter grades that will stay on the books forever. But at Austin Peay State University, students do not have to wait for the end of a semester to learn their grade averages. Thanks to a new technology, pioneered by the university's provost, they do not even have to wait for the semester to start.

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.

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.

REFRAME REFRAME is a university-wide project reconceptualising QUT's evaluation of learning and teaching. REFRAME is fundamentally reconsidering QUT's overall approach to evaluating learning and teaching. Our aim is to develop a sophisticated risk-based system to gather, analyse and respond to data along with a broader set of user-centered resources. The objective is to provide individuals and teams with the tools, support and reporting they need to meaningfully reflect upon, review and improve teaching, student learning and the curriculum. The approach will be informed by feedback from the university community, practices in other institutions and the literature, and will, as far as possible, be 'future-proofed' through awareness of emergent evaluation trends and tools. Central to REFRAME is the consideration of the purpose of evaluation and the features that a future approach should consider.

"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)."

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.

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.

People | Member | Simon Buckingham Shum Snr Lecturer in Knowledge Media I am fundamentally interested in technologies for sensemaking, specifically, which structure discourse to assist reflection and analysis. Examples: D3E, Compendium, ClaiMaker and Cohere.

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.

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.

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.

CORE IDEAS We decided on the name Learning Emergence because we are very much learning about emergence and complex systems phenomena ourselves, even as we develop our thinking on learning as an emergent, systemic phenomenon in different contexts. We must shift to a new paradigm for learning in schools, universities and the workplace which addresses the challenges of the 21st Century. Society needs learners who can cope with intellectual, ethical and emotional complexity of an unprecedented nature. Learning Emergence partners share an overarching focus on deep, systemic learning and leadership - the pro-active engagement of learners and leaders in their own authentic learning journey, in the context of relationship and community. We work at the intersection of (1) deep learning and sensemaking, (2) leadership, (3) complex systems, and (4) technology:

"Personal reflections on 2 workshops and a lecture with Tony Bryk (Carnegie Foundation for the Advancement of Teaching), hosted last week by Ruth Deakin Crick at University of Bristol. What follows after a brief introduction to the concept of NICs, are my thoughts on the intersection of NICs with Learning Analytics. I made a number of connection points between the features of the DEED+NIC approach, and learning analytics, which I'll highlight in green."

"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"."

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.

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.

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.

Learning networks, crowds and communities Full Text: PDF Author: Caroline Haythornthwaite University of British Columbia, Vancouver, BC Who we learn from, where and when is dramatically affected by the reach of the Internet. From learning for formal education to learning for pleasure, we look to the web early and often for our data and knowledge needs, but also for places and spaces where we can collaborate, contribute to, and create learning and knowledge communities. Based on the keynote presentation given at the first Learning Analytics and Knowledge Conference held in 2011 in Banff, Alberta, this paper explores a social network perspective on learning with reference to social network principles and studies by the author. The paper explores the ways a social network perspective can be used to examine learning, with attention to the structure and dynamics of online learning networks, and emerging configurations such as online crowds and communities.