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Theoretical and empirical evidence in the learning sciences substantiates the view that deep engagement in learning is a function of a complex combination of learners' identities, dispositions, values, attitudes and skills. When these are fragile, learners struggle to achieve their potential in conventional assessments, and critically, are not prepared for the novelty and complexity of the challenges they will meet in the workplace, and the many other spheres of life which require personal qualities such as resilience, critical thinking and collaboration skills. To date, the learning analytics research and development communities have not addressed how these complex concepts can be modelled and analysed, and how more traditional social science data analysis can support and be enhanced by learning analytics. We report progress in the design and implementation of learning analytics based on a research validated multidimensional construct termed "learning power". We describe, for the first time, a learning analytics infrastructure for gathering data at scale, managing stakeholder permissions, the range of analytics that it supports from real time summaries to exploratory research, and a particular visual analytic which has been shown to have demonstrable impact on learners. We conclude by summarising the ongoing research and development programme and identifying the challenges of integrating traditional social science research, with learning analytics and modelling.

"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)."

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.

Simon Buckingham Shum Ruth Deakin Crick 2012 (In review) Theoretical and empirical evidence in the learning sciences  substantiates the view that deep engagement in learning is a  function of a  combination of learners' dispositions,  values,  attitudes and skills. When these are fragile, learners struggle to  achieve their potential in conventional assessments, and critically,  are not prepared for the novelty and complexity of the challenges  they will meet in the workplace, and the many other spheres of  life which require personal qualities such as resilience, critical  thinking and collaboration skills. To date, the learning analytics  research and development communities have not addressed how  these complex concepts can be modelled and analysed. We report  progress in the design and implementation of learning analytics  based on an empirically validated  multidimensional construct  termed  "learning power". We describe a  learning analytics  infrastructure  for gathering data at scale, managing stakeholder  permissions, the range of analytics that it supports from real time  summaries to exploratory research, and a particular visual analytic which has been shown to have demonstrable impact on learners.  We conclude by  summarising the ongoing research and  development programme.

We propose the concept of Contested Collective Intelligence (CCI) as a distinctive subset of the broader Collective Intelligence design space. CCI is relevant to the many organizational contexts in which it is important to work with contested knowledge, for instance, due to different intellectual traditions, competing organizational objectives, information overload or ambiguous environmental signals. The CCI challenge is to design sociotechnical infrastructures to augment such organizational capability. Since documents are often the starting points for contested discourse, and discourse markers provide a powerful cue to the presence of claims, contrasting ideas and argumentation, discourse and rhetoric provide an annotation focus in our approach to CCI. Research in sensemaking, computer-supported discourse and rhetorical text analysis motivate a conceptual framework for the combined human and machine annotation of texts with this specific focus. This conception is explored through two tools: a social-semantic web application for human annotation and knowledge mapping (Cohere), plus the discourse analysis component in a textual analysis software tool (Xerox Incremental Parser: XIP). As a step towards an integrated platform, we report a case study in which a document corpus underwent independent human and machine analysis, providing quantitative and qualitative insight into their respective contributions. A promising finding is that significant contributions were signalled by authors via explicit rhetorical moves, which both human analysts and XIP could readily identify. Since working with contested knowledge is at the heart of CCI, the evidence that automatic detection of contrasting ideas in texts is possible through rhetorical discourse analysis is progress towards the effective use of automatic discourse analysis in the CCI framework.

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.

From the Semantic Web to social machines: A research challenge for AI on the World Wide Web Jim Hendler, Tim Berners-Lee Abstract The advent of social computing on the Web has led to a new generation of Web applications that are powerful and world-changing. However, we argue that we are just at the beginning of this age of "social machines" and that their continued evolution and growth requires the cooperation of Web and AI researchers. In this paper, we show how the growing Semantic Web provides necessary support for these technologies, outline the challenges we see in bringing the technology to the next level, and propose some starting places for the research.

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

Drawing on sociocultural discourse analysis and argumentation theory, we motivate a focus on learners' discourse as a promising site for identifying patterns of activity which correspond to meaningful learning and knowledge construction. However, software platforms must gain access to qualitative information about the rhetorical dimensions to discourse contributions to enable such analytics. This is difficult to extract from naturally occurring text, but the emergence of more-structured annotation and deliberation platforms for learning makes such information available. Using the Cohere web application as a research vehicle, we present examples of analytics at the level of individual learners and groups, showing conceptual and social network patterns, which we propose as indicators of meaningful learning.

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.

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).

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.

While generic web analytics tend to focus on easily harvested quantitative data, Learning Analytics will often seek qualitative understanding of the context and meaning of this information. This is critical in the case of dialogue, which may be employed to share knowledge and jointly construct understandings, but which also involves many superficial exchanges. Previous studies have validated a particular pattern of "exploratory dialogue" in learning environments to signify sharing, challenge, evaluation and careful consideration by participants. This study investigates the use of sociocultural discourse analysis to analyse synchronous text chat during an online conference. Key words and phrases indicative of exploratory dialogue were identified in these exchanges, and peaks of exploratory dialogue were associated with periods set aside for discussion and keynote speakers. Fewer individuals posted at these times, but meaningful discussion outweighed trivial exchanges. If further analysis confirms the validity of these markers as learning analytics, they could be used by recommendation engines to support learners and teachers in locating dialogue exchanges where deeper learning appears to be taking place.

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.

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.

Measuring Teacher Effectiveness Significant State Data Capacity is Required to Measure and Improve Teacher Effectiveness  States Increasingly Focus on Improving Teacher Effectiveness: There is significant activity at the local, state, and federal levels to  measure and improve teacher effectiveness, with an unprecedented focus on the use of student achievement as a primary indicator of  effectiveness. > 23 states require that teacher evaluations include evidence of student learning in the form of student growth and/or value-added data (NCTQ, 2011). > 17 states and DC have adopted legislation or regulations that specifically require student achievement and/or student growth to "significantly" inform or be the primary criterion in teacher evaluations(NCTQ, 2011).  States Need Significant Data Capacity to Do This Work: These policy changes have significant data implications. > The linchpin of all these efforts is that states must reliably link students and teachers in ways that capture the complex connections that  exist in schools. > If such data is to be used for high stakes decisions-such as hiring, firing, and tenure-it must be accepted as valid, reliable, and fair. > Teacher effectiveness data can be leveraged to target professional development, inform staffing assignments, tailor classroom instruction,  reflect on practice, support research, and otherwise support teachers.  Federal Policies Are Accelerating State and Local Efforts: Federal policies increasingly support states' efforts to use student  achievement data to measure teacher effectiveness. > Various competitive grant funds, including the Race to the Top grants and the Teacher Incentive Fund, require states to implement teacher  and principal evaluation systems that take student data into account.  > States applying for NCLB waivers, including the 11 that submitted requests in November 2011, must commit to implementing teacher and  principal evaluation and support systems. > P

Listing visualizations of data. IBM Research and the IBM Cognos experiment.

About Cohere The Web is about IDEAS+PEOPLE. Cohere is a visual tool to create, connect and share Ideas. Back them up with websites. Support or challenge them. Embed them to spread virally. Discover who - literally - connects with your thinking. Publish ideas and optionally add relevant websites Weave webs of meaningful connections between ideas: your own and the world's Discover new ideas and people We experience the information ocean as streams of media fragments, flowing past us in every modality. To make sense of these, learners, researchers and analysts must organise them into coherent patterns. Cohere is an idea management tool for you to annotate URLs with ideas, and weave meaningful connections between ideas for personal, team or social use. Key Features Annotate a URL with any number of Ideas, or vice-versa. Visualize your network as it grows Make connections between your Ideas, or Ideas that anyone else has made public or shared with you via a common Group Use Groups to organise your Ideas and Connections by project, and to manage access-rights Import your data as RSS feeds (eg. bookmarks or blog posts), to convert them to Ideas, ready for connecting Use the RESTful API services to query, edit and mashup data from other tools Learn More Subscribe to our Blog to track developments as they happen. Read this article to learn more about the design of Cohere to support dialogue and debate.

"Rethinking Educaitonal Leadership: mapping the terrain of leadership in learning organisations in conditions of complexity, diversity and change Jul 26 2015 0 Rethinking Educational Leadership: an Open Space Symposium The purpose Symposium was to provide experienced practitioners and researchers with an opportunity to bring fresh thinking to the current challenges facing school leaders and to generate new ideas about leadership development. The Open Space Technology provided a means of capturing the collective intelligence generated by the group in response to the core question. This post reports on the outcomes of this Open Space Symposium which was held in 2013. "