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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.

"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"."

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:

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.

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.

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.

"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."

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)."

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.

"Developing Student Learning Outcomes Student learning outcome (SLO) statements take the program learning goals and focus on how students can demonstrate that the goals are being met. In other words, SLOs answer the question: how can graduates from this program demonstrate they have the needed/stated knowledge, skills, and/or values. SLOs are clear, concise statements that describe how students can demonstrate their mastery of program learning goals. Each student learning outcome statement must be measurable. Measures are applied to student work and may include student assignments, work samples, tests, etc. measuring student ability/skill, knowledge, or attitude/value."

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.

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.

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.

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.

Technical Report KMI-11-01 June 2011 Simon Buckingham Shum and Rebecca Ferguson Abstract: We propose that the design and implementation of effective Social Learning Analytics presents significant challenges and opportunities for both research and enterprise, in three important respects. The first is the challenge of implementing analytics that have pedagogical and ethical integrity, in a context where power and control over data is now of primary importance. The second challenge is that the educational landscape is extraordinarily turbulent at present, in no small part due to technological drivers. Online social learning is emerging as a significant phenomenon for a variety of reasons, which we review, in order to motivate the concept of social learning, and ways of conceiving social learning environments as distinct from other social platforms. This sets the context for the third challenge, namely, to understand different types of Social Learning Analytic, each of which has specific technical and pedagogical challenges. We propose an initial taxonomy of five types. We conclude by considering potential futures for Social Learning Analytics, if the drivers and trends reviewed continue, and the prospect of solutions to some of the concerns that institution-centric learning analytics may provoke. 

"Awards for Excellence in Learning Analytics: Submissions The LA SIG operates an Awards program recognising excellence in the practical application of LA to enhance learning and teaching. A key driver for the Awards program is to create and share resources about effective LA practices. We want to give a voice to all who are working with LA to improve learning and teaching - whatever the scale of their endeavours.  To this end, all presentations from Award applicants are available below for viewing. (Submissions closed on 11 September).  We believe these presentations will form an important resource library around the use of LA in tertiary education across Australasia and New Zealand. You may view the awards submission criteria and other background information on the awards in the SIG Awards Program Information (PDF) and to find out more about LA-SIG activities, go here."

educause learning initiative 2012 online spring focus session: learning analytics Teaching and Learning Community's ideas

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.

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.