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"Epistemic modal logic is a subfield of modal logic that is concerned with reasoning about knowledge. While epistemology has a long philosophical tradition dating back to Ancient Greece, epistemic logic is a much more recent development with applications in many fields, including philosophy, theoretical computer science, artificial intelligence, economics and linguistics. While philosophers since Aristotle have discussed modal logic, and Medieval philosophers such as Ockham and Duns Scotus developed many of their observations, it was C. I. Lewis who created the first symbolic and systematic approach to the topic, in 1912. It continued to mature as a field, reaching its modern form in 1963 with the work of Kripke."

"The American Statistical Association seeks to embrace science's inherent complexity and push for more data transparency by rejecting a common, oversimplified measure of statistical significance. March 15, 2016 By Colleen Flaherty Is the tyrannical reign of the P value finally ending (if it was ever tyrannical at all)? An unprecedented statement from the American Statistical Association seeks to usher in a "post-P

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.

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.

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.

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.

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 »

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.

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.

Data presentation can be beautiful, elegant and descriptive. There is a variety of conventional ways to visualize data - tables, histograms, pie charts and bar graphs are being used every day, in every project and on every possible occasion. However, to convey a message to your readers effectively, sometimes you need more than just a simple pie chart of your results. In fact, there are much better, profound, creative and absolutely fascinating ways to visualize data. Many of them might become ubiquitous in the next few years.

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

"   Welcome to the Program Evaluation Standards, 3rd Edition   Standards Names and Statements Errata Sheet for the book   After seven years of systematic effort and much study, the 3rd edition of the Program Evaluation Standards was published this fall by Sage Publishers: http://www.sagepub.com/booksProdDesc.nav?prodId=Book230597&_requestid=255617. The development process relied on formal and informal needs assessments, reviews of existing scholarship, and the involvement of more than 400 stakeholders in national and international reviews, field trials, and national hearings. It's the first revision of the standards in 17 years. This third edition is similar to the previous two editions (1981, 1994) in many respects, for example, the book is organized into the same four dimensions of evaluation quality (utility, feasibility, propriety, and accuracy). It also still includes the popular and useful "Functional Table of Standards," a glossary, extensive documentation, information about how to apply the standards, and numerous case applications."

"This toolkit assembled by the Social Media Lab seeks to provide an overview of some of the many open access tools available for the study and analysis of social media and online communities. The table below presents the tools in alphabetical order and highlights the social media platforms they support and the features they provide. The list is not exhaustive and will be reviewed, updated, and enhanced in the coming months. The tools in this list offer varying degrees of analysis."

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.

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.