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George Bradford

College Degrees, Designed by the Numbers - Technology - The Chronicle of Higher Education - 0 views

  • Arizona State's retention rate rose to 84 percent from 77 percent in recent years, a change that the provost credits largely to eAdvisor.
  • Mr. Lange and his colleagues had found that by the eighth day of class, they could predict, with 70-percent accuracy, whether a student would score a C or better. Mr. Lange built a system, rolled out in 2009, that sent professors frequently updated alerts about how well each student was predicted to do, based on course performance and online behavior.
  • Rio Salado knows from its database that students who hand in late assignments and don't log in frequently often fail or withdraw from a course. So the software is more likely to throw up a red flag for current students with those characteristics.
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  • And in a cautionary tale about technical glitches, the college began sharing grade predictions with students last summer, hoping to encourage those lagging behind to step up, but had to shut the alerts down in the spring. Course revisions had skewed the calculations, and some predictions were found to be inaccurate. An internal analysis found no increase in the number of students dropping classes. An improved system is promised for the fall.
  • His software borrows a page from Netflix. It melds each student's transcript with thousands of past students' grades and standardized-test scores to make suggestions. When students log into the online portal, they see 10 "Course Suggestions for You," ranked on a five-star scale. For, say, a health-and-human-performance major, kinesiology might get five stars, as the next class needed for her major. Physics might also top the list, to satisfy a science requirement in the core curriculum.
  • Behind those recommendations is a complex algorithm, but the basics are simple enough. Degree requirements figure in the calculations. So do classes that can be used in many programs, like freshman writing. And the software bumps up courses for which a student might have a talent, by mining their records—grades, high-school grade-point average, ACT scores—and those of others who walked this path before.
  • The software sifts through a database of hundreds of thousands of grades other students have received. It analyzes the historical data to figure out how much weight to assign each piece of the health major's own academic record in forecasting how she will do in a particular course. Success in math is strongly predictive of success in physics, for example. So if her transcript and ACT score indicate a history of doing well in math, physics would probably be recommended over biology, though both satisfy the same core science requirement.
  • Every year, students in Tennessee lose their state scholarships because they fall a hair short of the GPA cutoff, Mr. Denley says, a financial swing that "massively changes their likelihood of graduating."
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    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.
George Bradford

Open Research Online - Contested Collective Intelligence: rationale, technologies, and ... - 0 views

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    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.
George Bradford

Learning process analytics - EduTech Wiki - 1 views

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    "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"."
George Bradford

Open Research Online - Learning dispositions and transferable competencies: pedagogy, m... - 0 views

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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.
George Bradford

Learning Dispositions and Transferable Competencies: Pedagogy, Modelling, and Learning ... - 0 views

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    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.
George Bradford

Cohere >>> make the connection - 0 views

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    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.
George Bradford

Dr Ruth Deakin Crick - Graduate School of Education - 0 views

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    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.
George Bradford

About | SNAPP - Social Networks Adapting Pedagogical Practice - 3 views

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    "The Social Networks Adapting Pedagogical Practice (SNAPP) tool performs real-time social network analysis and visualization of discussion forum activity within popular commercial and open source Learning Management Systems (LMS). SNAPP essentially serves as a diagnostic instrument, allowing teaching staff to evaluate student behavioral patterns against learning activity design objectives and intervene as required a timely manner. Valuable interaction data is stored within a discussion forum but from the default threaded display of messages it is difficult to determine the level and direction of activity between participants. SNAPP infers relationship ties from the post-reply data and renders a social network diagram below the forum thread. The social network visualization can be filtered based upon user activity and social network data can be exported for further analysis in NetDraw. SNAPP integrates seamlessly with a variety of Learning Management Systems (Blackboard, Moodle and Desire2Learn) and must be triggered while a forum thread is displayed in a Web browser."
George Bradford

Information graphics - Wikipedia, the free encyclopedia - 0 views

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    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.
George Bradford

Assessment and Analytics in Institutional Transformation (EDUCAUSE Review) | EDUCAUSE - 0 views

  • At the University of Maryland, Baltimore County (UMBC), we believe that process is an important factor in creating cultural change. We thus approach transformational initiatives by using the same scholarly rigor that we expect of any researcher. This involves (1) reviewing the literature and prior work in the area, (2) identifying critical factors and variables, (3) collecting data associated with these critical factors, (4) using rigorous statistical analysis and modeling of the question and factors, (5) developing hypotheses to influence the critical factors, and (6) collecting data based on the changes and assessing the results.
  • among predominantly white higher education institutions in the United States, UMBC has become the leading producer of African-American bachelor’s degree recipients who go on to earn Ph.D.’s in STEM fields. The program has been recognized by the National Science Foundation and the National Academies as a national model.
  • UMBC has recently begun a major effort focused on the success of transfer students in STEM majors. This effort, with pilot funding from the Bill and Melinda Gates Foundation, will look at how universities can partner with community colleges to prepare their graduates to successfully complete a bachelor’s degree in a STEM field.
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  • Too often, IT organizations try to help by providing an analytics “dashboard” designed by a vendor that doesn’t know the institution. As a result, the dashboard indicators don’t focus on those key factors most needed at the institution and quickly become window-dressing.
  • IT organizations can support assessment by showing how data in separate systems can become very useful when captured and correlated. For example, UMBC has spent considerable effort to develop a reporting system based on our learning management system (LMS) data. This effort, led from within the IT organization, has helped the institution find new insights into the way faculty and students are using the LMS and has helped us improve the services we offer. We are now working to integrate this data into our institutional data warehouse and are leveraging access to important demographic data to better assess student risk factors and develop interventions.
  • the purpose of learning analytics is “to observe and understand learning behaviors in order to enable appropriate interventions.
  • the 1st International Conference on Learning Analytics and Knowledge (LAK) was held in Banff, Alberta, Canada, in early 2011 (https://tekri.athabascau.ca/analytics/)
  • At UMBC, we are using analytics and assessment to shine a light on students’ performance and behavior and to support teaching effectiveness. What has made the use of analytics and assessment particularly effective on our campus has been the insistence that all groups—faculty, staff, and students—take ownership of the challenge involving student performance and persistence.
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    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.
George Bradford

[!!!] Penetrating the Fog: Analytics in Learning and Education (EDUCAUSE Review) | EDUC... - 0 views

  • Continued growth in the amount of data creates an environment in which new or novel approaches are required to understand the patterns of value that exist within the data.
  • learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs.
  • Academic analytics, in contrast, is the application of business intelligence in education and emphasizes analytics at institutional, regional, and international levels.
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  • Course-level:
  • Educational data-mining
  • Intelligent curriculum
  • Adaptive content
  • the University of Maryland, Baltimore County (UMBC) Check My Activity tool, allows learners to “compare their own activity . . . against an anonymous summary of their course peers.
  • Mobile devices
  • social media monitoring tools (e.g., Radian6)
  • Analytics in education must be transformative, altering existing teaching, learning, and assessment processes, academic work, and administration.
    • George Bradford
       
      See Bradford - Brief vision of the semantic web as being used to support future learning: http://heybradfords.com/moonlight/research-resources/SemWeb_EducatorsVision 
    • George Bradford
       
      See Peter Goodyear's work on the Ecology of Sustainable e-Learning in Education.
  • How “real time” should analytics be in classroom settings?
  • Adaptive learning
  • EDUCAUSE Review, vol. 46, no. 5 (September/October 2011)
  • Penetrating the Fog: Analytics in Learning and Education
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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.
George Bradford

[!!!!] Social Learning Analytics - Technical Report (pdf) - 0 views

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    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. 
George Bradford

Stunning Infographics and Data Visualization - Noupe - 1 views

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    Creating an effective infographic requires both artistic sense and a clear vision of what to tell the audience. The following are some cool infographics we have collected. Some are colorful, some are simple, but all are informative and visually pleasing. Not only do they provide information in a format that is easy to understand, but they are also artistic creations in their own right.
George Bradford

Data Visualization: Modern Approaches - Smashing Magazine | Smashing Magazine - 0 views

  • 2. Displaying News
  • Digg Stack 15: Digg stories arrange themselves as stack as users digg them. The more diggs a story gets, the larger is the stack.
  • Let’s take a look at the most interesting modern approaches to data visualization as well as related articles, resources and tools.
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  • an application that visually reflects the constantly changing landscape of the Google News news aggregator. The size of data blocks is defined by their popularity at the moment.
  • Digg stories arrange themselves as stack as users digg them. The more diggs a story gets, the larger is the stack.
  • a typographic book search, collects the information from Amazon and presents it in the form of keyword you’ve provided.
  • uses visual hills (spikes) to emphasize the density of American population in its map.
  • lets you explore the behavior of your visitors with a heat map. More popular sections, which are clicked more often, are highlighted as “warm” – in red color.
  • Eric Blue provides some references to unusual Data Visualization methods.
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    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.
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