Early Alert Program
The Early Alert classroom assistance program at Sinclair Community College is an intervention program teaming faculty, counselors, and advisors together in order to promote the success of students facing challenges.
An Overview:
Early Alert is an intervention program that allows for faculty to notify advisors/counselors of issues that may affect the success of a student.
It is a simple way of assisting students in difficulty find the help they need while taking very little time.
Web-based Early Alert notifications are easy ways to promote the retention efforts of the college and the success of students.
Utilized currently in all DEV courses, English 111, select Math courses, and SCC 101 courses.
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 »
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
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.
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:
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.
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.
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.
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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.
One of my areas of interest that has grown over the last couple years has been data visualization. I'm a visually-oriented learner, and I look forward to seeing any techniques, illustrations, or technologies that:
1) Allow people to assimilate information as fast as possible.
2) Deepen understanding of knowledge by visually illustrating data in new and interesting ways. There is nothing like having an intellectual epiphony after looking at a picture for a few seconds (pictures can definitely be worth a thousand words).
3) Present information in an aesthetically pleasing way. Or, in extreme examples, inspire a sense of awe!
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.
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.
The terms Data visualization and Infographics are used interchangeably, the former means the study of visual representation of data and the latter is its representation per se.
50 MOST STUNNING EXAMPLES OF DATA VISUALIZATION AND INFOGRAPHICS
Posted by Richie on Thursday, April 15, 2010
"A picture is worth a thousand words", if I had a penny for every time I heard that!! There is so much data in the world today that it has become impossible for us to analyze them with patience. Data as we perceive it, need not be boring, bland and cumbersome to remember. To make complex things seem simple, is Creativity and using pictures to represent data has been an age old method to analyze data in a fun way.
From navigating the web in an entirely new dimension to understanding how the human brain works; from peeking into how Google has evolved to analyzing the inner working of the geeky mind, Infographics has completely changed the way we view content and visualize data.
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.
Learning Analytics: Dream, Nightmare, or Fairydust?
From today's keynote at Ascilite 2011, here's the podcast plus the slides. I am grateful to Gary, Renee and everyone else at Ascilite for their understanding and flexibility, since after months of planning this trip, unfortunately I could not be there in person after my father passed away last weekend.
For those of you who like to download and watch offline: podcast [Hi-Res version: 93.3Mb] + slides [PPTX/PDF]
For detailed descriptions of work presented here, see other posts tagged learning analytics and the references below.
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.
Researchers have created a database that measures 33 variables for the online coursework of 640,000 students – a whopping 3 million course-level records.
Project Participants
American Public University System
Community College System of Colorado
Rio Salado College
University of Hawaii System
University of Illinois-Springfield
University of Phoenix
“What the data seem to suggest, however, is that for students who seem to have a high propensity of dropping out of an online course-based program, the fewer courses they take initially, the better-off they are.”
Phil Ice, vice president of research and development for the American Public University System and the project’s lead investigator.
Predictive Analytics Reporting Framework
Rio Salado, for example, has used the database to create a student performance tracking system.
The two-year college, which is based in Arizona, has a particularly strong online presence for a community college – 43,000 of its students are enrolled in online programs. The new tracking system allows instructors to see a red, yellow or green light for each student’s performance. And students can see their own tracking lights.
It measures student engagement through their Web interactions, how often they look at textbooks and whether they respond to feedback from instructors, all in addition to their performance on coursework.
The data set has the potential to give institutions sophisticated information about small subsets of students – such as which academic programs are best suited for a 25-year-old male Latino with strength in mathematics
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.