In scholarly and scientific publishing, altmetrics are non-traditional metrics proposed as an alternative to more traditional citation impact metrics, such as impact factor and h-index. The term altmetrics was proposed in 2010, as a generalization of article level metrics, and has its roots in the #altmetrics hashtag. Although altmetrics are often thought of as metrics about articles, they can be applied to people, journals, books, data sets, presentations, videos, source code repositories, web pages, etc. They are related to Webometrics, which had similar goals but evolved before the social web. Altmetrics did not originally cover citation counts. It also covers other aspects of the impact of a work, such as how many data and knowledge bases refer to it, article views, downloads, or mentions in social media and news media.
more on analytics and metrics in education in this IMS blog
The data shared in June by the Office for Civil Rights, which compiled it from a 2013-2014 survey completed by nearly every school district and school in the United States. new is a report from Attendance Works and the Everyone Graduates Center that encourages schools and districts to use their own data to pinpoint ways to take on the challenge of chronic absenteeism.
The first is research that shows that missing that much school is correlated with “lower academic performance and dropping out.” Second, it also helps in identifying students earlier in the semester in order to get a jump on possible interventions.
The report offers a six-step process for using data tied to chronic absence in order to reduce the problem.
The first step is investing in “consistent and accurate data.” That’s where the definition comes in — to make sure people have a “clear understanding” and so that it can be used “across states and districts” with school years that vary in length. The same step also requires “clarifying what counts as a day of attendance or absence.”
The second step is to use the data to understand what the need is and who needs support in getting to school. This phase could involve defining multiple tiers of chronic absenteeism (at-risk, moderate or severe), and then analyzing the data to see if there are differences by student sub-population — grade, ethnicity, special education, gender, free and reduced price lunch, neighborhood or other criteria that require special kinds of intervention.
Step three asks schools and districts to use the data to identify places getting good results. By comparing chronic absence rates across the district or against schools with similar demographics, the “positive outliers” may surface, showing people that the problem isn’t unstoppable but something that can be addressed for the better.
Steps five and six call on schools and districts to help people understand why the absences are happening, develop ways to address the problem.
As you may be aware that TERI is a global think-tank knowledge driven organisation working in the field of Energy, Environment and Sustainable Development. TERI is organising it’s one of the flagship event ICDL 2016 from
13 to 16 December, 2016 at India Habitat Center, Lodhi Road, New Delhi. The theme of the conference is “Smart Future: Knowledge Trends that will Change the World”. (URL: http://www.teriin.org/events/icdl/)
As we understand that in the current scenario all enterprises are heading towards Digital Transformation, which derives business value for an effective decision making process. To be a part of this transformation strategy, all stakeholders at various levels should be aware of certain pertinent components, which are mentioned below. This conference is a unique platform to brainstorm and network with leading speakers and digital luminaries. Some of the major thrust areas to be covered are:
Innovation and Knowledge Management
Big Data and Analytics
Social Media and Analytics
Internet of Things (IoT)
To get yourself and your team to engage in one of these issues, we would request you to kindly share your skills, expertise and experiences with audiences in this thought provoking and stimulating interactive platform of ICDL 2016.
The U.S. Department of Education has increasingly encouraged and funded states to collect and analyze information about students: grades, state test scores, attendance, behavior, lateness, graduation rates and school climate measures like surveys of student engagement.
The argument in favor of all this is that the more we know about how students are doing, the better we can target instruction and other interventions. And sharing that information with parents and the community at large is crucial. It can motivate big changes.
what might be lost when schools focus too much on data. Here are five arguments against the excesses of data-driven instruction.
The National Education Policy Center releases annual reports on commercialization and marketing in public schools. In its most recent report in May, researchers there raised concerns about targeted marketing to students using computers for schoolwork and homework. Companies like Google pledge not to track the content of schoolwork for the purposes of advertising. But in reality these boundaries can be a lot more porous. For example, a high school student profiled in the NEPC report often consulted commercial programs like dictionary.com and Sparknotes: “Once when she had been looking at shoes, she mentioned, an ad for shoes appeared in the middle of a Sparknotes chapter summary.”
4) Missing What Data Can’t Capture
Computer systems are most comfortable recording and analyzing quantifiable, structured data. The number of absences in a semester, say; or a three-digit score on a multiple-choice test that can be graded by machine, where every question has just one right answer.
5) Exposing Students’ “Permanent Records”
In the past few years several states have passed laws banning employers from looking at the credit reports of job applicants. Employers want people who are reliable and responsible. But privacy advocates argue that a past medical issue or even a bankruptcy shouldn’t unfairly dun a person who needs a fresh start.
There are two types of Lexile measures: a person’s reading ability and the text’s difficulty. Students who are tested against state standards receive a Lexile reader measure from the Kansas Reading Assessment. Books and other texts receive a Lexile text measure from a MetaMetrics software tool called the Lexile Analyzer, which describes the book’s reading demand or complexity. When used together, the two measures are intended to help match a reader with reading material that is at an appropriate difficulty or will at least help give an idea of how well a reader should comprehend text. The reader should encounter some level of difficulty with the text, but not enough to get frustrated. The Lexile reader measure is used to monitor reader progress.
My note: is this another way / attempt to replace humans as educators? Or it is a supplemental approach to improve students’ reading abilities.
At a Ford Foundation conference dubbed Fairness by Design, officials, academics and advocates discussed how to address the problem of encoding human bias in algorithmic analysis. The White House recently issued a report on the topic to accelerate research into the issue.
U.S. CTO Megan Smith said the government has been “creating a seat for these techies,” but that training future generations of data scientists to tackle these issues depends on what we do today. “It’s how did we teach our children?” she said. “Why don’t we teach math and science the way we teach P.E. and art and music and make it fun?”
“Ethics is not just an elective, but some portion of the main core curriculum.”
The U.S. Bureau of Labor Statistics backs that up, predicting that employment of statisticians will grow 34 percent between 2014 and 2024. Not surprisingly, the bureau notes, that is “much faster than the average for all occupations.”
ACRL e-Learning webcast series: Learning Analytics – Strategies for Optimizing Student Data on Your Campus
This three-part webinar series, co-sponsored by the ACRL Value of Academic Libraries Committee, the Student Learning and Information Committee, and the ACRL Instruction Section, will explore the advantages and opportunities of learning analytics as a tool which uses student data to demonstrate library impact and to identify learning weaknesses. How can librarians initiate learning analytics initiatives on their campuses and contribute to existing collaborations? The first webinar will provide an introduction to learning analytics and an overview of important issues. The second will focus on privacy issues and other ethical considerations as well as responsible practice, and the third will include a panel of librarians who are successfully using learning analytics on their campuses.
Webcast One: Learning Analytics and the Academic Library: The State of the Art and the Art of Connecting the Library with Campus Initiatives
March 29, 2016
Learning analytics are used nationwide to augment student success initiatives as well as bolster other institutional priorities. As a key aspect of educational reform and institutional improvement, learning analytics are essential to defining the value of higher education, and academic librarians can be both of great service to and well served by institutional learning analytics teams. In addition, librarians who seek to demonstrate, articulate, and grow the value of academic libraries should become more aware of how they can dovetail their efforts with institutional learning analytics projects. However, all too often, academic librarians are not asked to be part of initial learning analytics teams on their campuses, despite the benefits of library inclusion in these efforts. Librarians can counteract this trend by being conversant in learning analytics goals, advantages/disadvantages, and challenges as well as aware of existing examples of library successes in learning analytics projects.
Learn about the state of the art in learning analytics in higher education with an emphasis on 1) current models, 2) best practices, 3) ethics, privacy, and other difficult issues. The webcast will also focus on current academic library projects and successes in gaining access to and inclusion in learning analytics initiatives on their campus. Benefit from the inclusion of a “short list” of must-read resources as well as a clearly defined list of ways in which librarians can leverage their skills to be both contributing members of learning analytics teams, suitable for use in advocating on their campuses.
student’s opinion of this process
benefits: self-assessment, personal learning, empwerment
analytics and data privacy – students are OK with harvesting the data (only 6% not happy)
8 in 10 are interested in personal dashboard, which will help them perform
Big Mother vs Big Brother: creepy vs helpful. tracking classes, helpful, out of class (where on campus, social media etc) is creepy. 87% see that having access to their data is positive
recognize metrics, assessment, analytics, data. visualization, data literacy, data science, interpretation
INSTRUCTION DEPARTMENT – N.B.
determine who is the key leader: director of institutional research, president, CIO
who does analyics services: institutional research, information technology, dedicated center
analytic maturity: data drivin, decision making culture; senior leadership commitment,; policy supporting (data ollection, accsess, use): data efficacy; investment and resourcefs; staffing; technical infrastrcture; information technology interaction
student success maturity: senior leader commited; fudning of student success efforts; mechanism for making student success decisions; interdepart collaboration; undrestanding of students success goals; advising and student support ability; policies; information systems
developing learning analytics strategy
understand institutional challenges; identify stakeholders; identify inhibitors/challenges; consider tools; scan the environment and see what other done; develop a plan; communicate the plan to stakeholders; start small and build
ways librarians can help
idenfify institu partners; be the partners; hone relevant learning analytics; participate in institutional analytics; identify questions and problems; access and work to improve institu culture; volunteer to be early adopters;
questions to ask: environmental scanning
do we have a learning analytics system? does our culture support? leaders present? stakeholders need to know?
questions to ask: Data
questions to ask: Library role
learning analytics & the academic library: the state of the art of connecting the library with campus initiatives
7 Things You Should Know About First-Generation Learning Analytics. Published:
causation versus correlation studies. speakers claims that it is difficult to establish causation argument. institutions try to predict as accurately as possible via correlation, versus “if you do that it will happen what.”
Instruction and Liaison Librarian, University of Northern Iowa
games and gamification. the semantics are important. using the right terms can be crucial in the next several years.
gamification for the enthusiasm. credit course with buffet. the pper-to-peer is very important
affordability; east to use; speed to create.
assessment. if you want heavy duty, SPSS kind of assessment, use polldaddy or polleverywhere.
Kahoot only Youtube, does not allow to upload own video or use Kaltura AKA Medispace, text versus multimedia
Kahoot is replacing Voicethread at K12, use the wave
Kahoot allows to share the quizzes and surveys
Kahoot is not about assessment, it is not about drilling knowledge, it is about conversation starter. why do we read an article? there is no shame in wrong answer.
the carrot: when they reach the 1000 points, they can leave the class
Kahoot music can be turned off, how short, the answers are limited like in Twitter
screenshot their final score and reach 80%
gravity is hard, scatter start with. auditory output
1st day is Kahoot, second day is Team challange and test
embed across the curriculum
gaming toolkit for campus
what to take home: have students facing students from differnt library
In the age of Big Data, there is an abundance of free or cheap data sources available to libraries about their users’ behavior across the many components that make up their web presence. Data from vendors, data from Google Analytics or other third-party tracking software, and data from user testing are all things libraries have access to at little or no cost. However, just like many students can become overloaded when they do not know how to navigate the many information sources available to them, many libraries can become overloaded by the continuous stream of data pouring in from these sources. This session will aim to help librarians understand 1) what sorts of data their library already has (or easily could have) access to about how their users use their various web tools, 2) what that data can and cannot tell them, and 3) how to use the datasets they are collecting in a holistic manner to help them make design decisions. The presentation will feature examples from the presenters’ own experience of incorporating user data in decisions related to design the Bethel University Libraries’ web presence.
data tools: user testing, google analytics, click trakcer vendor data
user testing, free, no visualization, cross-domain, easy to use, requires scripts
qualitative q/s : why people do what they do and how will users think about your content
3 versions: variables: options on book search and order/wording of the sections in the articles tab
Findings: big difference between tabs versus single-page. Lil difference btw single-page options. Take-aways it won’t tell how to fix the problem, how to be empathetic how the user is using the page
Like to do in the future: FAQ and Chat. Problem: low use. Question how to make it be used (see PPT details)
Crazy Egg – Click Trackers. not a free tool, lowest tier, less $10/m.
see PPT for details>
interaction with the pates, clicks and scrollings
not easy to use, steep learning curve
“blob” GAnalytics recognize the three different domains that r clicked through as one.
vendor data: springshare
chat and FAQ
is there a dashboard tool that can combine all these tools?
optimal workshop: reframe, but it is more about qualitative data.
how long does it take to build this? about two years in general, but in the last 6 months focused.