Posts Tagged ‘Big Data’

bid data and school abscence

Data Can Help Schools Confront ‘Chronic Absence’

By Dian Schaffhauser 09/22/16

https://thejournal.com/articles/2016/09/22/data-can-help-schools-confront-chronic-absence.aspx

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.

The report links to free data tools on the Attendance Works website, including a calculator for tallying chronic absences and guidance on how to protect student privacy when sharing data.

The full report is freely available on the Attendance Works website.

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more on big data in education in this IMS blog
http://blog.stcloudstate.edu/ims?s=data

Smart Future: Knowledge Trends that will Change the World

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:

 

  1. Innovation and Knowledge Management
  2. Big Data and Analytics
  3. Social Media and Analytics
  4. 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.

 

For your reference and further information about this event, please refer to 1. Brochure http://www.teriin.org/events/icdl/pdf/Brochure.pdf

  1. Background paper

http://www.teriin.org/events/icdl/pdf/ICDL_BackgroundPaper/

 

Do write back to us for further queries, if any.

For further Information Contact:

Mr V V S Parihar

ICDL 2016 Secretariat

The Energy and Resources Institute (TERI) India Habitat Centre Complex, Lodhi Road, New Delhi-110003, India

Tel: +91 11 24682100 or 41504900

Fax: 24682144 Email: ICDL2016@teri.res.in, vijayvsp@teri.res.in

Website: http://www.teriin.org/events/icdl

how teachers use data

The Three Ways Teachers Use Data—and What Technology Needs to Do Better

By Karen Johnson May 17, 2016

https://www.edsurge.com/news/2016-05-17-the-three-ways-teachers-use-data-and-what-technology-can-do-better

After surveying more than 4,650 educators, we learned that teachers are essentially trying to do three things with data—each of which technology can dramatically improve:

1. Assess

2. Analyze

3. Pivot

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What’s At Risk When Schools Focus Too Much on Student Data?

What’s At Risk When Schools Focus Too Much on Student Data?

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.

1) Motivation stereotype threat.

it could create negative feelings about school, threatening students’ sense of belonging, which is key to academic motivation.

2) Helicoptering

Today, parents increasingly are receiving daily text messages with photos and videos from the classroom. A style of overly involved “intrusive parenting” has been associated in studies with increased levels of anxiety and depression when students reach college. “Parent portals as utilized in K-12 education are doing significant harm to student development,” argues college instructor John Warner in a recent piece for Inside Higher Ed.

3) Commercial Monitoring and Marketing

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.

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more on big data in education in this blog:
http://blog.stcloudstate.edu/ims?s=big+data+education

quantile measurements in education

Quantile Measures for Math Added to Kansas Student Assessments

By Dian Schaffhauser 05/27/16

https://thejournal.com/articles/2016/05/27/quantile-measures-for-math-added-to-kansas-student-assessments.aspx

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.

big data and the government

What can the government do about big data fairness?

https://fcw.com/articles/2016/05/23/big-data-fairness.aspx

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.

The FTC released two studies on how big data is used to segment consumers into profiles and interests.

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.”

more on big data in this IMS blog:

http://blog.stcloudstate.edu/ims?s=big+data

big data and higher ed

Higher Ed Can Be a One-Two Punch

According to a recent survey, many colleges lack critical analytics skills to effectively leverage data.

More on analytics and big data in this IMS blog:

http://blog.stcloudstate.edu/ims?s=analytics
http://blog.stcloudstate.edu/ims?s=big+data

analytics in education

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.

my notes:

open academic analytics initiative
https://confluence.sakaiproject.org/pages/viewpage.action?pageId=75671025

where data comes from:

  • students information systems (SIS)
  • LMS
  • Publishers
  • Clickers
  • Video streaming and web conferencing
  • Surveys
  • Co-curricular and extra-curricular involvement

D2L degree compass
Predictive Analytics Reportitng PAR – was open, but just bought by Hobsons (https://www.hobsons.com/)

Learning Analytics

IMS Caliper Enabled Services. the way to connect the library in the campus analytics https://www.imsglobal.org/activity/caliperram

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

librarians:
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

questions:
pole analytics library

 

 

 

 

 

 

 

 

 

 

 

 

 

 

literature

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.”

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More on analytics in this blog:

http://blog.stcloudstate.edu/ims/?s=analytics&submit=Search

tech lib conference 2016

http://2016libtechconference.sched.org/event/69f9/come-on-down-gaming-in-the-flipped-classroom#

avatar for Angie Cox

Angie Cox

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

gaming types

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

Quizlet

screenshot their final score and reach 80%

gravity is hard, scatter start with. auditory output

drill game

Teach Challenge.

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

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http://sched.co/69f2

Putting it all together: a holistic approach to utilizing your library’s user data for making informed web design decisions 

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.

http://tinyurl.com/jbchapf

data tools: user testing, google analytics, click trakcer vendor data

  1. 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)
  2. Crazy Egg – Click Trackers. not a free tool, lowest tier, less $10/m.
    see PPT for details>
    interaction with the pates, clicks and scrollings
  3. scroll analytics
    not easy to use, steep learning curve
    “blob” GAnalytics recognize the three different domains that r clicked through as one.
  4. vendor data: springshare
    chat and FAQ
    Libguides

questions:

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.

artificial-intelligence engine https://www.technologyreview.com/s/600984/an-ai-with-30-years-worth-of-knowledge-finally-goes-to-work/,
Doug Lenat

digital literacy

digital literacy planning tool

Definition:

Digital literacy = technology use + critical thinking + social awareness

7 characteristics of a digital mindset

https://www.peoplematters.in/article/hr-technology/7-characteristics-digital-mindset-12980

The digital five forces – Social Media, Big Data, Mobility and Pervasive Computing, Cloud, and AI and Robotics – are disintermediating, disrupting and deconstructing the old world order.

Abundance Mindset
Growth Mindset
Agile Approach
Comfort with Ambiguity
Explorer’s Mind
Collaborative Approach
Embracing Diversity

Scientific Studies on Literacy and Digital Literacy Indexed in Scopus: A Literature Review (2000-2013)

http://revistas.lasalle.edu.co/index.php/ap/article/view/3579/2933
Conclusions:
the study of digital tools linked to these new literacies is absolutely necessary, particularly because Web 2.0 allow users to interact and cooperate together as content creators in a virtual community. Although this concept may suggest a new version of the World Wide Web (WWW), it really does not refer to an update of the technical features, but rather to the changes concerning the use and  interaction through the Web.

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More on digital literacy in this blog:

http://blog.stcloudstate.edu/ims/?s=digital+literacy&submit=Search

#digilit

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