Searching for "analytics"

analytics on demand

Free Webinar: Driving Decisions With Data

with Analytics On Demand, you can add value to your library’s existing data and unlock key insights about your community.

Monday, July 24, 2017 12 p.m. Central

Tune in to this free 60-minute webcast Joining us for this webinar are:

  • Jason Kucsma, deputy director, Toledo Lucas County (Ohio) Public Library
  • Liz Bondie, education sales consultant, Gale, a Cengage company

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

responsible analytics

Call for Chapters: Responsible Analytics and Data Mining in Education

https://www.linkedin.com/groups/934617/934617-6276907956181233664

• Who collects and controls the data?
• Is it accessible to all stakeholders?
• How are the data being used, and is there a possibility for abuse?
• How do we assess data quality?
• Who determines which data to trust and use?
• What happens when the data analysis yields flawed results?
• How do we ensure due process when data-driven errors are uncovered?
• What policies are in place to address errors?
• Is there a plan for handling data breaches?

Call for Chapter Proposals page (https://big-data-in-education.blogspot.com)

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more on data mining in this IMS blog
http://blog.stcloudstate.edu/ims?s=data+mining
more on analytics in this IMS blog
http://blog.stcloudstate.edu/ims?s=analytics

Analytics and Data Mining in Education

https://www.linkedin.com/groups/934617/934617-6255144273688215555

Call For Chapters: Responsible Analytics and Data Mining in Education: Global Perspectives on Quality, Support, and Decision-Making

SUBMIT A 1-2 PAGE CHAPTER PROPOSAL
Deadline – June 1, 2017

Title:  Responsible Analytics and Data Mining in Education: Global Perspectives on Quality, Support, and Decision-Making

Synopsis:
Due to rapid advancements in our ability to collect, process, and analyze massive amounts of data, it is now possible for educators at all levels to gain new insights into how people learn. According to Bainbridge, et. al. (2015), using simple learning analytics models, educators now have the tools to identify, with up to 80% accuracy, which students are at the greatest risk of failure before classes even begin. As we consider the enormous potential of data analytics and data mining in education, we must also recognize a myriad of emerging issues and potential consequences—intentional and unintentional—to implement them responsibly. For example:

· Who collects and controls the data?
· Is it accessible to all stakeholders?
· How are the data being used, and is there a possibility for abuse?
· How do we assess data quality?
· Who determines which data to trust and use?
· What happens when the data analysis yields flawed results?
· How do we ensure due process when data-driven errors are uncovered?
· What policies are in place to address errors?
· Is there a plan for handling data breaches?

This book, published by Routledge Taylor & Francis Group, will provide insights and support for policy makers, administrators, faculty, and IT personnel on issues pertaining the responsible use data analytics and data mining in education.

Important Dates:

· June 1, 2017 – Chapter proposal submission deadline
· July 15, 2017 – Proposal decision notification
· October 15, 2017 – Full chapter submission deadline
· December 1, 2017 – Full chapter decision notification
· January 15, 2018 – Full chapter revisions due
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more on data mining in this IMS blog
http://blog.stcloudstate.edu/ims?s=data+mining

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

social media analytics

social media measurement matrix

http://www.affectstrategies.com/files/AFT_SocialMediaSuccess_Measurement.pdf

social media measurement: a step by step approach

https://www.prinz.org.nz/Attachment?Action=Download&Attachment_id=1023

5 Essential & Easy Social Media Metrics You Should Be Measuring Right Now

5 Essential & Easy Social Media Metrics You Should Be Measuring Right Now

 

  1. volume
  2. reach
  3. engagement
  4. influence
  5. share of voice

Twitter Social Media Analytics

#1: Adjust Your Content Mix

On Facebook, go to Insights > Posts > Post Types to review the engagement by the type of content you posted (post, link, image, video). On Twitter, you can see a snapshot of each post you’ve made by going to Settings > Analytics > Tweets.

#2: Fine-tune Your Posting Schedule

On Facebook, go to Insights > Posts > When Your Fans Are Online. For Twitter, you can use a tool such a Tweriod to find out when the bulk of your followers are online.

#3: Inform Your Messaging

On Facebook, open the Ads Manager and go to Audience Insights. On Twitter, you can check your audience data by going to Settings > Twitter Ads > Analytics > Audience Insights.

#4: Boost Your Engagement

On Twitter, go to Settings > Analytics > Tweets and take a look at which post topics get the most engagement. On Facebook, go to Insights > Posts > Post Types and then switch the engagement metrics in Facebook to show reactions, comments, and shares for each post rather than post clicks or general engagement.

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more on social media analytics in this blog

http://blog.stcloudstate.edu/ims?s=social+media+analytics

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

data analytics education

Analytics for Achievement: White Paper

Around the world, in both developing and developed countries, too many primary and secondary students are falling below proficiency levels. Measuring and monitoring performance and understanding the factors at play in student achievement can help educators create the right conditions and design the most effective interventions for student success.

link to the article (PDF file) ; THE_IBM_data_Analytics_for_Achievement k12

learning analytics

ACRL e-Learning webcast series: Learning Analytics – Strategies for Optimizing Student Data on Your Campus

http://www.ala.org/acrl/learninganalytics

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

Webcast Two: Privacy and the Online Classroom: Learning Analytics, Ethical Considerations, and Responsible Practice
April 13, 2016

Webcast Three: Moving Beyond Counts and Check Marks: Bringing the Library into Campus-Wide Learning Analytics Programs
May 11, 2016

Predictive Analytics

Educational Intelligence and the Student Lifecycle – Leveraging Predictive Analytics for Profit in Higher Education

This presentation will begin on Wednesday, August 12, 2015 at 02:00 PM Eastern Daylight Time.

Wednesday, August 12, 2015 02:00 PM EDT

This webinar will provide an overview of the student lifecycle – from lead generation to job placement. You will learn what the components are and how student data can be leveraged for competitive gain through the use of predictive analytics tools. While these technologies have been in use by other industries for many years, especially in the area of assessing consumer demand, higher education is a relatively late adopter. As an example of benefit, colleges and universities can deploy them to determine which students are most at risk for attrition and – armed with deep, historical data – craft segment-specific retention strategies designed to compel them to persist toward degree completion. During this session, Eduventures analysts will provide concrete examples of how predictive analytics has been used within the student lifecycle at a variety of institutions, citing interviews with practitioners, that led to measurable performance improvements. To conclude, we will uncover the benefits of sharing data amongst key stakeholders to the ultimate gain of the institution and its constituents.

Speakers:

Jeff Alderson
Principal Analyst
Max Woolf
Senior Analyst

Audience members may arrive 15 minutes in advance of this time.

 

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