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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.

 

Twitter Analytics

How to Improve Your Tweets Using Twitter Analytics

http://www.socialmediaexaminer.com/improve-tweets-using-twitter-analytics/

Twitter ads and Twitonomy are helpful and cost-effective. Find time to go through these reports to see what works for you and your competition. The improvement in results from your Twitter marketing will be worth it.

Once you get comfortable with this kind of data review, check back every week, month or quarter to make sure that you are still hitting the optimal mark. The social media world moves fast, and analytics will help you keep pace with the changes.

mobile technology, badges, flipped classrooms, and learning analytics according to Bryan Alexander

Very short video of Bryan Alexander, senior fellow at the National Institute for Technology in Liberal Education, discussing the issues and opportunities facing mobile technology, badges, flipped classrooms, and learning analytics: 

http://online.qmags.com/CPT0113/default.aspx?sessionID=C711175DBEE9188D0D93C2F28&cid=2335187&eid=17730&pg=18&mode=2#pg18&mode1

100 tech debacles of the decade

http://hackeducation.com/2019/12/31/what-a-shitshow

1. Anti-School Shooter Software

4. “The Year of the MOOC” (2012)

6. “Everyone Should Learn to Code”

8. LAUSD’s iPad Initiative (2013)

9. Virtual Charter Schools

10. Google for Education

14. inBloom. The Shared Learning Collaborative (2011)

17. Test Prep

20. Predictive Analytics

22. Automated Essay Grading

25. Peter Thiel

26. Google Glass

32. Common Core State Standards

44. YouTube, the New “Educational TV”

48. The Hour of Code

49. Yik Yak

52. Virtual Reality

57. TurnItIn (and the Cheating Detection Racket) (my note: repeating the same for years: http://blog.stcloudstate.edu/ims?s=turnitin)

59. Clayton Christensen’s Predictions
http://blog.stcloudstate.edu/ims?s=clayton

61. Edmodo. http://blog.stcloudstate.edu/ims?s=edmodo

62. Edsurge

64. Alexa at School

65. Apple’s iTextbooks (2011)

67. UC Berkeley Deletes Its Online Lectures. ADA

72. Chatbot Instructors. IBM Watson “AI” technology (2016)

81. Interactive Whiteboards (my note: repeating the same for years: http://blog.stcloudstate.edu/ims?s=smartboard)

82. “The End of Library” Stories (and the Software that Seems to Support That)

86. Badges

89. Clickers

90. “Ban Laptops” Op-Eds (my note: collecting pros and cons for years: http://blog.stcloudstate.edu/ims/2017/04/03/use-of-laptops-in-the-classroom/)

92. “The Flipped Classroom”

93. 3D Printing

100. The Horizon Report

corporate surveillance

Behind the One-Way Mirror: A Deep Dive Into the Technology of Corporate Surveillance
BY BENNETT CYPHERS DECEMBER 2, 2019

https://www.eff.org/wp/behind-the-one-way-mirror

Corporations have built a hall of one-way mirrors: from the inside, you can see only apps, web pages, ads, and yourself reflected by social media. But in the shadows behind the glass, trackers quietly take notes on nearly everything you do. These trackers are not omniscient, but they are widespread and indiscriminate. The data they collect and derive is not perfect, but it is nevertheless extremely sensitive.

A data-snorting company can just make low bids to ensure it never wins while pocketing your data for nothing. This is a flaw in the implied deal where you trade data for benefits.

You can limit what you give away by blocking tracking cookies. Unfortunately, you can still be tracked by other techniques. These include web beaconsbrowser fingerprinting and behavioural data such as mouse movements, pauses and clicks, or sweeps and taps.

The EU’s GDPR (General Data Protection Regulation) was a baby step in the right direction. BOWM also mentions Vermont’s data privacy law, the Illinois Biometric Information Protection Act (BIPA) and next year’s California Consumer Privacy Act (CCPA).

Tor, the original anti-surveillance browser, is based on an old, heavily modified version of Firefox.

Most other browsers are now, like Chrome, based on Google’s open source Chromium. Once enough web developers started coding for Chrome instead of for open standards, it became arduous and expensive to sustain alternative browser engines. Chromium-based browsers now include Opera, Vivaldi, Brave, the Epic Privacy Browser and next year’s new Microsoft Edge.

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

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