Posts Tagged ‘Big Data’
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:
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:
https://blog.stcloudstate.edu/ims/?s=analytics&submit=Search
http://2016libtechconference.sched.org/event/69f9/come-on-down-gaming-in-the-flipped-classroom#
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
- 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
- scroll analytics
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
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.
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)
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:
https://blog.stcloudstate.edu/ims/?s=digital+literacy&submit=Search
#digilit
Driving positive change in the student life cycle
https://www-01.ibm.com/marketing/iwm/dre/signup?source=ibm-analytics&S_PKG=ov18048&S_TACT=C3310AVW&dynform=4817
How to make better decisions faster
IBM Predictive Analytics Solutions for Education can help you improve outcomes
Your data is a record of what’s already happened. But did you know that the same data—combined with the right analytical tools—can give you a forward-looking view of your situation, along with recommendations for decision making?
Read this white paper to learn how predictive analytics can help your institution address a range of challenges, from increasing graduation rates student by student to optimizing recruitment, fundraising and the performance measures that matter most.
Center for Digital Education (CDE)
real-time impact on curriculum structure, instruction delivery and student learning, permitting change and improvement. It can also provide insight into important trends that affect present and future resource needs.
Big Data: Traditionally described as high-volume, high-velocity and high-variety information.
Learning or Data Analytics: The measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.
Educational Data Mining: The techniques, tools and research designed for automatically extracting meaning from large repositories of data generated by or related to people’s learning activities in educational settings.
Predictive Analytics: Algorithms that help analysts predict behavior or events based on data.
Predictive Modeling: The process of creating, testing and validating a model to best predict the probability of an outcome.
Data analytics, or the measurement, collection, analysis and reporting of data, is driving decisionmaking in many institutions. However, because of the unique nature of each district’s or college’s data needs, many are building their own solutions.
For example, in 2014 the nonprofit company inBloom, Inc., backed by $100 million from the Gates Foundation and the Carnegie Foundation for the Advancement of Teaching, closed its doors amid controversy regarding its plan to store, clean and aggregate a range of student information for states and districts and then make the data available to district-approved third parties to develop tools and dashboards so the data could be used by classroom educators.22
Tips for Student Data Privacy
Know the Laws and Regulations
There are many regulations on the books intended to protect student privacy and safety: the Family Educational Rights and Privacy Act (FERPA), the Protection of Pupil Rights Amendment (PPRA), the Children’s Internet Protection Act (CIPA), the Children’s Online Privacy Protection Act (COPPA) and the Health Insurance Portability and Accountability Act (HIPAA)
— as well as state, district and community laws. Because technology changes so rapidly, it is unlikely laws and regulations will keep pace with new data protection needs. Establish a committee to ascertain your institution’s level of understanding of and compliance with these laws, along with additional safeguard measures.
Make a Checklist Your institution’s privacy policies should cover security, user safety, communications, social media, access, identification rules, and intrusion detection and prevention.
Include Experts
To nail down compliance and stave off liability issues, consider tapping those who protect privacy for a living, such as your school attorney, IT professionals and security assessment vendors. Let them review your campus or district technologies as well as devices brought to campus by students, staff and instructors. Finally, a review of your privacy and security policies, terms of use and contract language is a good idea.
Communicate, Communicate, Communicate
Students, staff, faculty and parents all need to know their rights and responsibilities regarding data privacy. Convey your technology plans, policies and requirements and then assess and re-communicate those throughout each year.
“Anything-as-a-Service” or “X-as-a-Service” solutions can help K-12 and higher education institutions cope with big data by offering storage, analytics capabilities and more. These include:
• Infrastructure-as-a-Service (IaaS): Providers offer cloud-based storage, similar to a campus storage area network (SAN)
• Platform-as-a-Service (PaaS): Opens up application platforms — as opposed to the applications themselves — so others can build their own applications
using underlying operating systems, data models and databases; pre-built application components and interfaces
• Software-as-a-Service (SaaS): The hosting of applications in the cloud
• Big-Data-as-a-Service (BDaaS): Mix all the above together, upscale the amount of data involved by an enormous amount and you’ve got BDaaS
Suggestions:
Use accurate data correctly
Define goals and develop metrics
Eliminate silos, integrate data
Remember, intelligence is the goal
Maintain a robust, supportive enterprise infrastructure.
Prioritize student privacy
Develop bullet-proof data governance guidelines
Create a culture of collaboration and sharing, not compliance.
more on big data in this IMS blog:
https://blog.stcloudstate.edu/ims/?s=big+data&submit=Search
5 Disruptive Tech Trends That Could Dominate in 2016
http://www.fool.com/investing/general/2015/09/26/5-disruptive-tech-trends-that-could-dominate-in-20.aspx
Andres Cardenal (IoT). The Internet of Things
Tim Brugger (Big Data): In part because the world around us is becoming “connected” through a growing number of IoT sensors, mobile devices, and the world’s affinity for the Internet, the sheer volume of information available is already staggering.
Daniel B. Kline (endless payment): While subscriptions have always been a factor on the enterprise side of the software business, they’re now moving into the consumer end of things. The leader has been Microsoft (NASDAQ:MSFT), which has managed to move a large part of its Office customer base into a subscription model.
Tim Green (budget smartphones): Zenfone 2 from Asus and the Moto G from Motorola.
RE.WORK Deep Learning Summit, Boston
May 26-27, 2015
Boston, Massachusetts
Big Data is Finally Coming to Education Here’s What We’ve Learned So Far
http://www.edukwest.com/big-data-education/
Long lectures don’t work.
The best predictor of future course behavior is past course behavior.
Data from MOOCs suggest that one way to boost completion rates is to increase engagement early in the course.
Even in online courses, offline support is essential.
More IMS blog entries on Big Data:
https://blog.stcloudstate.edu/ims/?s=big+data
A Bried History of BIG Data
Volume, Velocity, Variety
Business Intelligence
Internet of Things
privacy, security, intellectual property
mobile Internet
http://mashable.com/2015/01/19/super-mario-artificial-intelligence/
A team of German researchers has used artificial intelligence to create a “self-aware” version of Super Mario who can respond to verbal commands and automatically play his own game.
Artificial Intelligence helps Mario play his own game
Students at the University of Tubingen have used Mario as part of their efforts to find out how the human brain works.
The cognitive modelling unit claim their project has generated “a fully functional program” and “an alive and somewhat intelligent artificial agent”.
http://www.bbc.co.uk/newsbeat/30879456
Can Super Mario Save Artificial Intelligence?
The most popular approaches today focus on Big Data, or mimicking humansthat already know how to do some task. But sheer mimicry breaks down when one gives a machine new tasks, and, as I explained a few weeks ago, Big Data approaches tend to excel at finding correlations without necessarily being able to induce the rules of the game. If Big Data alone is not a powerful enough tool to induce a strategy in a complex but well-defined game like chess, then that’s a problem, since the real world is vastly more open-ended, and considerably more complicated.
http://www.newyorker.com/tech/elements/can-super-mario-save-artificial-intelligence