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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
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more on analytics in this IMS blog
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student data mining

Beyond the Horizon Webinar on Student Data

March 29, 2017 @ 12-1pm US Central Time

NMC Beyond the Horizon > Integrating Student Data Across Platforms

The growing use of data mining software in online education has great potential to support student success by identifying and reaching out to struggling students and streamlining the path to graduation. This can be a challenge for institutions that are using a variety of technology systems that are not integrated with each other. As institutions implement learning management systems, degree planning technologies, early alert systems, and tutor scheduling that promote increased interactions among various stakeholders, there is a need for centralized aggregation of these data to provide students with holistic support that improves learning outcomes. Join us to hear from an institutional exemplar who is building solutions that integrate student data across platforms. Then work with peers to address challenges and develop solutions of your own.

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more on altmetrics in this IMS blog
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more on big data in this IMS blog
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text and data mining

38 great resources for learning data mining concepts and techniques

http://www.rubedo.com.br/2016/08/38-great-resources-for-learning-data.html

Learn data mining languages: R, Python and SQL

W3Schools – Fantastic set of interactive tutorials for learning different languages. Their SQL tutorial is second to none. You’ll learn how to manipulate data in MySQL, SQL Server, Access, Oracle, Sybase, DB2 and other database systems.
Treasure Data – The best way to learn is to work towards a goal. That’s what this helpful blog series is all about. You’ll learn SQL from scratch by following along with a simple, but common, data analysis scenario.
10 Queries – This course is recommended for the intermediate SQL-er who wants to brush up on his/her skills. It’s a series of 10 challenges coupled with forums and external videos to help you improve your SQL knowledge and understanding of the underlying principles.
TryR – Created by Code School, this interactive online tutorial system is designed to step you through R for statistics and data modeling. As you work through their seven modules, you’ll earn badges to track your progress helping you to stay on track.
Leada – If you’re a complete R novice, try Lead’s introduction to R. In their 1 hour 30 min course, they’ll cover installation, basic usage, common functions, data structures, and data types. They’ll even set you up with your own development environment in RStudio.
Advanced R – Once you’ve mastered the basics of R, bookmark this page. It’s a fantastically comprehensive style guide to using R. We should all strive to write beautiful code, and this resource (based on Google’s R style guide) is your key to that ideal.
Swirl – Learn R in R – a radical idea certainly. But that’s exactly what Swirl does. They’ll interactively teach you how to program in R and do some basic data science at your own pace. Right in the R console.
Python for beginners – The Python website actually has a pretty comprehensive and easy-to-follow set of tutorials. You can learn everything from installation to complex analyzes. It also gives you access to the Python community, who will be happy to answer your questions.
PythonSpot – A complete list of Python tutorials to take you from zero to Python hero. There are tutorials for beginners, intermediate and advanced learners.
Read all about it: data mining books
Data Jujitsu: The Art of Turning Data into Product – This free book by DJ Patil gives you a brief introduction to the complexity of data problems and how to approach them. He gives nice, understandable examples that cover the most important thought processes of data mining. It’s a great book for beginners but still interesting to the data mining expert. Plus, it’s free!
Data Mining: Concepts and Techniques – The third (and most recent) edition will give you an understanding of the theory and practice of discovering patterns in large data sets. Each chapter is a stand-alone guide to a particular topic, making it a good resource if you’re not into reading in sequence or you want to know about a particular topic.
Mining of Massive Datasets – Based on the Stanford Computer Science course, this book is often sighted by data scientists as one of the most helpful resources around. It’s designed at the undergraduate level with no formal prerequisites. It’s the next best thing to actually going to Stanford!
Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners – This book is a must read for anyone who needs to do applied data mining in a business setting (ie practically everyone). It’s a complete resource for anyone looking to cut through the Big Data hype and understand the real value of data mining. Pay particular attention to the section on how modeling can be applied to business decision making.
Data Smart: Using Data Science to Transform Information into Insight – The talented (and funny) John Foreman from MailChimp teaches you the “dark arts” of data science. He makes modern statistical methods and algorithms accessible and easy to implement.
Hadoop: The Definitive Guide – As a data scientist, you will undoubtedly be asked about Hadoop. So you’d better know how it works. This comprehensive guide will teach you how to build and maintain reliable, scalable, distributed systems with Apache Hadoop. Make sure you get the most recent addition to keep up with this fast-changing service.
 Online learning: data mining webinars and courses
DataCamp – Learn data mining from the comfort of your home with DataCamp’s online courses. They have free courses on R, Statistics, Data Manipulation, Dynamic Reporting, Large Data Sets and much more.
Coursera – Coursera brings you all the best University courses straight to your computer. Their online classes will teach you the fundamentals of interpreting data, performing analyzes and communicating insights. They have topics for beginners and advanced learners in Data Analysis, Machine Learning, Probability and Statistics and more.
Udemy – With a range of free and pay for data mining courses, you’re sure to find something you like on Udemy no matter your level. There are 395 in the area of data mining! All their courses are uploaded by other Udemy users meaning quality can fluctuate so make sure you read the reviews.
CodeSchool – These courses are handily organized into “Paths” based on the technology you want to learn. You can do everything from build a foundation in Git to take control of a data layer in SQL. Their engaging online videos will take you step-by-step through each lesson and their challenges will let you practice what you’ve learned in a controlled environment.
Udacity – Master a new skill or programming language with Udacity’s unique series of online courses and projects. Each class is developed by a Silicon Valley tech giant, so you know what your learning will be directly applicable to the real world.
Treehouse – Learn from experts in web design, coding, business and more. The video tutorials from Treehouse will teach you the basics and their quizzes and coding challenges will ensure the information sticks. And their UI is pretty easy on the eyes.
Learn from the best: top data miners to follow
John Foreman – Chief Data Scientist at MailChimp and author of Data Smart, John is worth a follow for his witty yet poignant tweets on data science.
DJ Patil – Author and Chief Data Scientist at The White House OSTP, DJ tweets everything you’ve ever wanted to know about data in politics.
Nate Silver – He’s Editor-in-Chief of FiveThirtyEight, a blog that uses data to analyze news stories in Politics, Sports, and Current Events.
Andrew Ng – As the Chief Data Scientist at Baidu, Andrew is responsible for some of the most groundbreaking developments in Machine Learning and Data Science.
Bernard Marr – He might know pretty much everything there is to know about Big Data.
Gregory Piatetsky – He’s the author of popular data science blog KDNuggets, the leading newsletter on data mining and knowledge discovery.
Christian Rudder – As the Co-founder of OKCupid, Christian has access to one of the most unique datasets on the planet and he uses it to give fascinating insight into human nature, love, and relationships
Dean Abbott – He’s contributed to a number of data blogs and authored his own book on Applied Predictive Analytics. At the moment, Dean is Chief Data Scientist at SmarterHQ.
Practice what you’ve learned: data mining competitions
Kaggle – This is the ultimate data mining competition. The world’s biggest corporations offer big prizes for solving their toughest data problems.
Stack Overflow – The best way to learn is to teach. Stackoverflow offers the perfect forum for you to prove your data mining know-how by answering fellow enthusiast’s questions.
TunedIT – With a live leaderboard and interactive participation, TunedIT offers a great platform to flex your data mining muscles.
DrivenData – You can find a number of nonprofit data mining challenges on DataDriven. All of your mining efforts will go towards a good cause.
Quora – Another great site to answer questions on just about everything. There are plenty of curious data lovers on there asking for help with data mining and data science.
Meet your fellow data miner: social networks, groups and meetups
Reddit – Reddit is a forum for finding the latest articles on data mining and connecting with fellow data scientists. We recommend subscribing to r/dataminingr/dataisbeautiful,r/datasciencer/machinelearning and r/bigdata.
Facebook – As with many social media platforms, Facebook is a great place to meet and interact with people who have similar interests. There are a number of very active data mining groups you can join.
LinkedIn – If you’re looking for data mining experts in a particular field, look no further than LinkedIn. There are hundreds of data mining groups ranging from the generic to the hyper-specific. In short, there’s sure to be something for everyone.
Meetup – Want to meet your fellow data miners in person? Attend a meetup! Just search for data mining in your city and you’re sure to find an awesome group near you.
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8 fantastic examples of data storytelling

8 fantastic examples of data storytelling

Data storytelling is the realization of great data visualization. We’re seeing data that’s been analyzed well and presented in a way that someone who’s never even heard of data science can get it.

Google’s Cole Nussbaumer provides a friendly reminder of what data storytelling actually is, it’s straightforward, strategic, elegant, and simple.

 

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European Commission and text and data mining

The EU just told data mining startups to take their business elsewhere

Lenard Koschwitz

By enabling the development and creation of big data for non-commercial use only, the European Commission has come up with a half-baked policy. Startups will be discouraged from mining in Europe and it will be impossible for companies to grow out of universities in the EU.

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Text and Data Mining

Webinar: Text and Data Mining: The Way Forward, June 30, 10am (EDT)

LITA announcement. Date: Thursday, June 30, 2016,  Time:  10am-11:30am (EDT), Platform: WebEx. Registration required.

a critically important means of uncovering patterns of intellectual practice and usage that have the potential for illuminating facets and perspectives in research and scholarship that might otherwise not be noted. At the same time, challenges exist in terms of project management and support, licensing and other necessary protections.

Confirmed speakers include: Audrey McCulloch, Executive Director, ALPSP; Michael Levine-Clark, Dean of Libraries, University of Denver; Ellen Finnie, Head, Scholarly Communications and Collections Strategies, Massachusetts Institute of Technology; and Jeremy Frey, Professor of Physical Chemistry, Head of Computational Systems Chemistry, University of Southampton, UK.

Audrey McCulloch, Chief Executive, Association of Learned Professional and Society Publishers (ALPSP) and Director of the Publishers Licensing Society

Text and Data Mining: Library Opportunities and Challenges
Michael Levine-Clark, Dean and Director of Libraries, University of Denver

As scholars engage with text and data mining (TDM), libraries have struggled to provide support for projects that are unpredictable and tremendously varied. While TDM can be considered a fair use, in many cases contracts need to be renegotiated and special data sets created by the vendor. The unique nature of TDM projects makes it difficult to plan for them, and often the library and scholar have to figure them out as they go along. This session will explore strategies for libraries to effectively manage TDM, often in partnership with other units on campus and will offer suggestions to improve the process for all.

Michael Levine-Clark, the Dean and Director of the University of Denver Libraries, is the recipient of the 2015 HARRASOWITZ Leadership in Library Acquisitions Award. He writes and speaks regularly on strategies for improving academic library collection development practices, including the use of e-books in academic libraries, the development of demand-driven acquisition models, and implications of discovery tool implementation.

Library licensing approaches in text and data mining access for researchers at MIT
Ellen Finnie, Head, Scholarly Communications & Collections Strategy, MIT Libraries

This talk will address the challenges and successes that the MIT libraries have experienced in providing enabling services that deliver TDM access to MIT researchers, including:
· emphasizing TDM in negotiating contracts for scholarly resources

· defining requirements for licenses for TDM access

· working with information providers to negotiate licenses that work for our researchers

· addressing challenges and retooling to address barriers to success

· offering educational guides and workshops

· managing current needs v. the long-term goal– TDM as a reader’s right

Ellen Finnie is Head, Scholarly Communications & Collections Strategy in the MIT Libraries. She leads the MIT Libraries’ scholarly communications and collections strategy in support of the Libraries’ and MIT’s objectives, including in particular efforts to influence models of scholarly publishing and communication in ways that increase the impact and reach of MIT’s research and scholarship and which promote open, sustainable publishing and access models. She leads outreach efforts to faculty in support of scholarly publication reform and open access activities at MIT, and acts as the Libraries’ chief resource for copyright issues and for content licensing policy and negotiations. In that role, she is involved in negotiating licenses to include text/data mining rights and coordinating researcher access to TDM services for licensed scholarly resources. She has written and spoken widely on digital acquisitions, repositories, licensing, and open access.

Jeremy Frey, Professor of Physical Chemistry, Head of Computational Systems Chemistry, University of Southampton, UK

Text and Data Mining (TDM) facilitates the discovery, selection, structuring, and analysis of large numbers of documents/sets of data, enabling the visualization of results in new ways to support innovation and the development of new knowledge. In both academia and commercial contexts, TDM is increasingly recognized as a means to extract, re-use and leverage additional value from published information, by linking concepts, addressing specific questions, and creating efficiencies. But TDM in practice is not straightforward. TDM methodology and use are fast changing but are not yet matched by the development of enabling policies.

This webinar provides a review of where we are today with TDM, as seen from the perspective of the researcher, library, and licensing-publisher communities. 

big data

big-data-in-education-report

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.

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Big Data. Tracking Students’ Grades Minute-By-Minute: Help or Hindrance

Flanagan, L. (n.d.). Tracking Students’ Grades Minute-By-Minute: Help or Hindrance? MindShift. Retrieved May 12, 2014, from http://blogs.kqed.org/mindshift/2014/05/tracking-students-grades-minute-by-minute-help-or-hindrance/
For all the data and feedback they provide, student information systems interfere with learning.
“School isn’t about learning. It’s about doing well.”
The singular focus on grades that these systems encourage turns learning into a competitive, zero-sum game for students.
My notes:
the parallel with the online grades systems at K12 is the Big Data movement at Higher Ed. Big Data must be about assisting teaching, not about determining teaching and instructors must be very well aware and very carefully navigating in this nebulous areas of assisting versus determining.
This article about quantifying management of teaching and learning in K12 reminds me the big hopes put on technocrats governing counties and economies in the 70s of the last centuries when the advent of the computers was celebrated as the solution of all our problems. Haven’t we, as civilization learned anything from that lesson?

topics for IM260

proposed topics for IM 260 class

  • Media literacy. Differentiated instruction. Media literacy guide.
    Fake news as part of media literacy. Visual literacy as part of media literacy. Media literacy as part of digital citizenship.
  • Web design / web development
    the roles of HTML5, CSS, Java Script, PHP, Bootstrap, JQuery and other scripting languages. Heat maps and other usability issues; website content strategy.
  • Social media for institutional use. Digital Curation. Social Media algorithms. Etiquette Ethics. Mastodon
    I hosted a LITA webinar in the fall of 2016 (four weeks); I can accommodate any information from that webinar for the use of the IM students
  • OER and instructional designer’s assistance to book creators.
    I can cover both the “library part” (“free” OER, copyright issues etc) and the support / creative part of an OER book / textbook
  • “Big Data.” Data visualization. Large scale visualization. Text encoding. Analytics, Data mining. Unizin
    I can introduce the students to the large idea of Big Data and its importance in lieu of the upcoming IoT, but also departmentalize its importance for academia, business, etc. From infographics to heavy duty visualization (Primo X-Services API. JSON, Flask).
  • NetNeutrality, Digital Darwinism, Internet economy and the role of your professional in such environment
    I can introduce students to the issues, if not familiar and / or lead a discussion on a rather controversial topic
  • Digital assessment. Digital Assessment literacy.
    I can introduce students to tools, how to evaluate and select tools and their pedagogical implications
  • Wikipedia
    a hands-on exercise on working with Wikipedia. After the session, students will be able to create Wikipedia entries thus knowing intimately the process of Wikipedia and its information.
  • Effective presentations. Tools, methods, concepts and theories (cognitive load). Presentations in the era of VR, AR and mixed reality.
    I can facilitate a discussion among experts (your students) on selection of tools and their didactically sound use to convey information. I can supplement the discussion with my own findings and conclusions.
  • eConferencing. Tools and methods
    I can facilitate a discussion among your students on selection of tools and comparison. Discussion about the their future and their place in an increasing online learning environment
  • Digital Storytelling. Immersive Storytelling. The Moth. Twine. Transmedia Storytelling
    I am teaching a LIB 490/590 Digital Storytelling class. I can adapt any information from that class to the use of IM students
  • VR, AR, Mixed Reality.
    besides Mark Gill, I can facilitate a discussion, which goes beyond hardware and brands, but expand on the implications for academia and corporate education / world
  • IoT. Arduino, Raspberry PI. Industry 4.0
  • Instructional design. ID2ID
    I can facilitate a discussion based on the Educause suggestions about the profession’s development
  • Microcredentialing in academia and corporate world. Blockchain
  • IT in K12. How to evaluate; prioritize; select. obsolete trends in 21 century schools. K12 mobile learning
  • Podcasting: past, present, future. Beautiful Audio Editor.
    a definition of podcasting and delineation of similar activities; advantages and disadvantages.
  • Digital, Blended (Hybrid), Online teaching and learning: facilitation. Methods and techniques. Proctoring. Online students’ expectations. Faculty support. Asynch. Blended Synchronous Learning Environment
  • Gender, race and age in education. Digital divide. Xennials, Millennials and Gen Z. generational approach to teaching and learning. Young vs old Millennials. Millennial employees.
  • Privacy, [cyber]security, surveillance. K12 cyberincidents. Hackers.
  • Gaming and gamification. Appsmashing. Gradecraft
  • Lecture capture, course capture.
  • Bibliometrics, altmetrics
  • Technology and cheating, academic dishonest, plagiarism, copyright.

IRDL proposal

Applications for the 2018 Institute will be accepted between December 1, 2017 and January 27, 2018. Scholars accepted to the program will be notified in early March 2018.

Title:

Learning to Harness Big Data in an Academic Library

Abstract (200)

Research on Big Data per se, as well as on the importance and organization of the process of Big Data collection and analysis, is well underway. The complexity of the process comprising “Big Data,” however, deprives organizations of ubiquitous “blue print.” The planning, structuring, administration and execution of the process of adopting Big Data in an organization, being that a corporate one or an educational one, remains an elusive one. No less elusive is the adoption of the Big Data practices among libraries themselves. Seeking the commonalities and differences in the adoption of Big Data practices among libraries may be a suitable start to help libraries transition to the adoption of Big Data and restructuring organizational and daily activities based on Big Data decisions.
Introduction to the problem. Limitations

The redefinition of humanities scholarship has received major attention in higher education. The advent of digital humanities challenges aspects of academic librarianship. Data literacy is a critical need for digital humanities in academia. The March 2016 Library Juice Academy Webinar led by John Russel exemplifies the efforts to help librarians become versed in obtaining programming skills, and respectively, handling data. Those are first steps on a rather long path of building a robust infrastructure to collect, analyze, and interpret data intelligently, so it can be utilized to restructure daily and strategic activities. Since the phenomenon of Big Data is young, there is a lack of blueprints on the organization of such infrastructure. A collection and sharing of best practices is an efficient approach to establishing a feasible plan for setting a library infrastructure for collection, analysis, and implementation of Big Data.
Limitations. This research can only organize the results from the responses of librarians and research into how libraries present themselves to the world in this arena. It may be able to make some rudimentary recommendations. However, based on each library’s specific goals and tasks, further research and work will be needed.

 

 

Research Literature

“Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it…”
– Dan Ariely, 2013  https://www.asist.org/publications/bulletin/aprilmay-2017/big-datas-impact-on-privacy-for-librarians-and-information-professionals/

Big Data is becoming an omnipresent term. It is widespread among different disciplines in academia (De Mauro, Greco, & Grimaldi, 2016). This leads to “inconsistency in meanings and necessity for formal definitions” (De Mauro et al, 2016, p. 122). Similarly, to De Mauro et al (2016), Hashem, Yaqoob, Anuar, Mokhtar, Gani and Ullah Khan (2015) seek standardization of definitions. The main connected “themes” of this phenomenon must be identified and the connections to Library Science must be sought. A prerequisite for a comprehensive definition is the identification of Big Data methods. Bughin, Chui, Manyika (2011), Chen et al. (2012) and De Mauro et al (2015) single out the methods to complete the process of building a comprehensive definition.

In conjunction with identifying the methods, volume, velocity, and variety, as defined by Laney (2001), are the three properties of Big Data accepted across the literature. Daniel (2015) defines three stages in big data: collection, analysis, and visualization. According to Daniel, (2015), Big Data in higher education “connotes the interpretation of a wide range of administrative and operational data” (p. 910) and according to Hilbert (2013), as cited in Daniel (2015), Big Data “delivers a cost-effective prospect to improve decision making” (p. 911).

The importance of understanding the process of Big Data analytics is well understood in academic libraries. An example of such “administrative and operational” use for cost-effective improvement of decision making are the Finch & Flenner (2016) and Eaton (2017) case studies of the use of data visualization to assess an academic library collection and restructure the acquisition process. Sugimoto, Ding & Thelwall (2012) call for the discussion of Big Data for libraries. According to the 2017 NMC Horizon Report “Big Data has become a major focus of academic and research libraries due to the rapid evolution of data mining technologies and the proliferation of data sources like mobile devices and social media” (Adams, Becker, et al., 2017, p. 38).

Power (2014) elaborates on the complexity of Big Data in regard to decision-making and offers ideas for organizations on building a system to deal with Big Data. As explained by Boyd and Crawford (2012) and cited in De Mauro et al (2016), there is a danger of a new digital divide among organizations with different access and ability to process data. Moreover, Big Data impacts current organizational entities in their ability to reconsider their structure and organization. The complexity of institutions’ performance under the impact of Big Data is further complicated by the change of human behavior, because, arguably, Big Data affects human behavior itself (Schroeder, 2014).

De Mauro et al (2015) touch on the impact of Dig Data on libraries. The reorganization of academic libraries considering Big Data and the handling of Big Data by libraries is in a close conjunction with the reorganization of the entire campus and the handling of Big Data by the educational institution. In additional to the disruption posed by the Big Data phenomenon, higher education is facing global changes of economic, technological, social, and educational character. Daniel (2015) uses a chart to illustrate the complexity of these global trends. Parallel to the Big Data developments in America and Asia, the European Union is offering access to an EU open data portal (https://data.europa.eu/euodp/home ). Moreover, the Association of European Research Libraries expects under the H2020 program to increase “the digitization of cultural heritage, digital preservation, research data sharing, open access policies and the interoperability of research infrastructures” (Reilly, 2013).

The challenges posed by Big Data to human and social behavior (Schroeder, 2014) are no less significant to the impact of Big Data on learning. Cohen, Dolan, Dunlap, Hellerstein, & Welton (2009) propose a road map for “more conservative organizations” (p. 1492) to overcome their reservations and/or inability to handle Big Data and adopt a practical approach to the complexity of Big Data. Two Chinese researchers assert deep learning as the “set of machine learning techniques that learn multiple levels of representation in deep architectures (Chen & Lin, 2014, p. 515). Deep learning requires “new ways of thinking and transformative solutions (Chen & Lin, 2014, p. 523). Another pair of researchers from China present a broad overview of the various societal, business and administrative applications of Big Data, including a detailed account and definitions of the processes and tools accompanying Big Data analytics.  The American counterparts of these Chinese researchers are of the same opinion when it comes to “think about the core principles and concepts that underline the techniques, and also the systematic thinking” (Provost and Fawcett, 2013, p. 58). De Mauro, Greco, and Grimaldi (2016), similarly to Provost and Fawcett (2013) draw attention to the urgent necessity to train new types of specialists to work with such data. As early as 2012, Davenport and Patil (2012), as cited in Mauro et al (2016), envisioned hybrid specialists able to manage both technological knowledge and academic research. Similarly, Provost and Fawcett (2013) mention the efforts of “academic institutions scrambling to put together programs to train data scientists” (p. 51). Further, Asomoah, Sharda, Zadeh & Kalgotra (2017) share a specific plan on the design and delivery of a big data analytics course. At the same time, librarians working with data acknowledge the shortcomings in the profession, since librarians “are practitioners first and generally do not view usability as a primary job responsibility, usually lack the depth of research skills needed to carry out a fully valid” data-based research (Emanuel, 2013, p. 207).

Borgman (2015) devotes an entire book to data and scholarly research and goes beyond the already well-established facts regarding the importance of Big Data, the implications of Big Data and the technical, societal, and educational impact and complications posed by Big Data. Borgman elucidates the importance of knowledge infrastructure and the necessity to understand the importance and complexity of building such infrastructure, in order to be able to take advantage of Big Data. In a similar fashion, a team of Chinese scholars draws attention to the complexity of data mining and Big Data and the necessity to approach the issue in an organized fashion (Wu, Xhu, Wu, Ding, 2014).

Bruns (2013) shifts the conversation from the “macro” architecture of Big Data, as focused by Borgman (2015) and Wu et al (2014) and ponders over the influx and unprecedented opportunities for humanities in academia with the advent of Big Data. Does the seemingly ubiquitous omnipresence of Big Data mean for humanities a “railroading” into “scientificity”? How will research and publishing change with the advent of Big Data across academic disciplines?

Reyes (2015) shares her “skinny” approach to Big Data in education. She presents a comprehensive structure for educational institutions to shift “traditional” analytics to “learner-centered” analytics (p. 75) and identifies the participants in the Big Data process in the organization. The model is applicable for library use.

Being a new and unchartered territory, Big Data and Big Data analytics can pose ethical issues. Willis (2013) focusses on Big Data application in education, namely the ethical questions for higher education administrators and the expectations of Big Data analytics to predict students’ success.  Daries, Reich, Waldo, Young, and Whittinghill (2014) discuss rather similar issues regarding the balance between data and student privacy regulations. The privacy issues accompanying data are also discussed by Tene and Polonetsky, (2013).

Privacy issues are habitually connected to security and surveillance issues. Andrejevic and Gates (2014) point out in a decision making “generated by data mining, the focus is not on particular individuals but on aggregate outcomes” (p. 195). Van Dijck (2014) goes into further details regarding the perils posed by metadata and data to the society, in particular to the privacy of citizens. Bail (2014) addresses the same issue regarding the impact of Big Data on societal issues, but underlines the leading roles of cultural sociologists and their theories for the correct application of Big Data.

Library organizations have been traditional proponents of core democratic values such as protection of privacy and elucidation of related ethical questions (Miltenoff & Hauptman, 2005). In recent books about Big Data and libraries, ethical issues are important part of the discussion (Weiss, 2018). Library blogs also discuss these issues (Harper & Oltmann, 2017). An academic library’s role is to educate its patrons about those values. Sugimoto et al (2012) reflect on the need for discussion about Big Data in Library and Information Science. They clearly draw attention to the library “tradition of organizing, managing, retrieving, collecting, describing, and preserving information” (p.1) as well as library and information science being “a historically interdisciplinary and collaborative field, absorbing the knowledge of multiple domains and bringing the tools, techniques, and theories” (p. 1). Sugimoto et al (2012) sought a wide discussion among the library profession regarding the implications of Big Data on the profession, no differently from the activities in other fields (e.g., Wixom, Ariyachandra, Douglas, Goul, Gupta, Iyer, Kulkami, Mooney, Phillips-Wren, Turetken, 2014). A current Andrew Mellon Foundation grant for Visualizing Digital Scholarship in Libraries seeks an opportunity to view “both macro and micro perspectives, multi-user collaboration and real-time data interaction, and a limitless number of visualization possibilities – critical capabilities for rapidly understanding today’s large data sets (Hwangbo, 2014).

The importance of the library with its traditional roles, as described by Sugimoto et al (2012) may continue, considering the Big Data platform proposed by Wu, Wu, Khabsa, Williams, Chen, Huang, Tuarob, Choudhury, Ororbia, Mitra, & Giles (2014). Such platforms will continue to emerge and be improved, with librarians as the ultimate drivers of such platforms and as the mediators between the patrons and the data generated by such platforms.

Every library needs to find its place in the large organization and in society in regard to this very new and very powerful phenomenon called Big Data. Libraries might not have the trained staff to become a leader in the process of organizing and building the complex mechanism of this new knowledge architecture, but librarians must educate and train themselves to be worthy participants in this new establishment.

 

Method

 

The study will be cleared by the SCSU IRB.
The survey will collect responses from library population and it readiness to use and use of Big Data.  Send survey URL to (academic?) libraries around the world.

Data will be processed through SPSS. Open ended results will be processed manually. The preliminary research design presupposes a mixed method approach.

The study will include the use of closed-ended survey response questions and open-ended questions.  The first part of the study (close ended, quantitative questions) will be completed online through online survey. Participants will be asked to complete the survey using a link they receive through e-mail.

Mixed methods research was defined by Johnson and Onwuegbuzie (2004) as “the class of research where the researcher mixes or combines quantitative and qualitative research techniques, methods, approaches, concepts, or language into a single study” (Johnson & Onwuegbuzie, 2004 , p. 17).  Quantitative and qualitative methods can be combined, if used to complement each other because the methods can measure different aspects of the research questions (Sale, Lohfeld, & Brazil, 2002).

 

Sampling design

 

  • Online survey of 10-15 question, with 3-5 demographic and the rest regarding the use of tools.
  • 1-2 open-ended questions at the end of the survey to probe for follow-up mixed method approach (an opportunity for qualitative study)
  • data analysis techniques: survey results will be exported to SPSS and analyzed accordingly. The final survey design will determine the appropriate statistical approach.

 

Project Schedule

 

Complete literature review and identify areas of interest – two months

Prepare and test instrument (survey) – month

IRB and other details – month

Generate a list of potential libraries to distribute survey – month

Contact libraries. Follow up and contact again, if necessary (low turnaround) – month

Collect, analyze data – two months

Write out data findings – month

Complete manuscript – month

Proofreading and other details – month

 

Significance of the work 

While it has been widely acknowledged that Big Data (and its handling) is changing higher education (http://blog.stcloudstate.edu/ims?s=big+data) as well as academic libraries (http://blog.stcloudstate.edu/ims/2016/03/29/analytics-in-education/), it remains nebulous how Big Data is handled in the academic library and, respectively, how it is related to the handling of Big Data on campus. Moreover, the visualization of Big Data between units on campus remains in progress, along with any policymaking based on the analysis of such data (hence the need for comprehensive visualization).

 

This research will aim to gain an understanding on: a. how librarians are handling Big Data; b. how are they relating their Big Data output to the campus output of Big Data and c. how librarians in particular and campus administration in general are tuning their practices based on the analysis.

Based on the survey returns (if there is a statistically significant return), this research might consider juxtaposing the practices from academic libraries, to practices from special libraries (especially corporate libraries), public and school libraries.

 

 

References:

 

Adams Becker, S., Cummins M, Davis, A., Freeman, A., Giesinger Hall, C., Ananthanarayanan, V., … Wolfson, N. (2017). NMC Horizon Report: 2017 Library Edition.

Andrejevic, M., & Gates, K. (2014). Big Data Surveillance: Introduction. Surveillance & Society, 12(2), 185–196.

Asamoah, D. A., Sharda, R., Hassan Zadeh, A., & Kalgotra, P. (2017). Preparing a Data Scientist: A Pedagogic Experience in Designing a Big Data Analytics Course. Decision Sciences Journal of Innovative Education, 15(2), 161–190. https://doi.org/10.1111/dsji.12125

Bail, C. A. (2014). The cultural environment: measuring culture with big data. Theory and Society, 43(3–4), 465–482. https://doi.org/10.1007/s11186-014-9216-5

Borgman, C. L. (2015). Big Data, Little Data, No Data: Scholarship in the Networked World. MIT Press.

Bruns, A. (2013). Faster than the speed of print: Reconciling ‘big data’ social media analysis and academic scholarship. First Monday, 18(10). Retrieved from http://firstmonday.org/ojs/index.php/fm/article/view/4879

Bughin, J., Chui, M., & Manyika, J. (2010). Clouds, big data, and smart assets: Ten tech-enabled business trends to watch. McKinsey Quarterly, 56(1), 75–86.

Chen, X. W., & Lin, X. (2014). Big Data Deep Learning: Challenges and Perspectives. IEEE Access, 2, 514–525. https://doi.org/10.1109/ACCESS.2014.2325029

Cohen, J., Dolan, B., Dunlap, M., Hellerstein, J. M., & Welton, C. (2009). MAD Skills: New Analysis Practices for Big Data. Proc. VLDB Endow., 2(2), 1481–1492. https://doi.org/10.14778/1687553.1687576

Daniel, B. (2015). Big Data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904–920. https://doi.org/10.1111/bjet.12230

Daries, J. P., Reich, J., Waldo, J., Young, E. M., Whittinghill, J., Ho, A. D., … Chuang, I. (2014). Privacy, Anonymity, and Big Data in the Social Sciences. Commun. ACM, 57(9), 56–63. https://doi.org/10.1145/2643132

De Mauro, A. D., Greco, M., & Grimaldi, M. (2016). A formal definition of Big Data based on its essential features. Library Review, 65(3), 122–135. https://doi.org/10.1108/LR-06-2015-0061

De Mauro, A., Greco, M., & Grimaldi, M. (2015). What is big data? A consensual definition and a review of key research topics. AIP Conference Proceedings, 1644(1), 97–104. https://doi.org/10.1063/1.4907823

Dumbill, E. (2012). Making Sense of Big Data. Big Data, 1(1), 1–2. https://doi.org/10.1089/big.2012.1503

Eaton, M. (2017). Seeing Library Data: A Prototype Data Visualization Application for Librarians. Publications and Research. Retrieved from http://academicworks.cuny.edu/kb_pubs/115

Emanuel, J. (2013). Usability testing in libraries: methods, limitations, and implications. OCLC Systems & Services: International Digital Library Perspectives, 29(4), 204–217. https://doi.org/10.1108/OCLC-02-2013-0009

Graham, M., & Shelton, T. (2013). Geography and the future of big data, big data and the future of geography. Dialogues in Human Geography, 3(3), 255–261. https://doi.org/10.1177/2043820613513121

Harper, L., & Oltmann, S. (2017, April 2). Big Data’s Impact on Privacy for Librarians and Information Professionals. Retrieved November 7, 2017, from https://www.asist.org/publications/bulletin/aprilmay-2017/big-datas-impact-on-privacy-for-librarians-and-information-professionals/

Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Ullah Khan, S. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47(Supplement C), 98–115. https://doi.org/10.1016/j.is.2014.07.006

Hwangbo, H. (2014, October 22). The future of collaboration: Large-scale visualization. Retrieved November 7, 2017, from http://usblogs.pwc.com/emerging-technology/the-future-of-collaboration-large-scale-visualization/

Laney, D. (2001, February 6). 3D Data Management: Controlling Data Volume, Velocity, and Variety.

Miltenoff, P., & Hauptman, R. (2005). Ethical dilemmas in libraries: an international perspective. The Electronic Library, 23(6), 664–670. https://doi.org/10.1108/02640470510635746

Philip Chen, C. L., & Zhang, C.-Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275(Supplement C), 314–347. https://doi.org/10.1016/j.ins.2014.01.015

Power, D. J. (2014). Using ‘Big Data’ for analytics and decision support. Journal of Decision Systems, 23(2), 222–228. https://doi.org/10.1080/12460125.2014.888848

Provost, F., & Fawcett, T. (2013). Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data, 1(1), 51–59. https://doi.org/10.1089/big.2013.1508

Reilly, S. (2013, December 12). What does Horizon 2020 mean for research libraries? Retrieved November 7, 2017, from http://libereurope.eu/blog/2013/12/12/what-does-horizon-2020-mean-for-research-libraries/

Reyes, J. (2015). The skinny on big data in education: Learning analytics simplified. TechTrends: Linking Research & Practice to Improve Learning, 59(2), 75–80. https://doi.org/10.1007/s11528-015-0842-1

Schroeder, R. (2014). Big Data and the brave new world of social media research. Big Data & Society, 1(2), 2053951714563194. https://doi.org/10.1177/2053951714563194

Sugimoto, C. R., Ding, Y., & Thelwall, M. (2012). Library and information science in the big data era: Funding, projects, and future [a panel proposal]. Proceedings of the American Society for Information Science and Technology, 49(1), 1–3. https://doi.org/10.1002/meet.14504901187

Tene, O., & Polonetsky, J. (2012). Big Data for All: Privacy and User Control in the Age of Analytics. Northwestern Journal of Technology and Intellectual Property, 11, [xxvii]-274.

van Dijck, J. (2014). Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society; Newcastle upon Tyne, 12(2), 197–208.

Waller, M. A., & Fawcett, S. E. (2013). Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management. Journal of Business Logistics, 34(2), 77–84. https://doi.org/10.1111/jbl.12010

Weiss, A. (2018). Big-Data-Shocks-An-Introduction-to-Big-Data-for-Librarians-and-Information-Professionals. Rowman & Littlefield Publishers. Retrieved from https://rowman.com/ISBN/9781538103227/Big-Data-Shocks-An-Introduction-to-Big-Data-for-Librarians-and-Information-Professionals

West, D. M. (2012). Big data for education: Data mining, data analytics, and web dashboards. Governance Studies at Brookings, 4, 1–0.

Willis, J. (2013). Ethics, Big Data, and Analytics: A Model for Application. Educause Review Online. Retrieved from https://docs.lib.purdue.edu/idcpubs/1

Wixom, B., Ariyachandra, T., Douglas, D. E., Goul, M., Gupta, B., Iyer, L. S., … Turetken, O. (2014). The current state of business intelligence in academia: The arrival of big data. CAIS, 34, 1.

Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97–107. https://doi.org/10.1109/TKDE.2013.109

Wu, Z., Wu, J., Khabsa, M., Williams, K., Chen, H. H., Huang, W., … Giles, C. L. (2014). Towards building a scholarly big data platform: Challenges, lessons and opportunities. In IEEE/ACM Joint Conference on Digital Libraries (pp. 117–126). https://doi.org/10.1109/JCDL.2014.6970157

 

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more on big data





social media algorithms

How algorithms impact our browsing behavior? browsing history?
What is the connection between social media algorithms and fake news?
Are there topic-detection algorithms as they are community-detection ones?
How can I change the content of a [Google] search return? Can I? 

Larson, S. (2016, July 8). What is an Algorithm and How Does it Affect You? The Daily Dot. Retrieved from https://www.dailydot.com/debug/what-is-an-algorithm/
Berg, P. (2016, June 30). How Do Social Media Algorithms Affect You | Forge and Smith. Retrieved September 19, 2017, from https://forgeandsmith.com/how-do-social-media-algorithms-affect-you/
Oremus, W., & Chotiner, I. (2016, January 3). Who Controls Your Facebook Feed. Slate. Retrieved from http://www.slate.com/articles/technology/cover_story/2016/01/how_facebook_s_news_feed_algorithm_works.html
Lehrman, R. A. (2013, August 11). The new age of algorithms: How it affects the way we live. Christian Science Monitor. Retrieved from https://www.csmonitor.com/USA/Society/2013/0811/The-new-age-of-algorithms-How-it-affects-the-way-we-live
Johnson, C. (2017, March 10). How algorithms affect our way of life. Desert News. Retrieved from https://www.deseretnews.com/article/865675141/How-algorithms-affect-our-way-of-life.html
Understanding algorithms and their impact on human life goes far beyond basic digital literacy, some experts said.
An example could be the recent outcry over Facebook’s news algorithm, which enhances the so-called “filter bubble”of information.
personalized search (https://en.wikipedia.org/wiki/Personalized_search)
Kounine, A. (2016, August 24). How your personal data is used in personalization and advertising. Retrieved September 19, 2017, from https://www.tastehit.com/blog/personal-data-in-personalization-and-advertising/
Hotchkiss, G. (2007, March 9). The Pros & Cons Of Personalized Search. Retrieved September 19, 2017, from http://searchengineland.com/the-pros-cons-of-personalized-search-10697
Magid, L. (2012). How (and why) To Turn Off Google’s Personalized Search Results. Forbes. Retrieved from https://www.forbes.com/sites/larrymagid/2012/01/13/how-and-why-to-turn-off-googles-personalized-search-results/#53a30be838f2
Nelson, P. (n.d.). Big Data, Personalization and the No-Search of Tomorrow. Retrieved September 19, 2017, from https://www.searchtechnologies.com/blog/big-data-search-personalization

gender

Massanari, A. (2017). #Gamergate and The Fappening: How Reddit’s algorithm, governance, and culture support toxic technocultures. New Media & Society19(3), 329-346. doi:10.1177/1461444815608807

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community detection algorithms:

Bedi, P., & Sharma, C. (2016). Community detection in social networks. Wires: Data Mining & Knowledge Discovery6(3), 115-135.

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CRUZ, J. D., BOTHOREL, C., & POULET, F. (2014). Community Detection and Visualization in Social Networks: Integrating Structural and Semantic Information. ACM Transactions On Intelligent Systems & Technology5(1), 1-26. doi:10.1145/2542182.2542193

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Bai, X., Yang, P., & Shi, X. (2017). An overlapping community detection algorithm based on density peaks. Neurocomputing2267-15. doi:10.1016/j.neucom.2016.11.019

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topic-detection algorithms:

Zeng, J., & Zhang, S. (2009). Incorporating topic transition in topic detection and tracking algorithms. Expert Systems With Applications36(1), 227-232. doi:10.1016/j.eswa.2007.09.013

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topic detection and tracking (TDT) algorithms based on topic models, such as LDA, pLSI (https://en.wikipedia.org/wiki/Probabilistic_latent_semantic_analysis), etc.

Zhou, E., Zhong, N., & Li, Y. (2014). Extracting news blog hot topics based on the W2T Methodology. World Wide Web17(3), 377-404. doi:10.1007/s11280-013-0207-7

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The W2T (Wisdom Web of Things) methodology considers the information organization and management from the perspective of Web services, which contributes to a deep understanding of online phenomena such as users’ behaviors and comments in e-commerce platforms and online social networks.  (https://link.springer.com/chapter/10.1007/978-3-319-44198-6_10)

ethics of algorithm

Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 2053951716679679. https://doi.org/10.1177/2053951716679679

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

journalism

Malyarov, N. (2016, October 18). Journalism in the age of algorithms, platforms and newsfeeds | News | FIPP.com. Retrieved September 19, 2017, from http://www.fipp.com/news/features/journalism-in-the-age-of-algorithms-platforms-newsfeeds

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