Support analytics initiatives with data integration and governance. The changing landscape of enterprise IT is characterized by an expanding set of services, systems, and sourcing strategies. Data governance, cross-enterprise partnerships, and data integration are key ingredients in supporting higher education’s growing need for reliable information.
Enterprise IT Case Studies
In this set of EDUCAUSE Reviewcase studies, see how Drake University, the University of Tennessee, and the University of Montana improved their analytics initiatives through data integrations and governance.
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.
AI is being used to monitor students and their work. The most prominent uses of AI in higher education are attached to applications designed to protect or preserve academic integrity through the use of plagiarism-detection software (60%) and proctoring applications (42%) (see figure 1).
The chatbots are coming! A sizable percentage (36%) of respondents reported that chatbots and digital assistants are in use at least somewhat on their campuses, with another 17% reporting that their institutions are in the planning, piloting, and initial stages of use (see figure 2). The use of chatbots in higher education by admissions, student affairs, career services, and other student success and support units is not entirely new, but the pandemic has likely contributed to an increase in their use as they help students get efficient, relevant, and correct answers to their questions without long waits.Footnote10 Chatbots may also liberate staff from repeatedly responding to the same questions and reduce errors by deploying updates immediately and universally.
AI is being used for student success tools such as identifying students who are at-risk academically (22%) and sending early academic warnings (16%); another 14% reported that their institutions are in the stage of planning, piloting, and initial usage of AI for these tasks.
Nearly three-quarters of respondents said that ineffective data management and integration (72%) and insufficient technical expertise (71%) present at least a moderate challenge to AI implementation. Financial concerns (67%) and immature data governance (66%) also pose challenges. Insufficient leadership support (56%) is a foundational challenge that is related to each of the previous listed challenges in this group.
Current use of AI
Chatbots for informational and technical support, HR benefits questions, parking questions, service desk questions, and student tutoring
Research applications, conducting systematic reviews and meta-analyses, and data science research (my italics)
Library services (my italics)
Recruitment of prospective students
Providing individual instructional material pathways, assessment feedback, and adaptive learning software
Proctoring and plagiarism detection
Student engagement support and nudging, monitoring well-being, and predicting likelihood of disengaging the institution
1. Information security: Developing a risk-based security strategy that keeps pace with security threats and challenges.
2. Student success: Managing the system implementations and integrations that support multiple student success initiatives.
3. Institution-wide IT strategy: Repositioning or reinforcing the role of IT leadership as an integral strategic partner of institutional leadership in achieving institutions missions.
4. Data-enabled institutional culture: Using BI and analytics to inform the broad conversation and answer big questions.
5. Student-centered institution: Understanding and advancing technology’s role in defining the student experience on campus (from applicants to alumni).
6. Higher education affordability: Balancing and rightsizing IT priorities and budget to support IT-enabled institutional efficiencies and innovations in the context if institutional funding realities.
7. IT staffing and organizational models: Ensuring adequate staffing capacity and staff retention in the face of retirements, new sourcing models, growing external competition, rising salaries, and the demands of technology initiatives on both IT and non-IT staff.
8. (tie) Data management and governance: Implementing effective institutional data governance practices.
9. (tie) Digital integrations: Ensuring system interoperability, scalability, and extensibility, as well as data integrity, standards, and governance, across multiple applications and platforms.
10. Change leadership: Helping institutional constituents (including the IT staff) adapt to the increasing pace of technology change.
It requires that companies become what we call digital masters. Digital masters cultivate two capabilities: digital capability, which enables them to use innovative technologies to improve elements of the business, and leadership capability, which enables them to envision and drive organizational change in systematic and profitable ways. Together, these two capabilities allow a company to transform digital technology into business advantage.
We found that the elements of leadership capability have endured, but new elements of digital capability have come to the fore.
While strong leadership capability is even more essential than ever, its core elements — vision, engagement, and governance — are not fundamentally changed, though they are informed by recent innovations. The elements of digital capability, on the other hand, have been more profoundly altered by the rapid technological advances of recent years.
Experience design: Customer experience has become the ultimate battleground for many companies and brands.
Customer intelligence: Integrating customer data across silos and understanding customer behavior
Emotional engagement: Emotional connections with customers are as essential as technology in creating compelling customer experiences.
As ever, well-managed operations are essential to converting revenue into profit, but now we’re seeing a shift in the focus of digital transformation in this arena.
Core process automation: Amazon’s distribution centers deliver inventory to workers rather than sending workers to collect inventory. Rio Tinto, an Australian mining company, uses autonomous trucks, trains, and drilling machinery so that it can shift workers to less dangerous tasks, leading to higher productivity and better safety.
Connected and dynamic operations: Thanks to the growing availability of cheap sensors, cloud infrastructure, and machine learning, concepts such as Industry 4.0, digital threads, and digital twins have become a reality. Digital threads connecting machines, models, and processes provide a single source of truth to manage, optimize, and enhance processes from requirements definition through maintenance.
Data-driven decision-making: from backward-looking reports to real-time data. Now, connected devices, new machine learning algorithms, smarter experimentation, and plentiful data enable more-informed decisions.
Transforming Employee Experience
Augmentation: Warnings that robots will replace humans have given way to a more nuanced and productive discussion.
Workers in Huntington Ingalls Industries’ shipyard use augmented reality to help build giant complex vessels such as aircraft carriers and submarines. They can “see” where to route wires or pipes or what is behind a wall before they start drilling into it.
Future-readying: providing employees with the skills they need to keep up with the pace of change. In the past few years, this has given rise to new models of managing learning and development in organizations, led by a new kind of chief learning officer, whom we call the transformer CLO
Flexforcing: To respond to fast-paced digital opportunities and threats, companies also need to build agility into their talent sourcing systems. As automation and AI applications take over tasks once performed by humans, some companies are multiskilling employees to make the organization more agile.
Transforming Business Models
three elements supporting business model transformation: digital enhancements, information-based service extensions, and multisided platforms.
Holland, B. (2020). Emerging Technology and Today’s Libraries. In Holland, B. (Eds.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 1-33). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch001
The purpose of this chapter is to examine emerging technology and today’s libraries. New technology stands out first and foremost given that they will end up revolutionizing every industry in an age where digital transformation plays a major role. Major trends will define technological disruption. The next-gen of communication, core computing, and integration technologies will adopt new architectures. Major technological, economic, and environmental changes have generated interest in smart cities. Sensing technologies have made IoT possible, but also provide the data required for AI algorithms and models, often in real-time, to make intelligent business and operational decisions. Smart cities consume different types of electronic internet of things (IoT) sensors to collect data and then use these data to manage assets and resources efficiently. This includes data collected from citizens, devices, and assets that are processed and analyzed to monitor and manage, schools, libraries, hospitals, and other community services.
Makori, E. O. (2020). Blockchain Applications and Trends That Promote Information Management. In Holland, B. (Eds.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 34-51). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch002
Blockchain revolutionary paradigm is the new and emerging digital innovation that organizations have no choice but to embrace and implement in order to sustain and manage service delivery to the customers. From disruptive to sustaining perspective, blockchain practices have transformed the information management environment with innovative products and services. Blockchain-based applications and innovations provide information management professionals and practitioners with robust and secure opportunities to transform corporate affairs and social responsibilities of organizations through accountability, integrity, and transparency; information governance; data and information security; as well as digital internet of things.
Hahn, J. (2020). Student Engagement and Smart Spaces: Library Browsing and Internet of Things Technology. In Holland, B. (Eds.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 52-70). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch003
The purpose of this chapter is to provide evidence-based findings on student engagement within smart library spaces. The focus of smart libraries includes spaces that are enhanced with the internet of things (IoT) infrastructure and library collection maps accessed through a library-designed mobile application. The analysis herein explored IoT-based browsing within an undergraduate library collection. The open stacks and mobile infrastructure provided several years (2016-2019) of user-generated smart building data on browsing and selecting items in open stacks. The methods of analysis used in this chapter include transactional analysis and data visualization of IoT infrastructure logs. By analyzing server logs from the computing infrastructure that powers the IoT services, it is possible to infer in greater detail than heretofore possible the specifics of the way library collections are a target of undergraduate student engagement.
Treskon, M. (2020). Providing an Environment for Authentic Learning Experiences. In Holland, B. (Eds.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 71-86). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch004
The Loyola Notre Dame Library provides authentic learning environments for undergraduate students by serving as “client” for senior capstone projects. Through the creative application of IoT technologies such as Arduinos and Raspberry Pis in a library setting, the students gain valuable experience working through software design methodology and create software in response to a real-world challenge. Although these proof-of-concept projects could be implemented, the library is primarily interested in furthering the research, teaching, and learning missions of the two universities it supports. Whether the library gets a product that is worth implementing is not a requirement; it is a “bonus.”
Rashid, M., Nazeer, I., Gupta, S. K., & Khanam, Z. (2020). Internet of Things: Architecture, Challenges, and Future Directions. In Holland, B. (Ed.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 87-104). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch005
The internet of things (IoT) is a computing paradigm that has changed our daily livelihood and functioning. IoT focuses on the interconnection of all the sensor-based devices like smart meters, coffee machines, cell phones, etc., enabling these devices to exchange data with each other during human interactions. With easy connectivity among humans and devices, speed of data generation is getting multi-fold, increasing exponentially in volume, and is getting more complex in nature. In this chapter, the authors will outline the architecture of IoT for handling various issues and challenges in real-world problems and will cover various areas where usage of IoT is done in real applications. The authors believe that this chapter will act as a guide for researchers in IoT to create a technical revolution for future generations.
Martin, L. (2020). Cloud Computing, Smart Technology, and Library Automation. In Holland, B. (Eds.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 105-123). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch006
As technology continues to change, the landscape of the work of librarians and libraries continue to adapt and adopt innovations that support their services. Technology also continues to be an essential tool for dissemination, retrieving, storing, and accessing the resources and information. Cloud computing is an essential component employed to carry out these tasks. The concept of cloud computing has long been a tool utilized in libraries. Many libraries use OCLC to catalog and manage resources and share resources, WorldCat, and other library applications that are cloud-based services. Cloud computing services are used in the library automation process. Using cloud-based services can streamline library services, minimize cost, and the need to have designated space for servers, software, or other hardware to perform library operations. Cloud computing systems with the library consolidate, unify, and optimize library operations such as acquisitions, cataloging, circulation, discovery, and retrieval of information.
Owusu-Ansah, S. (2020). Developing a Digital Engagement Strategy for Ghanaian University Libraries: An Exploratory Study. In Holland, B. (Eds.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 124-139). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch007
This study represents a framework that digital libraries can leverage to increase usage and visibility. The adopted qualitative research aims to examine a digital engagement strategy for the libraries in the University of Ghana (UG). Data is collected from participants (digital librarians) who are key stakeholders of digital library service provision in the University of Ghana Library System (UGLS). The chapter reveals that digital library services included rare collections, e-journal, e-databases, e-books, microfilms, e-theses, e-newspapers, and e-past questions. Additionally, the research revealed that the digital library service patronage could be enhanced through outreach programmes, open access, exhibitions, social media, and conferences. Digital librarians recommend that to optimize digital library services, literacy programmes/instructions, social media platforms, IT equipment, software, and website must be deployed. In conclusion, a DES helps UGLS foster new relationships, connect with new audiences, and establish new or improved brand identity.
Nambobi, M., Ssemwogerere, R., & Ramadhan, B. K. (2020). Implementation of Autonomous Library Assistants Using RFID Technology. In Holland, B. (Ed.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 140-150). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch008
This is an interesting time to innovate around disruptive technologies like the internet of things (IoT), machine learning, blockchain. Autonomous assistants (IoT) are the electro-mechanical system that performs any prescribed task automatically with no human intervention through self-learning and adaptation to changing environments. This means that by acknowledging autonomy, the system has to perceive environments, actuate a movement, and perform tasks with a high degree of autonomy. This means the ability to make their own decisions in a given set of the environment. It is important to note that autonomous IoT using radio frequency identification (RFID) technology is used in educational sectors to boost the research the arena, improve customer service, ease book identification and traceability of items in the library. This chapter discusses the role, importance, the critical tools, applicability, and challenges of autonomous IoT in the library using RFID technology.
Priya, A., & Sahana, S. K. (2020). Processor Scheduling in High-Performance Computing (HPC) Environment. In Holland, B. (Ed.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 151-179). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch009
Processor scheduling is one of the thrust areas in the field of computer science. The future technologies use a huge amount of processing for execution of their tasks like huge games, programming software, and in the field of quantum computing. In real-time, many complex problems are solved by GPU programming. The primary concern of scheduling is to reduce the time complexity and manpower. Several traditional techniques exit for processor scheduling. The performance of traditional techniques is reduced when it comes to the huge processing of tasks. Most scheduling problems are NP-hard in nature. Many of the complex problems are recently solved by GPU programming. GPU scheduling is another complex issue as it runs thousands of threads in parallel and needs to be scheduled efficiently. For such large-scale scheduling problems, the performance of state-of-the-art algorithms is very poor. It is observed that evolutionary and genetic-based algorithms exhibit better performance for large-scale combinatorial and internet of things (IoT) problems.
Kirsch, B. (2020). Virtual Reality in Libraries. In Holland, B. (Eds.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 180-193). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch010
Librarians are beginning to offer virtual reality (VR) services in libraries. This chapter reviews how libraries are currently using virtual reality for both consumption and creation purposes. Virtual reality tools will be compared and contrasted, and recommendations will be given for purchasing and circulating headsets and VR equipment. Google Tour Creator and a smartphone or 360-degree camera can be used to create a virtual tour of the library and other virtual reality content. These new library services will be discussed along with practical advice and best practices for incorporating virtual reality into the library for instructional and entertainment purposes.
Heffernan, K. L., & Chartier, S. (2020). Augmented Reality Gamifies the Library: A Ride Through the Technological Frontier. In Holland, B. (Ed.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 194-210). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch011
Two librarians at a University in New Hampshire attempted to integrate gamification and mobile technologies into the exploration of, and orientation to, the library’s services and resources. From augmented reality to virtual escape rooms and finally an in-house app created by undergraduate, campus-based, game design students, the library team learned much about the triumphs and challenges that come with attempting to utilize new technologies to reach users in the 21st century. This chapter is a narrative describing years of various attempts, innovation, and iteration, which have led to the library team being on the verge of introducing an app that could revolutionize campus discovery and engagement.
Miltenoff, P. (2020). Video 360 and Augmented Reality: Visualization to Help Educators Enter the Era of eXtended Reality. In Holland, B. (Eds.), Emerging Trends and Impacts of the Internet of Things in Libraries (pp. 211-225). IGI Global. http://doi:10.4018/978-1-7998-4742-7.ch012
The advent of all types of eXtended Reality (XR)—VR, AR, MR—raises serious questions, both technological and pedagogical. The setup of campus services around XR is only the prelude to the more complex and expensive project of creating learning content using XR. In 2018, the authors started a limited proof-of-concept augmented reality (AR) project for a library tour. Building on their previous research and experience creating a virtual reality (VR) library tour, they sought a scalable introduction of XR services and content for the campus community. The AR library tour aimed to start us toward a matrix for similar services for the entire campus. They also explored the attitudes of students, faculty, and staff toward this new technology and its incorporation in education, as well as its potential and limitations toward the creation of a “smart” library.
Education scholars have already critiqued PISA as a valid global measure of education quality — but analysts also are skeptical about the selective participation of Chinese students from wealthier schools.
Second, Chinese students, on average, study 55 hours a week — also No. 1 among PISA-participating countries. This was about 20 hours more than students in Finland, the country that PISA declared to have the highest learning efficiency, or reading-test-score points per hour spent studying.
But PISA analysis also revealed that Chinese students are among the least satisfied with their lives.
Students look overseas for a more well-rounded education
Their top destination of choice, by far, is the United States. The 1.1 million or so foreign students in the United States in 2018 included 369,500 Chinese college students
hostility in U.S.-China relations could dampen the appeal of a U.S. education. Britain, in fact, recorded a 30 percent surge in Chinese applicants in 2019, challenging the U.S. global dominance in higher education.
Immigrant students, who made up 23 percent of all U.S. students taking PISA, performed significantly better compared to their native-born peers in the United States than they did on average throughout the OECD countries.
The survey found that 15-year-old students from Beijing, Shanghai, and the eastern provinces of Jiangsu and Zhejiang ranked top for all three core subjects, achieving the highest level 4 rating.
Students from the United States were ranked level 3 for reading and science, and level 2 for math, while teens from Britain scored a level 3 ranking in all three categories.
++++++++++++++++
Looking for Post-PISA Answers? Here’s What Our Obsession With Test Scores Overlooks
Andreas Schelicher, director of education and skills at the OECD—the Paris-based organization behind PISA wrote that “students who disagreed or strongly disagreed with the statement ‘Your intelligence is something about you that you can’t change very much’ scored 32 points higher in reading than students who agreed or strongly agreed.”
Those results are similar to recent findings published by Carol Dweck, a Stanford education professor who is often credited with making growth mindset a mainstream concept.
“Growth mindset is a very important thing that makes us active learners, and makes us invest in our personal education,” Schleicher states. “If learning isn’t based on effort and intelligence is predetermined, why would anyone bother?”
It’s “absolutely fascinating” to see the relationship between teachers’ enthusiasm, students’ social-emotional wellbeing and their learning outcomes, Schleicher notes. As one example, he noted in his summary report that “in most countries and economies, students scored higher in reading when they perceived their teachers as more enthusiastic, especially when they said their teachers were interested in the subject.”
In other words, happy teachers lead to better results. That’s hardly a surprising revelation, says Scheleicher. But professional development support is one thing that can sometimes be overlooked by policymakers when so much of the focus is on test scores.
what i find most important: Future IT Workforce: Deploying a broad array of modern recruitment, retention, and employment practices to develop a resilient IT talent pipeline for the institution
Digital Integrations: Ensuring system interoperability, scalability, and extensibility, as well as data integrity, security, standards, and governance, across multiple applications and platforms
Engaged Learning: Incorporating technologies that enable students to create content and engage in active learning in course curricula
Student Retention and Completion: Developing the capabilities and systems to incorporate artificial intelligence into student services to provide personalized, timely support
Administrative Simplification: Applying user-centered design, process improvement, and system reengineering to reduce redundant or unnecessary efforts and improve end-user experiences
Improved Enrollment: Using technology, data, and analytics to develop an inclusive and financially sustainable enrollment strategy to serve more and new learners by personalizing recruitment, enrollment, and learning experiences
Workforce of the Future: Using technology to develop curriculum, content, and learning experiences that prepare students for the evolving workforce
Holistic Student Success: Applying technology and data, including artificial intelligence, to understand and address the numerous contributors to student success, from finances to health and wellness to academic performance and degree planning (my note: this is what Christine Waisner, Mark Gill and Plamen Miltenoff are trying to do with their VR research)
Improved Teaching: Strengthening engagement among faculty, technologists, and researchers to achieve the true and expanding potential of technology to improve teaching
Student-Centric Higher Education: Creating a student-services ecosystem to support the entire student life cycle, from prospecting to enrollment, learning, job placement, alumni engagement, and continuing education
The term “digital humanities” can refer to research and instruction that is about information technology or that uses IT. By applying technologies in new ways, the tools and methodologies of digital humanities open new avenues of inquiry and scholarly production. Digital humanities applies computational capabilities to humanistic questions, offering new pathways for scholars to conduct research and to create and publish scholarship. Digital humanities provides promising new channels for learners and will continue to influence the ways in which we think about and evolve technology toward better and more humanistic ends.
As defined by Johanna Drucker and colleagues at UCLA, the digital humanities is “work at the intersection of digital technology and humanities disciplines.” An EDUCAUSE/CNI working group framed the digital humanities as “the application and/or development of digital tools and resources to enable researchers to address questions and perform new types of analyses in the humanities disciplines,” and the NEH Office of Digital Humanities says digital humanities “explore how to harness new technology for thumanities research as well as those that study digital culture from a humanistic perspective.” Beyond blending the digital with the humanities, there is an intentionality about combining the two that defines it.
digital humanities can include
creating digital texts or data sets;
cleaning, organizing, and tagging those data sets;
applying computer-based methodologies to analyze them;
and making claims and creating visualizations that explain new findings from those analyses.
Scholars might reflect on
how the digital form of the data is organized,
how analysis is conducted/reproduced, and
how claims visualized in digital form may embody assumptions or biases.
Digital humanities can enrich pedagogy as well, such as when a student uses visualized data to study voter patterns or conducts data-driven analyses of works of literature.
Digital humanities usually involves work by teams in collaborative spaces or centers. Team members might include
researchers and faculty from multiple disciplines,
graduate students,
librarians,
instructional technologists,
data scientists and preservation experts,
technologists with expertise in critical computing and computing methods, and undergraduates
some disciplinary associations, including the Modern Language Association and the American HistoricalAssociation, have developed guidelines for evaluating digital proj- ects, many institutions have yet to define how work in digital humanities fits into considerations for tenure and promotion
Because large projects are often developed with external funding that is not readily replaced by institutional funds when the grant ends sustainability is a concern. Doing digital humanities well requires access to expertise in methodologies and tools such as GIS, mod- eling, programming, and data visualization that can be expensive for a single institution to obtain
Resistance to learning new tech- nologies can be another roadblock, as can the propensity of many humanists to resist working in teams. While some institutions have recognized the need for institutional infrastructure (computation and storage, equipment, software, and expertise), many have not yet incorporated such support into ongoing budgets.
Opportunities for undergraduate involvement in research, provid ing students with workplace skills such as data management, visualization, coding, and modeling. Digital humanities provides new insights into policy-making in areas such as social media, demo- graphics, and new means of engaging with popular culture and understanding past cultures. Evolution in this area will continue to build connections between the humanities and other disci- plines, cross-pollinating research and education in areas like med- icine and environmental studies. Insights about digital humanities itself will drive innovation in pedagogy and expand our conceptualization of classrooms and labs
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.
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 (https://blog.stcloudstate.edu/ims?s=big+data) as well as academic libraries (https://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.
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