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.
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
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.
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.
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,
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.
Learning to Harness Big Data in an Academic Library
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.
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).
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.
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.
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
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., 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
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
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
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
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
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
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?
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 & Technology, 5(1), 1-26. doi:10.1145/2542182.2542193
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
Top 10 IT Issues, 2017: Foundations for Student Success
Susan Grajek and the 2016–2017 EDUCAUSE IT Issues Panel Tuesday, January 17, 2017http://er.educause.edu/articles/2017/1/top-10-it-issues-2017-foundations-for-student-successThe 2017 EDUCAUSE Top 10 IT Issues are all about student success
Developing a holistic, agile approach to reduce institutional exposure to information security threats
That program should encompass people, process, and technologies:
Develop processes to identify and protect the most sensitive data
Implement technologies to encrypt data and find and block advanced threats coming from outside the network via from any type of device
Who Outside the IT Department Should Care Most about This Issue?
End-users, to understand how to avoid exposing their credentials
Unit heads, to protect institutional data
Senior leaders, to hold people accountable
Institutional leadership, to endorse, fund, and advocate for good information security
Issue #2: Student Success and Completion
Effectively applying data and predictive analytics to improve student success and completion
Predictive analytics allows us to track trends, discover gaps and inefficiencies, and displace “best guess” scenarios based on implicitly developed stories about students.
Issue #3: Data-Informed Decision Making
Ensuring that business intelligence, reporting, and analytics are relevant, convenient, and used by administrators, faculty, and students
Higher education information systems generate vast amounts of data daily (including the classroom/LMS). This potentially rich source of information is underused. Even though most institutions have created reports, dashboards, and other distillations of data, these are not necessarily useful or used to inform strategic objectives such as student success or institutional efficiency.
Issue #4: Strategic Leadership
Repositioning or reinforcing the role of IT leadership as a strategic partner with institutional leadership
CIOs have two challenges in this regard. The first is getting to the table. Contemporary requirements for IT leaders position them well for strategic leadership.18 Those requirements include expertise in management and business practices, project portfolio management, negotiation, and change leadership. However, business-savvy CIOs can alienate some academics, particularly those opposed to administrators as leaders. Worse, not all CIOs are well-equipped for a position at the executive table.
Issue #5: Sustainable Funding
Developing IT funding models that sustain core services, support innovation, and facilitate growth
Two complications have deepened the IT funding challenge in recent years. The first is that information technology is now incontrovertibly core to the mission and function of colleges and universities. The second complication is that at most institutions, digital investments and technology refreshes have been funded with capital expenditures. Yet IT services and infrastructure are moving outside the institution, generally to the cloud, and cloud funding depends on ongoing expenditures rather than one-time investments.
Issue #6: Data Management and Governance
Improving the management of institutional data through data standards, integration, protection, and governance
Data management and governance is not an IT issue. It requires a broad, top-down approach because all departments need to buy in and agree. All stakeholders (data owners as well as IR, IT, and institutional leaders) must collaboratively develop a common set of data definitions and a common understanding of what data is needed, in what format, and for what purposes. This coordination, or governance, will enable constituents to communicate with confidence about the data (e.g., “the single version of truth”) and the standards (e.g., APLU, IPEDS, CDS) under which it is collected.
Institutions often choose to approach data management from three perspectives: (1) accuracy, (2) usability, and (3) privacy. The IT organization has a role to play in creating and maintaining data warehouses, integrating systems to facilitate data exchange, and maintaining standards for data privacy and security.
Issue #7: Higher Education Affordability
Prioritizing IT investments and resources in the context of increasing demand and limited resources
Uncoordinated, redundant expenditures supplant other needed investments, such as consistent classroom technology or dedicated information security staff. Planning needs to occur at the institutional or departmental level, but it also needs a place to coalesce and be assessed regionally, nationally, and in some cases, globally, because there isn’t enough money to do everything that institutional leaders, faculty, and others want or even need to do. Public systems are making some headway in sharing services, but for the most part, local optimization supersedes collaboration and compromise.
Issue #8: Sustainable Staffing
Ensuring adequate staffing capacity and staff retention as budgets shrink or remain flat and as external competition grows
As institutions become more dependent on their IT organizations, IT organizations are more dependent on the expertise and quality of their workforce. New hires need to be great hires, and great staff need to want to stay. Each new hire can change the culture and effectiveness of the IT organizations
Issue #9: Next-Gen Enterprise IT
Developing and implementing enterprise IT applications, architectures, and sourcing strategies to achieve agility, scalability, cost-effectiveness, and effective analytics
Buildings should outlive alumni; technology shouldn’t. IT leaders are examining core enterprise applications, including ERPs (traditionally, suites of financial, HR, and student information systems) and LMSs, for their ability to meet current and future needs.
Issue #10: Digital Transformation of Learning
Collaborating with faculty and academic leadership to apply technology to teaching and learning in ways that reflect innovations in pedagogy and the institutional mission
According to Michael Feldstein and Phil Hill, personalized learning applies technology to three processes: content (moving content delivery out of the classroom and allowing students to set their pace of learning); tutoring (allowing interactive feedback to both students and faculty); and contact time (enabling faculty to observe students’ work and coach them more).
more on IT in this IMS blog http://blog.stcloudstate.edu/ims?s=information+technology
Building/Education 5.0? The Future of Learning, the Future of Schools
1.2 Themes of the Parallel Program
Thursday features a parallel program with four workshop sessions and four presentation sessions, grouped according to the following themes which will be further modified in the CfP in September:
– Participation and Democracy in Education
– Learning Strategies and Instruction
– Human Resource Management / Professionalization of Educational Actors
– Leadership Development
– Migration and Education
– School Turnaround
– Governance and Educational Policy
– Collaboration, Networked Systems and System Leadership
1.4 Pre-Conference: International Seminar
As a pre-conference, the «International Seminar» takes place September 5-6, 2017. It mainly addresses international guests and those who want to network internationally in a more intimate atmosphere. Emphasis is put on the exchange of knowledge and experiences across countries and the discussion of challenges.
Groups of school leaders who participate in professional development programs are particularly welcome. As last time, we already have a few groups who signed up for 2017.
Besides a presentation of the Swiss school system(s), participants will have the opportunity to visit local schools in the canton of Zug.
purpose: draft a document for the provost to plan for charting the future goal 3.12 “develop a comprehensive strategy to increase awareness and development of e-textbooks and open educational resources (OERs)”
SCSU goal: to reduce the cost of textbooks as an affordable learning initiative. Amount of reduction is undetermined
According to Bossaler et al (2014), it might be worth considering that SCSU (MnSCU?) must go first through implementing of e-text[books] in courses first by using publisher materials and then by using “in-house” produce. At this point, SCSU does NOT have an aligned policy of integrating e-texts in courses across campus. Lack of such experience might make a strategy for adoption of e-textbooks much more complex and difficult to implement
stats are colored in green for convenience. Stats regarding the increase in textbook costs are re-printed from author to author: e.g. Acker (2011, p. 42). Murey and Perez (2011, p. 49 (bottom) – 50 (up)) reports stats from 2009 and projections for 2013 regarding etexbook adotion. Same authors, p. 50 second paragraph reports good stats regarding texbooks’ price increase : US$1122 per year for textbooks in 2010.
Wimmer at al (2014) presents a lucid graphic of the structure of the publishing process (see bottom of this blog entry for citation and perm link).
Wimmer at al (2014) discusses copyright and permissions, which is of interest for this research (p. 85)
regarding in-house creation of e-textbooks, see (Distance education, e-learning, education and training, 2015). It very much follow the example of SUNY, which Keith was laying out: a team of faculty charged with creation the e-textbook for mass consumption.
Besides the SUNY model Keith is envisioning for MnSCU (comparable), there is the option of clustering OER sources: e.g. NASTA as per Horejsi (2013), CourseSmart. FlatWorld Knowledge (Murrey and Perez, 2011) etc.
Hamedi & Ezaleila (2015) present an entire etextbook program. Article has been ordered through ILL. Same with Joseph (2015).
Open Educational Resources in Acker (2015, p. 44-47). Also in Murey and Perez (2011, p. 51).
Also in ICWL (Conference) (13th : 2014 : Tallinn, E., & Cao, Y. (2014): OpenDSA
Different models of pricing also in Acker (2015, p. 48). Keith touched on that
students learn equally well from etextbooks as from paper ones: Taylor (2011)
my note: there is no good definition about e-textbook in terms of the complexity, which e-textbook on campus might involve.
Considering Wimmer et al (2014) account on their campus experience in publishing e-textbook, a textbook may involve an LMS (Canvas) and blog (WordPress). Per my proposal during the F2F meeting, and following Rachel’s suggestion about discrimination of the different types of e-textbooks, here is an outline of e-textbook definition:
working definition for e-textbook for the purposes of SCSU:
e-textbook is a compilation of textual, multimedia and interactive material, which can be viewed on various electronic devices. E-textbook can: 1. be purchased from a publisher; 2. compiled in HTML format on faculty or group web space; 3. compiled on the content module of LMS (BB, D2L, Canvas, Moodle, etc.) 4. compiled on LMS (BB, D2L, Canvas, Moodle, etc.) and including all interactive materials: e.g. hyperlinks to MediaSpace multimedia, quizzes, etc.; 5. compiled on special apps, such as iBook Author, eCub, Sigil.
(Electronic-BOOK) The electronic counterpart of a printed book, which can be viewed on a desktop computer, laptop, smartphone, tablet or e-book reader (e-reader). When traveling, a huge number of e-books can be stored in portable units, dramatically eliminating weight and volume compared to paper. Electronic bookmarks make referencing easier, and e-book readers may allow the user to annotate pages.
Although fiction and non-fiction books come in e-book formats, technical material is especially suited for e-book delivery because it can be searched. In addition, programming code examples can be copied, which is why CD-ROMs that contained examples or the entire text were often packaged inside technical paper books.
Wimmer, Morrow, & Weber: Collaboration in eTextbook Publishing
There are several e-book formats on the market, including EPUB, Mobipocket (PRC, MOBI), eReader (PDB), Kindle (AZW, KF8) and Apple iBook (EPUB variation). Many e-readers also accept generic formats, including Adobe PDF and plain text (TXT).
According to a United States Government report, textbook prices have increased at over twice the rate of inflation in the last couple of decades. According to another report, the average student spends between $700 and $1,000 per year on textbooks while the cost of e-textbooks can be as much as 50% lower than paper textbooks.
Oxford dictionary, an electronic book or e-book is “an electronic version of a printed book that can be read on a computer or handheld device designed specifically for this purpose.” An e-textbook is defined as an e-book used for instructional or educational purposes and often includes features such as bookmarking, searching, highlighting, and note-taking as well as built-in dictionaries and pronunciation guides, embedded video-clips, embedded hyperlinks, and animated graphics.
E-textbooks have moved from occasional usage to a mainstream technology on college campuses. According to the Association of American Publishers, sales of e-books hit over $90 million; this is up over 200% when compared to the same month the previous year. When the cost of textbooks and the availability of formats are considered, the use of an e-textbook in the classroom may be the reasonable choice.
A digital textbook is a digital book or e-book intended to serve as the text for a class. Digital textbooks may also be known as e-textbooks or e-texts. Digital textbooks are a major component of technology-based education reform. They may serve as the texts for a traditional face-to-face class, an online course or degree.
The concepts of open access and open source support the idea of open textbooks, digital textbooks that are free (gratis) and easy to distribute, modify and update https://en.wikipedia.org/wiki/Digital_textbook
Exploring Students’ E-Textbook Practices in Higher Education
Authors: by Aimee Denoyelles, John Raible and Ryan Seilhamer Published: Monday, July 6, 2015. Instructional Designers, University of Central Florida
According to the United States Government Accountability Office, prices have increased 82 percent from 2002 to 2012.3This cost sometimes drives students to delay or avoid purchasing textbooks. Digital materials such as e-textbooks may offer a more cost-effective alternative.4 Also, the expectation for digital materials is gaining strength in the K–12 sector.5 For example, Florida school districts set a goal to spend at least half of classroom material funding on digital materials by the 2015–2016 school year. Given that 81 percent of first-time-in-college (FTIC) undergraduate students hailed from a Florida public high school during the fall 2014 semester at the University of Central Florida (UCF), it is important to anticipate student expectations of digital materials. Finally, the availability of digital materials has risen exponentially with the incredible popularity of mobile devices.
Despite the advantages that e-textbooks pose, such as interactive features and accessibility on mobile devices, several barriers exist regarding implementation in higher education, namely non-standardization of the platform, limited use by students, and the unclear role of the instructor in adoption.
a survey questionnaire in 2012 that explored basic usage and attitudes regarding e-textbooks.
Bossaller, J., & Kammer, J. (2014). Faculty Views on eTextbooks: A Narrative Study. College Teaching, 62(2), 68-75. doi:10.1080/87567555.2014.885877
This qualitative study gives insight into the experiences instructors have when working with publishers to integrate electronic content and technology into their courses.
Baek, E., & Monaghan, J. (2013). Journey to Textbook Affordability: An Investigation of Students’ Use of eTextbooks at Multiple Campuses. International Review Of Research In Open And Distance Learning, 14(3), 1-26.
the Advisory Committee on Student Financial Assistance (2007) reported that textbook prices represent a significant barrier to students’ accessibility to textbooks. The report concluded that textbooks cost between $700-$1000 per year; textbook prices have risen much faster than other commodities; and that college aid fails to cover textbook expenses. Textbook costs are equivalent to 26% of tuition costs for an average four-year public university student and 72% of tuition costs for an average community college student. In fact, the California State Auditor (2008) reported that textbook costs grew more rapidly than student fees in academic year 2007–08.
Much of the research related to digital texts has focused ontechnical aspects of readability (see Dillon, 1992, for a review) and limitations of digital media for note-taking, underlining, or highlighting text (Brown, 2001). However, the important—and unanswered—question from a teaching perspective is, ‘‘Can students learn as well from digital texts as from paperbound textbooks?’’ Few published studies have addressed this ques-tion directly, and even fewer studies have examined this ques-tion among college students.
Murray, M. C., & Pérez, J. (2011). E-Textbooks Are Coming: Are We Ready?. Issues In Informing Science & Information Technology, 849-60.
Pilot projects that can help build institutional expertise
Address how and where insights gained from pilot projects will be collected and
People resources (e.g., instructional designers) that will be needed to assist
instructors to use this technology
ICWL (Conference) (13th : 2014 : Tallinn, E., & Cao, Y. (2014). New horizons in web based learning: ICWL 2014 international workshops, SPeL, PRASAE, IWMPL, OBIE, and KMEL, FET, Tallinn, Estonia, August 14-17, 2014, revised selected papers. Cham: Springer.
MnSCU will by as Content Authoring Tool – SoftChalk. Here is a promo from Softchalk (my bold):
NEW SoftChalk Create 10 and SoftChalk Cloud eBook publishing features will arrive on April 25th! Come check out the latest enhancements at our upcoming webinars!
Sleek Designer Headers and Callout Boxes – Add some new pizazz to your SoftChalk lessons!
Three New Quiz Types – Test your students’ understanding with Sentence Completion, Multiple Blanks and Feedback Questions.
Polished New QuizPopper and Activity displays – With an enhanced interface for instructors and students.
Accessibility enhancements – Make your lessons available to everyone with even more accessibility enhancements.
NEW SoftChalk Cloud eBook creation and publishing – Includes a totally re-vamped, easier eBook creation and management. New SoftChalk eReader apps available for free download in the iOS, Android, Chromebook and Windows app stores. (Cloud Only)