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.
Please have also materials, which might help you organize our thoughts and expedite your Chapter 2 writing….
Do you agree with (did you use) the following observations:
The purpose of the review of the literature is to prove that no one has studied the gap in the knowledge outlined in Chapter 1. The subjects in the Review of Literature should have been introduced in the Background of the Problem in Chapter 1. Chapter 2 is not a textbook of subject matter loosely related to the subject of the study. Every research study that is mentioned should in some way bear upon the gap in the knowledge, and each study that is mentioned should end with the comment that the study did not collect data about the specific gap in the knowledge of the study as outlined in Chapter 1.
The review should be laid out in major sections introduced by organizational generalizations. An organizational generalization can be a subheading so long as the last sentence of the previous section introduces the reader to what the next section will contain. The purpose of this chapter is to cite major conclusions, findings, and methodological issues related to the gap in the knowledge from Chapter 1. It is written for knowledgeable peers from easily retrievable sources of the most recent issue possible.
Empirical literature published within the previous 5 years or less is reviewed to prove no mention of the specific gap in the knowledge that is the subject of the dissertation is in the body of knowledge. Common sense should prevail. Often, to provide a history of the research, it is necessary to cite studies older than 5 years. The object is to acquaint the reader with existing studies relative to the gap in the knowledge and describe who has done the work, when and where the research was completed, and what approaches were used for the methodology, instrumentation, statistical analyses, or all of these subjects.
If very little literature exists, the wise student will write, in effect, a several-paragraph book report by citing the purpose of the study, the methodology, the findings, and the conclusions. If there is an abundance of studies, cite only the most recent studies. Firmly establish the need for the study. Defend the methods and procedures by pointing out other relevant studies that implemented similar methodologies. It should be frequently pointed out to the reader why a particular study did not match the exact purpose of the dissertation.
The Review of Literature ends with a Conclusion that clearly states that, based on the review of the literature, the gap in the knowledge that is the subject of the study has not been studied. Remember that a “summary” is different from a “conclusion.” A Summary, the final main section, introduces the next chapter.
When conducting qualitative data, how many people should be interviewed? Is there a minimum or a max
Here is my take on it:
Simple question, not so simple answer.
It depends.
Generally, the number of respondents depends on the type of qualitative inquiry: case study methodology, phenomenological study, ethnographic study, or ethnomethodology. However, a rule of thumb is for scholars to achieve saturation point–that is the point in which no fresh information is uncovered in response to an issue that is of interest to the researcher.
If your qualitative method is designed to meet rigor and trustworthiness, thick, rich data is important. To achieve these principles you would need at least 12 interviews, ensuring your participants are the holders of knowledge in the area you intend to investigate. In grounded theory you could start with 12 and interview more if your data is not rich enough.
In IPA the norm tends to be 6 interviews.
You may check the sample size in peer reviewed qualitative publications in your field to find out about popular practice. In all depends on the research problem, choice of specific qualitative approach and theoretical framework, so the answer to your question will vary from few to few dozens.
How many interviews are needed in a qualitative research?
There are different views in literature and no one agreed to the exact number. Here I reviewed some mostly cited references. Based Creswell (2014), it is estimated that 16 participants will provide rich and detailed data. There are a couple of researchers agreed on 10–15 in-depth interviews are sufficient (Guest, Bunce & Johnson 2006; Baker & Edwards 2012).
your methodological choices need to reflect your ontological position and understanding of knowledge production, and that’s also where you can argue a strong case for smaller qualitative studies, as you say. This is not only a problem for certain subjects, I think it’s a problem in certain departments or journals across the board of social science research, as it’s a question of academic culture.
here more serious literature and research (in case you need to cite in Chapter 3)
Sample Size and Saturation in PhD Studies Using Qualitative Interviews
Gaskell, George (2000). Individual and Group Interviewing. In Martin W. Bauer & George Gaskell (Eds.), Qualitative Researching With Text, Image and Sound. A Practical Handbook (pp. 38-56). London: SAGE Publications.
Savolainen, Jukka 1994: “The Rationality of Drawing Big Conclusions Based on Small Samples.” Social Forces 72:1217-24. (http://www.jstor.org/pss/2580299).
Small, M.(2009) ‘How many cases do I need ? On science and the logic of case selection in field-based research’ Ethnography 10(1) 5-38
Williams,M. (2000) ‘Interpretivism and generalisation ‘ Sociology 34(2) 209-224
where you have several documents from the Graduate school and myself to start building your understanding and vocabulary regarding your quantitative, qualitative or mixed method research.
It has been agreed that before you go to the Statistical Center (Randy Kolb), it is wise to be prepared and understand the terminology as well as the basics of the research methods.
Please have an additional list of materials available through the SCSU library and the Internet. They can help you further with building a robust foundation to lead your research:
Books on intro to stat modeling available at the library. I understand the major pain borrowing books from the SCSU library can constitute, but you can use the titles and the authors and see if you can borrow them from your local public library
I also sought and shared with you “visual” explanations of the basics terms and concepts. Once you start looking at those, you should be able to further research (e.g. YouTube) and find suitable sources for your learning style.
I (and the future cohorts) will deeply appreciate if you remember to share those “suitable sources for your learning style” either by sharing in this Google Group thread and/or sharing in the comments section of the blog entry: https://blog.stcloudstate.edu/ims/2017/07/10/intro-to-stat-modeling. Your Facebook group page is also a good place to discuss among ourselves best practices to learn and use research methods for your chapter 3.
Watching the video, you may remember the same #BooleanSearch techniques from our BI (bibliography instruction) session of last semester.
Considering the fact of preponderance of information in 2017: your Chapter 2 is NOT ONLY about finding information regrading your topic.
Your Chapter 2 is about proving your extensive research of the existing literature.
The techniques presented in the short video will arm you with methods to dig deeper and look further.
If you would like to do a decent job exploring all corners of the vast area called Internet, please consider other search engines similar to Google Scholar:
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.
References:
Adams Becker, S., Cummins M, Davis, A., Freeman, A., Giesinger Hall, C., Ananthanarayanan, V., … Wolfson, N. (2017). NMC Horizon Report: 2017 Library Edition.
Andrejevic, M., & Gates, K. (2014). Big Data Surveillance: Introduction. Surveillance & Society, 12(2), 185–196.
Asamoah, D. A., Sharda, R., Hassan Zadeh, A., & Kalgotra, P. (2017). Preparing a Data Scientist: A Pedagogic Experience in Designing a Big Data Analytics Course. Decision Sciences Journal of Innovative Education, 15(2), 161–190. https://doi.org/10.1111/dsji.12125
Bughin, J., Chui, M., & Manyika, J. (2010). Clouds, big data, and smart assets: Ten tech-enabled business trends to watch. McKinsey Quarterly, 56(1), 75–86.
Cohen, J., Dolan, B., Dunlap, M., Hellerstein, J. M., & Welton, C. (2009). MAD Skills: New Analysis Practices for Big Data. Proc. VLDB Endow., 2(2), 1481–1492. https://doi.org/10.14778/1687553.1687576
Daniel, B. (2015). Big Data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904–920. https://doi.org/10.1111/bjet.12230
Daries, J. P., Reich, J., Waldo, J., Young, E. M., Whittinghill, J., Ho, A. D., … Chuang, I. (2014). Privacy, Anonymity, and Big Data in the Social Sciences. Commun. ACM, 57(9), 56–63. https://doi.org/10.1145/2643132
De Mauro, A. D., Greco, M., & Grimaldi, M. (2016). A formal definition of Big Data based on its essential features. Library Review, 65(3), 122–135. https://doi.org/10.1108/LR-06-2015-0061
De Mauro, A., Greco, M., & Grimaldi, M. (2015). What is big data? A consensual definition and a review of key research topics. AIP Conference Proceedings, 1644(1), 97–104. https://doi.org/10.1063/1.4907823
Eaton, M. (2017). Seeing Library Data: A Prototype Data Visualization Application for Librarians. Publications and Research. Retrieved from http://academicworks.cuny.edu/kb_pubs/115
Emanuel, J. (2013). Usability testing in libraries: methods, limitations, and implications. OCLC Systems & Services: International Digital Library Perspectives, 29(4), 204–217. https://doi.org/10.1108/OCLC-02-2013-0009
Graham, M., & Shelton, T. (2013). Geography and the future of big data, big data and the future of geography. Dialogues in Human Geography, 3(3), 255–261. https://doi.org/10.1177/2043820613513121
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
Laney, D. (2001, February 6). 3D Data Management: Controlling Data Volume, Velocity, and Variety.
Miltenoff, P., & Hauptman, R. (2005). Ethical dilemmas in libraries: an international perspective. The Electronic Library, 23(6), 664–670. https://doi.org/10.1108/02640470510635746
Philip Chen, C. L., & Zhang, C.-Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275(Supplement C), 314–347. https://doi.org/10.1016/j.ins.2014.01.015
Provost, F., & Fawcett, T. (2013). Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data, 1(1), 51–59. https://doi.org/10.1089/big.2013.1508
Reyes, J. (2015). The skinny on big data in education: Learning analytics simplified. TechTrends: Linking Research & Practice to Improve Learning, 59(2), 75–80. https://doi.org/10.1007/s11528-015-0842-1
Schroeder, R. (2014). Big Data and the brave new world of social media research. Big Data & Society, 1(2), 2053951714563194. https://doi.org/10.1177/2053951714563194
Sugimoto, C. R., Ding, Y., & Thelwall, M. (2012). Library and information science in the big data era: Funding, projects, and future [a panel proposal]. Proceedings of the American Society for Information Science and Technology, 49(1), 1–3. https://doi.org/10.1002/meet.14504901187
Tene, O., & Polonetsky, J. (2012). Big Data for All: Privacy and User Control in the Age of Analytics. Northwestern Journal of Technology and Intellectual Property, 11, [xxvii]-274.
van Dijck, J. (2014). Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society; Newcastle upon Tyne, 12(2), 197–208.
Waller, M. A., & Fawcett, S. E. (2013). Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management. Journal of Business Logistics, 34(2), 77–84. https://doi.org/10.1111/jbl.12010
West, D. M. (2012). Big data for education: Data mining, data analytics, and web dashboards. Governance Studies at Brookings, 4, 1–0.
Willis, J. (2013). Ethics, Big Data, and Analytics: A Model for Application. Educause Review Online. Retrieved from https://docs.lib.purdue.edu/idcpubs/1
Wixom, B., Ariyachandra, T., Douglas, D. E., Goul, M., Gupta, B., Iyer, L. S., … Turetken, O. (2014). The current state of business intelligence in academia: The arrival of big data. CAIS, 34, 1.
Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97–107. https://doi.org/10.1109/TKDE.2013.109
Wu, Z., Wu, J., Khabsa, M., Williams, K., Chen, H. H., Huang, W., … Giles, C. L. (2014). Towards building a scholarly big data platform: Challenges, lessons and opportunities. In IEEE/ACM Joint Conference on Digital Libraries (pp. 117–126). https://doi.org/10.1109/JCDL.2014.6970157
The EDUCAUSE Learning Initiative has just launched its 2018 Key Issues in Teaching and Learning Survey, so vote today: http://www.tinyurl.com/ki2018.
Each year, the ELI surveys the teaching and learning community in order to discover the key issues and themes in teaching and learning. These top issues provide the thematic foundation or basis for all of our conversations, courses, and publications for the coming year. Longitudinally they also provide the way to track the evolving discourse in the teaching and learning space. More information about this annual survey can be found at https://www.educause.edu/eli/initiatives/key-issues-in-teaching-and-learning.
ACADEMIC TRANSFORMATION (Holistic models supporting student success, leadership competencies for academic transformation, partnerships and collaborations across campus, IT transformation, academic transformation that is broad, strategic, and institutional in scope)
ACCESSIBILITY AND UNIVERSAL DESIGN FOR LEARNING (Supporting and educating the academic community in effective practice; intersections with instructional delivery modes; compliance issues)
ADAPTIVE TEACHING AND LEARNING (Digital courseware; adaptive technology; implications for course design and the instructor’s role; adaptive approaches that are not technology-based; integration with LMS; use of data to improve learner outcomes)
COMPETENCY-BASED EDUCATION AND NEW METHODS FOR THE ASSESSMENT OF STUDENT LEARNING (Developing collaborative cultures of assessment that bring together faculty, instructional designers, accreditation coordinators, and technical support personnel, real world experience credit)
DIGITAL AND INFORMATION LITERACIES (Student and faculty literacies; research skills; data discovery, management, and analysis skills; information visualization skills; partnerships for literacy programs; evaluation of student digital competencies; information evaluation)
EVALUATING TECHNOLOGY-BASED INSTRUCTIONAL INNOVATIONS (Tools and methods to gather data;data analysis techniques; qualitative vs. quantitative data; evaluation project design; using findings to change curricular practice; scholarship of teaching and learning; articulating results to stakeholders; just-in-time evaluation of innovations). here is my bibliographical overview on Big Data (scroll down to “Research literature”: https://blog.stcloudstate.edu/ims/2017/11/07/irdl-proposal/ )
EVOLUTION OF THE TEACHING AND LEARNING SUPPORT PROFESSION (Professional skills for T&L support; increasing emphasis on instructional design; delineating the skills, knowledge, business acumen, and political savvy for success; role of inter-institutional communities of practices and consortia; career-oriented professional development planning)
FACULTY DEVELOPMENT (Incentivizing faculty innovation; new roles for faculty and those who support them; evidence of impact on student learning/engagement of faculty development programs; faculty development intersections with learning analytics; engagement with student success)
GAMIFICATION OF LEARNING (Gamification designs for course activities; adaptive approaches to gamification; alternate reality games; simulations; technological implementation options for faculty)
INSTRUCTIONAL DESIGN (Skills and competencies for designers; integration of technology into the profession; role of data in design; evolution of the design profession (here previous blog postings on this issue: https://blog.stcloudstate.edu/ims/2017/10/04/instructional-design-3/); effective leadership and collaboration with faculty)
INTEGRATED PLANNING AND ADVISING FOR STUDENT SUCCESS (Change management and campus leadership; collaboration across units; integration of technology systems and data; dashboard design; data visualization (here previous blog postings on this issue: https://blog.stcloudstate.edu/ims?s=data+visualization); counseling and coaching advising transformation; student success analytics)
LEARNING ANALYTICS (Leveraging open data standards; privacy and ethics; both faculty and student facing reports; implementing; learning analytics to transform other services; course design implications)
LEARNING SPACE DESIGNS (Makerspaces; funding; faculty development; learning designs across disciplines; supporting integrated campus planning; ROI; accessibility/UDL; rating of classroom designs)
MICRO-CREDENTIALING AND DIGITAL BADGING (Design of badging hierarchies; stackable credentials; certificates; role of open standards; ways to publish digital badges; approaches to meta-data; implications for the transcript; Personalized learning transcripts and blockchain technology (here previous blog postings on this issue: https://blog.stcloudstate.edu/ims?s=blockchain)
MOBILE LEARNING (Curricular use of mobile devices (here previous blog postings on this issue:
MULTI-DIMENSIONAL TECHNOLOGIES (Virtual, augmented, mixed, and immersive reality; video walls; integration with learning spaces; scalability, affordability, and accessibility; use of mobile devices; multi-dimensional printing and artifact creation)
NEXT-GENERATION DIGITAL LEARNING ENVIRONMENTS AND LMS SERVICES (Open standards; learning environments architectures (here previous blog postings on this issue: https://blog.stcloudstate.edu/ims/2017/03/28/digital-learning/; social learning environments; customization and personalization; OER integration; intersections with learning modalities such as adaptive, online, etc.; LMS evaluation, integration and support)
ONLINE AND BLENDED TEACHING AND LEARNING (Flipped course models; leveraging MOOCs in online learning; course development models; intersections with analytics; humanization of online courses; student engagement)
OPEN EDUCATION (Resources, textbooks, content; quality and editorial issues; faculty development; intersections with student success/access; analytics; licensing; affordability; business models; accessibility and sustainability)
PRIVACY AND SECURITY (Formulation of policies on privacy and data protection; increased sharing of data via open standards for internal and external purposes; increased use of cloud-based and third party options; education of faculty, students, and administrators)
WORKING WITH EMERGING LEARNING TECHNOLOGY (Scalability and diffusion; effective piloting practices; investments; faculty development; funding; evaluation methods and rubrics; interoperability; data-driven decision-making)
An introduction to digital badges and a brief history
Simply put, a digital badge is an indicator of accomplishment or skill that can be displayed, accessed, and verified online. These badges can be earned in a wide variety of environments, an increasing number of which are online.
The anatomy of digital badges
In addition to the image-based design we think of as a digital badge, badges have meta-data to communicate details of the badge to anyone wishing to verify it, or learn more about the context of the achievement it signifies.
The many functions of digital badges
Just like their real-world counterparts, digital badges serve a wide variety of purposes depending on the issuing body and the individual. For the most part, badges’ functions can be bucketed into one of five categories.
Badges are issued by individual organizations who set criteria for what constitutes earning a badge. They’re most often issued through an online credential or badging platform.
Criticism of digital badges
There are various arguments to be made against the implementation of digital badges, including the common issuance of seemingly “meaningless” badges.
The future of digital badges
With the rise of online education and the increasing availability of high quality massive open online courses, there will be an increasing need for verifiable digital badges and digital credentials.
10. The Virtualized Library: A Librarian’s Introduction to Docker and Virtual Machines
This session will introduce two major types of virtualization, virtual machines using tools like VirtualBox and Vagrant, and containers using Docker. The relative strengths and drawbacks of the two approaches will be discussed along with plenty of hands-on time. Though geared towards integrating these tools into a development workflow, the workshop should be useful for anyone interested in creating stable and reproducible computing environments, and examples will focus on library-specific tools like Archivematica and EZPaarse. With virtualization taking a lot of the pain out of installing and distributing software, alleviating many cross-platform issues, and becoming increasingly common in library and industry practices, now is a great time to get your feet wet.
(One three-hour session)
11. Digital Empathy: Creating Safe Spaces Online
User research is often focused on measures of the usability of online spaces. We look at search traffic, run card sorting and usability testing activities, and track how users navigate our spaces. Those results inform design decisions through the lens of information architecture. This is important, but doesn’t encompass everything a user needs in a space.
This workshop will focus on the other component of user experience design and user research: how to create spaces where users feel safe. Users bring their anxieties and stressors with them to our online spaces, but informed design choices can help to ameliorate that stress. This will ultimately lead to a more positive interaction between your institution and your users.
The presenters will discuss the theory behind empathetic design, delve deeply into using ethnographic research methods – including an opportunity for attendees to practice those ethnographic skills with student participants – and finish with the practical application of these results to ongoing and future projects.
(One three-hour session)
14. ARIA Basics: Making Your Web Content Sing Accessibility
https://dequeuniversity.com/assets/html/jquery-summit/html5/slides/landmarks.html
Are you a web developer or create web content? Do you add dynamic elements to your pages? If so, you should be concerned with making those dynamic elements accessible and usable to as many as possible. One of the most powerful tools currently available for making web pages accessible is ARIA, the Accessible Rich Internet Applications specification. This workshop will teach you the basics for leveraging the full power of ARIA to make great accessible web pages. Through several hands-on exercises, participants will come to understand the purpose and power of ARIA and how to apply it for a variety of different dynamic web elements. Topics will include semantic HTML, ARIA landmarks and roles, expanding/collapsing content, and modal dialog. Participants will also be taught some basic use of the screen reader NVDA for use in accessibility testing. Finally, the lessons will also emphasize learning how to keep on learning as HTML, JavaScript, and ARIA continue to evolve and expand.
Participants will need a basic background in HTML, CSS, and some JavaScript.
(One three-hour session)
18. Learning and Teaching Tech
Tech workshops pose two unique problems: finding skilled instructors for that content, and instructing that content well. Library hosted workshops are often a primary educational resource for solo learners, and many librarians utilize these workshops as a primary outreach platform. Tackling these two issues together often makes the most sense for our limited resources. Whether a programming language or software tool, learning tech to teach tech can be one of the best motivations for learning that tech skill or tool, but equally important is to learn how to teach and present tech well.
This hands-on workshop will guide participants through developing their own learning plan, reviewing essential pedagogy for teaching tech, and crafting a workshop of their choice. Each participant will leave with an actionable learning schedule, a prioritized list of resources to investigate, and an outline of a workshop they would like to teach.
(Two three-hour sessions)
23. Introduction to Omeka S
Omeka S represents a complete rewrite of Omeka Classic (aka the Omeka 2.x series), adhering to our fundamental principles of encouraging use of metadata standards, easy web publishing, and sharing cultural history. New objectives in Omeka S include multisite functionality and increased interaction with other systems. This workshop will compare and contrast Omeka S with Omeka Classic to highlight our emphasis on 1) modern metadata standards, 2) interoperability with other systems including Linked Open Data, 3) use of modern web standards, and 4) web publishing to meet the goals medium- to large-sized institutions.
In this workshop we will walk through Omeka S Item creation, with emphasis on LoD principles. We will also look at the features of Omeka S that ease metadata input and facilitate project-defined usage and workflows. In accordance with our commitment to interoperability, we will describe how the API for Omeka S can be deployed for data exchange and sharing between many systems. We will also describe how Omeka S promotes multiple site creation from one installation, in the interest of easy publishing with many objects in many contexts, and simplifying the work of IT departments.
(One three-hour session)
24. Getting started with static website generators
Have you been curious about static website generators? Have you been wondering who Jekyll and Hugo are? Then this workshop is for you
But this article isn’t about setting up a domain name and hosting for your website. It’s for the step after that, the actual making of that site. The typical choice for a lot of people would be to use something like WordPress. It’s a one-click install on most hosting providers, and there’s a gigantic market of plugins and themes available to choose from, depending on the type of site you’re trying to build. But not only is WordPress a bit overkill for most websites, it also gives you a dynamically generated site with a lot of moving parts. If you don’t keep all of those pieces up to date, they can pose a significant security risk and your site could get hijacked.
The alternative would be to have a static website, with nothing dynamically generated on the server side. Just good old HTML and CSS (and perhaps a bit of Javascript for flair). The downside to that option has been that you’ve been relegated to coding the whole thing by hand yourself. It’s doable, but you just want a place to share your work. You shouldn’t have to know all the idiosyncrasies of low-level web design (and the monumental headache of cross-browser compatibility) to do that.
Static website generators are tools used to build a website made up only of HTML, CSS, and JavaScript. Static websites, unlike dynamic sites built with tools like Drupal or WordPress, do not use databases or server-side scripting languages. Static websites have a number of benefits over dynamic sites, including reduced security vulnerabilities, simpler long-term maintenance, and easier preservation.
In this hands-on workshop, we’ll start by exploring static website generators, their components, some of the different options available, and their benefits and disadvantages. Then, we’ll work on making our own sites, and for those that would like to, get them online with GitHub pages. Familiarity with HTML, git, and command line basics will be helpful but are not required.
(One three-hour session)
26. Using Digital Media for Research and Instruction
To use digital media effectively in both research and instruction, you need to go beyond just the playback of media files. You need to be able to stream the media, divide that stream into different segments, provide descriptive analysis of each segment, order, re-order and compare different segments from the same or different streams and create web sites that can show the result of your analysis. In this workshop, we will use Omeka and several plugins for working with digital media, to show the potential of video streaming, segmentation and descriptive analysis for research and instruction.
(One three-hour session)
28. Spark in the Dark 101 https://zeppelin.apache.org/
This is an introductory session on Apache Spark, a framework for large-scale data processing (https://spark.apache.org/). We will introduce high level concepts around Spark, including how Spark execution works and it’s relationship to the other technologies for working with Big Data. Following this introduction to the theory and background, we will walk workshop participants through hands-on usage of spark-shell, Zeppelin notebooks, and Spark SQL for processing library data. The workshop will wrap up with use cases and demos for leveraging Spark within cultural heritage institutions and information organizations, connecting the building blocks learned to current projects in the real world.
(One three-hour session)
29. Introduction to Spotlight https://github.com/projectblacklight/spotlight http://www.spotlighttechnology.com/4-OpenSource.htm
Spotlight is an open source application that extends the digital library ecosystem by providing a means for institutions to reuse digital content in easy-to-produce, attractive, and scholarly-oriented websites. Librarians, curators, and other content experts can build Spotlight exhibits to showcase digital collections using a self-service workflow for selection, arrangement, curation, and presentation.
This workshop will introduce the main features of Spotlight and present examples of Spotlight-built exhibits from the community of adopters. We’ll also describe the technical requirements for adopting Spotlight and highlight the potential to customize and extend Spotlight’s capabilities for their own needs while contributing to its growth as an open source project.
(One three-hour session)
31. Getting Started Visualizing your IoT Data in Tableau https://www.tableau.com/
The Internet of Things is a rising trend in library research. IoT sensors can be used for space assessment, service design, and environmental monitoring. IoT tools create lots of data that can be overwhelming and hard to interpret. Tableau Public (https://public.tableau.com/en-us/s/) is a data visualization tool that allows you to explore this information quickly and intuitively to find new insights.
This full-day workshop will teach you the basics of building your own own IoT sensor using a Raspberry Pi (https://www.raspberrypi.org/) in order to gather, manipulate, and visualize your data.
All are welcome, but some familiarity with Python is recommended.
(Two three-hour sessions)
32. Enabling Social Media Research and Archiving
Social media data represents a tremendous opportunity for memory institutions of all kinds, be they large academic research libraries, or small community archives. Researchers from a broad swath of disciplines have a great deal of interest in working with social media content, but they often lack access to datasets or the technical skills needed to create them. Further, it is clear that social media is already a crucial part of the historical record in areas ranging from events your local community to national elections. But attempts to build archives of social media data are largely nascent. This workshop will be both an introduction to collecting data from the APIs of social media platforms, as well as a discussion of the roles of libraries and archives in that collecting.
Assuming no prior experience, the workshop will begin with an explanation of how APIs operate. We will then focus specifically on the Twitter API, as Twitter is of significant interest to researchers and hosts an important segment of discourse. Through a combination of hands-on and demos, we will gain experience with a number of tools that support collecting social media data (e.g., Twarc, Social Feed Manager, DocNow, Twurl, and TAGS), as well as tools that enable sharing social media datasets (e.g., Hydrator, TweetSets, and the Tweet ID Catalog).
The workshop will then turn to a discussion of how to build a successful program enabling social media collecting at your institution. This might cover a variety of topics including outreach to campus researchers, collection development strategies, the relationship between social media archiving and web archiving, and how to get involved with the social media archiving community. This discussion will be framed by a focus on ethical considerations of social media data, including privacy and responsible data sharing.
Time permitting, we will provide a sampling of some approaches to social media data analysis, including Twarc Utils and Jupyter Notebooks.
According to the email below, library faculty are asked to provide their feedback regarding the qualifications for a possible faculty line at the library.
In the fall of 2013 during a faculty meeting attended by the back than library dean and during a discussion of an article provided by the dean, it was established that leading academic libraries in this country are seeking to break the mold of “library degree” and seek fresh ideas for the reinvention of the academic library by hiring faculty with more diverse (degree-wise) background.
Is this still the case at the SCSU library? The “democratic” search for the answer of this question does not yield productive results, considering that the majority of the library faculty are “reference” and they “democratically” overturn votes, who see this library to be put on 21st century standards and rather seek more “reference” bodies for duties, which were recognized even by the same reference librarians as obsolete.
It seems that the majority of the SCSU library are “purists” in the sense of seeking professionals with broader background (other than library, even “reference” skills).
In addition, most of the current SCSU librarians are opposed to a second degree, as in acquiring more qualification, versus seeking just another diploma. There is a certain attitude of stagnation / intellectual incest, where new ideas are not generated and old ideas are prepped in “new attire” to look as innovative and/or 21st
Last but not least, a consistent complain about workforce shortages (the attrition politics of the university’s reorganization contribute to the power of such complain) fuels the requests for reference librarians and, instead of looking for new ideas, new approaches and new work responsibilities, the library reorganization conversation deteriorates into squabbles for positions among different department.
Most importantly, the narrow sightedness of being stuck in traditional work description impairs most of the librarians to see potential allies and disruptors. E.g., the insistence on the supremacy of “information literacy” leads SCSU librarians to the erroneous conclusion of the exceptionality of information literacy and the disregard of multi[meta] literacies, thus depriving the entire campus of necessary 21st century skills such as visual literacy, media literacy, technology literacy, etc.
Simultaneously, as mentioned above about potential allies and disruptors, the SCSU librarians insist on their “domain” and if they are not capable of leading meta-literacies instructions, they would also not allow and/or support others to do so.
Considering the observations above, the following qualifications must be considered:
According to the information in this blog post: https://blog.stcloudstate.edu/ims/2016/06/14/technology-requirements-samples/
for the past year and ½, academic libraries are hiring specialists with the following qualifications and for the following positions (bolded and / or in red). Here are some highlights: Positions
digital humanities
Librarian and Instructional Technology Liaison
library Specialist: Data Visualization & Collections Analytics
Qualifications
Advanced degree required, preferably in education, educational technology, instructional design, or MLS with an emphasis in instruction and assessment.
Programming skills – Demonstrated experience with one or more metadata and scripting languages (e.g.Dublin Core, XSLT, Java, JavaScript, Python, or PHP)
Data visualization skills
multi [ meta] literacy skills
Data curation, helping students working with data
Experience with website creation and design in a CMS environment and accessibility and compliance issues
Demonstrated a high degree of facility with technologies and systems germane to the 21st century library, and be well versed in the issues surrounding scholarly communications and compliance issues (e.g. author identifiers, data sharing software, repositories, among others)
Bilingual
Provides and develops awareness and knowledge related to digital scholarship and research lifecycle for librarians and staff.
Experience developing for, and supporting, common open-source library applications such as Omeka, ArchiveSpace, Dspace,
Responsibilities Establishing best practices for digital humanities labs, networks, and services
Assessing, evaluating, and peer reviewing DH projects and librarians
Actively promote TIGER or GRIC related activities through social networks and other platforms as needed.
Coordinates the transmission of online workshops through Google HangoutsScript metadata transformations and digital object processing using BASH, Python, and XSLT
liaison consults with faculty and students in a wide range of disciplines on best practices for teaching and using data/statistical software tools such as R, SPSS, Stata, and MatLab.
In response to the form attached to the Friday, September 29, email regarding St. Cloud State University Library Position Request Form:
Title
Digital Initiatives Librarian
Responsibilities:
TBD, but generally:
– works with faculty across campus on promoting digital projects and other 21st century projects. Works with the English Department faculty on positioning the SCSU library as an equal participants in the digital humanities initiatives on campus
Works with the Visualization lab to establish the library as the leading unit on campus in interpretation of big data
Works with academic technology services on promoting library faculty as the leading force in the pedagogical use of academic technologies.
Quantitative data justification
this is a mute requirement for an innovative and useful library position. It can apply for a traditional request, such as another “reference” librarian. There cannot be a quantitative data justification for an innovative position, as explained to Keith Ewing in 2015. In order to accumulate such data, the position must be functioning at least for six months.
Qualitative justification: Please provide qualitative explanation that supports need for this position.
Numerous 21st century academic tendencies right now are scattered across campus and are a subject of political/power battles rather than a venue for campus collaboration and cooperation. Such position can seek the establishment of the library as the natural hub for “sandbox” activities across campus. It can seek a redirection of using digital initiatives on this campus for political gains by administrators and move the generation and accomplishment of such initiatives to the rightful owner and primary stakeholders: faculty and students.
Currently, there are no additional facilities and resources required. Existing facilities and resources, such as the visualization lab, open source and free application can be used to generate the momentum of faculty working together toward a common goal, such as, e.g. digital humanities.
blockchain is a database or digital ledger. The data in the ledger is arranged in batches known as blocks, with each block storing data about a specific transaction. The blocks are linked together using cryptographic validation to form an unbroken and unbreakable chain–hence the name blockchain. As it relates to bitcoin, the blocks are monetary units, and the chain includes information about all past transactions of that monetary unit.
Importantly, the database (i.e., the series of blocks) is duplicated thousands of times across a network of computers, meaning that it has no one central repository. This not only means that the records are truly public, but also that there is no centralized version of the data for a hacker to corrupt. In order to make changes to the ledger, consensus between all members of the group must be obtained, further adding to the system’s security.
1. Blockchain for the Future of Credentialing
With today’s technologies, graduates and prospective employers must go through a tedious process to obtain student transcripts or diplomas, and this complexity is compounded when these credentials are spread across multiple institutions. Not only that, but these transcripts can take days or weeks to produce and send, and usually require a small fee be paid to the institution.LinkedLinek
This could be a key enabler to facilitate student ownership of this data and would allow them to instantly produce secure and comprehensive credentials to any institute or employer requesting them, including information about a student’s performance on standardized tests, degree requirements, extracurricular activities, and other learning activities.
Blockchain could play a major role in Competency-Based Education (CBE) programs and micro-credentialing, which are becoming ever more popular across universities and internal business training programs.
various companies are currently working on such a system of record. One of the most well-known is called “BlockCert,” which is an open standard created by MIT Media Lab and which the institute hopes will help drive the adoption of blockchain credentialing.
imagine the role that LinkedIn or a similar platform could play in the distribution of such content. Beyond verification of university records, LinkedIn could become a platform for sharing verified work history and resumes as well, making the job application process far simpler
2. Blockchain’s Financial Implications and Student debt
how could blockchain influence student finances? For starters, financial aid and grants could be tied to student success. Instead of students and universities having to send over regular progress reports on a recipient’s performance, automatic updates to a student’s digital record would ensure that benchmarks were being met–and open up new opportunities for institutions looking to offer merit-based grants.
Electronic tuition payments and money transfers could also simplify the tuition process. This is an especially appealing option for international students, as bitcoin’s interchangeable nature and lack of special fees for international transfers makes it a simpler and more cost-effective payment method.
Consider the information they handle every day. School districts in America today are complex, sophisticated businesses, not only managing multiple applications across multiple platforms, but also managing people and equipment in the real world, like bus fleets, library systems, and cafeterias.
you will find admins working with an average of 30 onsite and online platforms. That’s 30 systems to feed with data and update. The kicker is that those systems might not be on speaking terms with each other.
Interoperability is a multi-headed issue for any IT professional, but in the K-12 education world it is especially complex. These unique complexities can be distilled down to eight truths, and may explain why vendors who have been very successful in other IT verticals never seem to meet expectations in K-12.
The Solution Cannot Be Point-to-Point
Data from many active sources is profoundly difficult to keep current, especially when considering the different protocols used for each particular point-to-point integration.
There Must Be Multiple Ways of Moving Data
A successful broker/dashboard must be able to accommodate all of these integration methods. The broker needs to support it as well as the industry’s existing standards, such as SIF and CSV.
The System Must Merge Disparate Feeds
Data comes into educational systems from a variety of feeds, including CSVs and file sharing. Handling all these feeds develops a vital function, coveted by IT professionals and system admins everywhere: a comprehensive representation of the data truth of your district.
Your Data Solution Must Be Bidirectional
Different systems don’t always talk to each other politely, and with some districts using as many as 30 applications, writing grades back to the SIS can get thorny.
We Need a Flexible Data Model
some of those free or low-cost integrations are profoundly rigid and can’t accommodate the data reality of school districts.
We Must Deal with “Dumb” End Points
In the world of district data, we are moving toward REST APIs and other unintelligent end points. There is no inherent logic in an API that tells the system how to move data. And as mentioned earlier, many legacy systems still depend on CSV’s for data.
Integration Belongs in the Cloud but Must Accommodate On-Premise Apps
know the cloud actually is an ideal setting for interoperability, especially since so many of our applications are cloud-based. It gives you maximum visibility, maximum diagnostic capability and manageability. You can manage from anywhere, anytime.
Be Multi-Tenant with Supervisory Capability
For areas where intermediate units or a Board of Cooperative Educational Standards (BOCES) provide IT services to districts, the system admins need a big picture approach. The integration platform must allow the IU or BOCES to troubleshoot, diagnose, manage, and support multiple districts in one dashboard, but only show district personnel data belonging to their organization. State education agencies also have this need.
There are several reputable companies that provide an iPaaS–in fact Gartner compared 20 of them in their 2017 Magic Quadrant for Enterprise Integration Platform as a Service. However, without a deep understanding of education data models, even these vendors may fall short, and may be expensive.
Are you seeing enrollments change? Which technologies hold the most promise? Will your campus become politically active? What collaborations might power up teaching and learning?
the big technological issues for the next year?
robotics? automation in education? big data / analytics?
organizational transformation. David Stone (Penn State) – centralization vs decentralization. technology is shifting everywhere, even the registrar. BA – where should be the IT department? CFO or Academic Department.
difference between undergrads and grad students and how to address. CETL join center for academic technologies.
faculty role, developing courses and materials. share these materials and make more usable. who should be maintaining these materials. life cycle, compensation for development materials. This is in essence the issues of the OER Open Education Resources initiative in MN
BA: OER and Open Access to Research has very similar models and issues. Open access scholarship both have a lot of impact on campus finances. Library and faculty budges.
Amanda Major is with Division of Digital Learning as part of Academic Affairs at UCF: Are there trends in competency-based learning, assessing quality course and programs, personalized adaptive learning, utilizing data analytics for retention and student success? BA: CBL continue to grow at state U’s and community colleges.
BA for group discussions: what are the technological changes happening this coming year, not only internally on campus, but global changes and how thy might be affecting us. Amazon Dash button, electric cars for U fleet, newer devices on campus
David Stone: students are price-sensitive. college and U can charge whatever they want and text books can raise prices.