Publisher / Organization: Athabasca University Press
Year founded: 2000
Description: The International Review of Research in Open and Distributed Learning disseminates original research, theory, and best practice in open and distributed learning worldwide.
Publisher / Organization: The University of Illinois at Chicago- University Library
Year founded: 1996
Description: First Monday is among the very first open access journals in the EdTech field. The journal’s subject matter encompasses the full range of Internet issues, including educational technologies, social media and web search. Contributors are urged via author guidelines to use simple explanations and less complex sentences and to be mindful that a large proportion of their readers are not part of academia and do not have English as a first language.
Academic Management: University of Catalonia (UOC)
Year founded: 2004
Description: This journal aims to: provide a vehicle for scholarly presentation and exchange of information between professionals, researchers and practitioners in the technology-enhanced education field; contribute to the advancement of scientific knowledge regarding the use of technology and computers in higher education; and inform readers about the latest developments in the application of information technologies (ITs) in higher education learning, training, research and management.
Description: Online Learning promotes the development and dissemination of new knowledge at the intersection of pedagogy, emerging technology, policy, and practice in online environments. The journal has been published for over 20 years as the Journal of Asynchronous Learning Networks (JALN) and recently merged with the Journal of Online Learning and Teaching (JOLT).
Publisher / Organization: International Forum of Educational Technology & Society
Year founded:1998
Description: Educational Technology & Society seeks academic articles on the issues affecting the developers of educational systems and educators who implement and manage these systems. Articles should discuss the perspectives of both communities – the programmers and the instructors. The journal is currently still accepting submissions for ongoing special issues, but will cease publication in the future as the editors feel that the field of EdTech is saturated with high quality publications.
Description: The Australasian Journal of Educational Technology aims to promote research and scholarship on the integration of technology in tertiary education, promote effective practice, and inform policy. The goal is to advance understanding of educational technology in post-school education settings, including higher and further education, lifelong learning, and training.
DESCRIPTION: The Internet and Higher Education is devoted to addressing contemporary issues and future developments related to online learning, teaching, and administration on the Internet in post-secondary settings. Articles should significantly address innovative deployments of Internet technology in instruction and report on research to demonstrate the effects of information technology on instruction in various contexts in higher education.
Publisher / Organization: British Educational Research Association (BERA)
YEAR FOUNDED: 1970
DESCRIPTION: The journal publishes theoretical perspectives, methodological developments and empirical research that demonstrate whether and how applications of instructional/educational technology systems, networks, tools and resources lead to improvements in formal and non-formal education at all levels, from early years through to higher, technical and vocational education, professional development and corporate training.
Description: Computers & Education aims to increase knowledge and understanding of ways in which digital technology can enhance education, through the publication of high quality research, which extends theory and practice.
Description: TechTrends targets professionals in the educational communication and technology field. It provides a vehicle that fosters the exchange of important and current information among professional practitioners. Among the topics addressed are the management of media and programs, the application of educational technology principles and techniques to instructional programs, and corporate and military training.
Description: Advances in technology and the growth of e-learning to provide educators and trainers with unique opportunities to enhance learning and teaching in corporate, government, healthcare, and higher education. IJEL serves as a forum to facilitate the international exchange of information on the current research, development, and practice of e-learning in these sectors.
Led by an Editorial Review Board of leaders in the field of e-Learning, the Journal is designed for the following audiences: researchers, developers, and practitioners in corporate, government, healthcare, and higher education. IJEL is a peer-reviewed journal.
Description: JCMST is a highly respected scholarly journal which offers an in-depth forum for the interchange of information in the fields of science, mathematics, and computer science. JCMST is the only periodical devoted specifically to using information technology in the teaching of mathematics and science.
Just as researchers build reputation over time that can be depicted (in part) through quantitative measures such as h-index and i10-index, journals are also compared based on the number of citations they receive..
Description: The Journal of Interactive Learning Research (JILR) publishes papers related to the underlying theory, design, implementation, effectiveness, and impact on education and training of the following interactive learning environments: authoring systems, cognitive tools for learning computer-assisted language learning computer-based assessment systems, computer-based training computer-mediated communications, computer-supported collaborative learning distributed learning environments, electronic performance support systems interactive learning environments, interactive multimedia systems interactive simulations and games, intelligent agents on the Internet intelligent tutoring systems, microworlds, virtual reality based learning systems.
Description: JEMH is designed to provide a multi-disciplinary forum to present and discuss research, development and applications of multimedia and hypermedia in education. It contributes to the advancement of the theory and practice of learning and teaching in environments that integrate images, sound, text, and data.
Publisher / Organization: Society for Information Technology and Teacher Education (SITE)
Year founded: 1997
Description: JTATE serves as a forum for the exchange of knowledge about the use of information technology in teacher education. Journal content covers preservice and inservice teacher education, graduate programs in areas such as curriculum and instruction, educational administration, staff development instructional technology, and educational computing.
Publisher / Organization: Association for the Advancement of Computing in Education (AACE)
YEAR FOUNDED: 2015
DESCRIPTION: The Journal of Online Learning Research (JOLR) is a peer-reviewed, international journal devoted to the theoretical, empirical, and pragmatic understanding of technologies and their impact on primary and secondary pedagogy and policy in primary and secondary (K-12) online and blended environments. JOLR is focused on publishing manuscripts that address online learning, catering particularly to the educators who research, practice, design, and/or administer in primary and secondary schooling in online settings. However, the journal also serves those educators who have chosen to blend online learning tools and strategies in their face-to-face classroom.
The most commonly used index to measure the relative importance of journals is the annual Journal Citation Reports (JCR). This report is published by Clarivate Analytics (previously Thomson Reuters).
SCImago Journal Rank (SJR indicator) measures the influence of journals based on the number of citations the articles in the journal receive and the importance or prestige of the journals where such citations come from. The SJR indicator is a free journal metric which uses an algorithm similar to PageRank and provides an open access alternative to the journal impact factor in the Web of Science Journal Citation Report. The portal draws from the information contained in the Scopus database (Elsevier B.V.).
Introduced by Google in 2004, Scholar is a freely accessible search engine that indexes the full text or metadata of scholarly publications across an array of publishing formats and disciplines.
Introduced by Elsevier in 2004, Scopus is an abstract and citation database that covers nearly 18,000 titles from more than 5,000 publishers. It offers journal metrics that go beyond just journals to include most serial titles, including supplements, special issues and conference proceedings. Scopus offers useful information such as the total number of citations, the total number of articles published, and the percent of articles cited.
Anne-Wil Harzing:
“Citations are not just a reflection of the impact that a particular piece of academic work has generated. Citations can be used to tell stories about academics, journals and fields of research, but they can also be used to distort stories”.
Harzing, A.-W. (2013). The publish or perish book: Your guide to effective and responsible citation analysis. http://harzing.com/popbook/index.htm
ResearchGate is a social networking site for scientists and researchers to share papers, ask and answer questions, and find collaborators. The community was founded in May 2008. Today it has over 14 million members.
Google Scholar allows users to search for digital or physical copies of articles, whether online or in libraries. It indexes “full-text journal articles, technical reports, preprints, theses, books, and other documents, including selected Web pages that are deemed to be ‘scholarly. It comprises an estimated 160 million documents.
Academia.edu is a social-networking platform for academics to share research papers. You can upload your own work, and follow the updates of your peers. Founded in 2008, the network currently has 59 million users, and adding 20 million documents.
The ORCHID (Open Researcher and Contributor ID) is a nonproprietary alphanumeric code to uniquely identify scientific and other academic authors and contributors. It provides a persistent identity for humans, similar to content-related entities on digital networks that utilize digital object identifiers (DOIs). The organization offers an open and independent registry intended to be the de facto standard for contributor identification in research and academic publishing.
The Scopus Author Identifier assigns a unique number to groups of documents written by the same author via an algorithm that matches authorship based on a certain criteria. If a document cannot be confidently matched with an author identifier, it is grouped separately. In this case, you may see more than one entry for the same author.
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more on metrics in this iMS blog
Definition:
The HyFlex model gives students the choice to attend class in person or via synchronous remote stream and to make that choice on a daily basis. In other words, unlike online and hybrid models which typically have a fixed course structure for the entire semester, the HyFlex model does not require students to make a choice at the beginning of term and then stick with it whether their choice works for them or not; rather students are able to make different choices each day depending on what works best for them on that day (hence the format is “flexible”) (Miller and Baham, 2018, to be published in the Proceedings of the 10th International Conference on Teaching Statistics).
Definition from Horizon Report, HIgher Ed edition, 2014. p. 10 integration of Online Hybrid and Collaborative Learning
Definition from U of Arizona (https://journals.uair.arizona.edu/index.php/itet/article/view/16464/16485)
Beatty (2010) defines HyFlex courses to be those that “enable a flexible participation policy for students whereby students may choose to attend face-to-face synchronous class sessions or complete course learning activities online without physically attending class”
Online courses
Definition
Goette, W. F., Delello, J. A., Schmitt, A. L., Sullivan, J. R., & Rangel, A. (2017). Comparing Delivery Approaches to Teaching Abnormal Psychology: Investigating Student Perceptions and Learning Outcomes. Psychology Learning and Teaching, 16(3), 336–352. https://doi.org/10.1177/1475725717716624
p.2.Online classes are a form of distance learning available completely over the Internet with no F2F interaction between an instructor and students (Helms, 2014).
https://www.oswego.edu/human-resources/section-6-instructional-policies-and-procedures
An online class is a class that is offered 100% through the Internet. Asynchronous courses require no time in a classroom. All assignments, exams, and communication are delivered using a learning management system (LMS). At Oswego, the campus is transitioning from ANGEL to Blackboard, which will be completed by the Fall 2015 semester. Fully online courses may also be synchronous. Synchronous online courses require student participation at a specified time using audio/visual software such as Blackboard Collaborate along with the LMS.
Web-enhanced courses
Web enhanced learning occurs in a traditional face-to-face (f2f) course when the instructor incorporates web resources into the design and delivery of the course to support student learning. The key difference between Web Enhanced Learning versus other forms of e-learning (online or hybrid courses) is that the internet is used to supplement and support the instruction occurring in the classroom rather than replace it. Web Enhanced Learning may include activities such as: accessing course materials, submitting assignments, participating in discussions, taking quizzes and exams, and/or accessing grades and feedback.”
Blended/Hybrid Learning
Definition
Goette, W. F., Delello, J. A., Schmitt, A. L., Sullivan, J. R., & Rangel, A. (2017). Comparing Delivery Approaches to Teaching Abnormal Psychology: Investigating Student Perceptions and Learning Outcomes. Psychology Learning and Teaching, 16(3), 336–352. https://doi.org/10.1177/1475725717716624
p.3.
Helms (2014) described blended education as incorporating both online and F2F character- istics into a single course. This definition captures an important confound to comparing course administration formats because otherwise traditional F2F courses may also incorp- orate aspects of online curriculum. Blended learning may thus encompass F2F classes in which any course content is available online (e.g., recorded lectures or PowerPoints) as well as more traditionally blended courses. Helms recommended the use of ‘‘blended’’ over ‘‘hybrid’’ because these courses combine different but complementary approaches rather than layer opposing methods and formats.
Blended learning can merge the relative strengths of F2F and online education within a flexible course delivery format. As such, this delivery form has a similar potential of online courses to reduce the cost of administration (Bowen et al., 2014) while addressing concerns of quality and achievement gaps that may come from online education. Advantages of blended courses include: convenience and efficiency for the student; promotion of active learning; more effective use of classroom space; and increased class time to spend on higher- level learning activities such as cooperative learning, working with case studies, and discuss- ing big picture concepts and ideas (Ahmed, 2010; Al-Qahtani & Higgins, 2013; Lewis & Harrison, 2012).
Although many definitions of hybrid and blended learning exist, there is a convergence upon three key points: (1) Web-based learning activities are introduced to complement face-to-face work; (2) “seat time” is reduced, though not eliminated altogether; (3) the Web-based and face-to-face components of the course are designed to interact pedagogically to take advantage of the best features of each.
The amount of in class time varies in hybrids from school to school. Some require more than 50% must be in class, others say more than 50% must be online. Others indicate that 20% – 80% must be in class (or online). There is consensus that generally the time is split 50-50, but it depends on the best pedagogy for what the instructor wants to achieve.
definition
“Backchannel” is a mode of communication created by audience members to connect with other observers both inside and outside of the presentation space, with or without the speaker’s awareness (Atkinson, 2009).
ARS (or CRS) are[audience] response systems are instructional technologies that allow instructors to rapidly collect and analyze student responses to questions posed during class” (Bruff, 2009, p. 1)
Distance learning courses are indicated in the schedule of classes on BU Brain with an Instructional Method of Online Asynchronous (OA), Online Synchronous (OS), Online Combined (OC), or Online Hybrid (OH). Online Asynchronous courses are those in which the instruction is recorded/stored and then accessed by the students at another time. Online Synchronous courses are those in which students are at locations remote from the instructor and viewing the instruction as it occurs. Online Combined courses are those in which there is a combination of asynchronous and synchronous instruction that occurs over the length of the course. Online Hybrid courses are those in which there is both in-person and online (asynchronous and/or synchronous) instruction that occurs over the length of the course.
Over the past 10 years, new learning management systems (LMSs) have sprung on the scene to rival the Blackboards and Moodles of old. On the EdSurge Product Index alone, 56 products self-identify and fall into the LMS category. And with certain established companies like Pearson pulling out of the LMS ranks, where do you start?
As University of Central Florida’s Associate Vice President of Distributed Learning, Tom Cavanagh, wrote in an article for EDUCAUSE, “every institute has a unique set of instructional and infrastructure circumstances to consider when deciding on an LMS,” but at the same time, “all institutions face certain common requirements”—whether a small charter school, a private university or a large public school district.
The LMS Checklist
#1: Is the platform straightforward and user-friendly?
#2: Who do we want to have access to this platform, and can we adjust what they can see?
#3: Can the instructor and student(s) talk to and communicate with each other easily?
“Students and faculty live a significant portion of their daily lives online in social media spaces,” writes University of Central Florida’s Tom Cavanagh in his article on the LMS selection process. “Are your students and faculty interested in these sorts of interplatform connections?”
#5: Does this platform plug in with all of the other platforms we have?
“Given the pace of change and the plethora of options with educational technology, it’s very difficult for any LMS vendor to keep up with stand-alone tools that will always outperform built-in tools,” explains Michael Truong, executive director of innovative teaching and technology at Azusa Pacific University. According to Truong, “no LMS will be able to compete directly with tools like Piazza (discussion forum), Socrative (quizzing), EdPuzzle (video annotation), etc.”
As a result, Truong says, “The best way to ‘prepare’ for future technological changes is to go with an LMS that plays well with external tools.”
Effective communication is one critical characteristics of effective and successful school principal. Research on effective schools and instructional leadership emphasizes the impact of principal leadership on creating safe and secure learning environment and positive nurturing school climate (Halawah, 2005, p. 334)
Halawah, I. (2005). The Relationship between Effective Communication of High School Principal and School Climate. Education, 126(2), 334-345.
Selection of school principals in Hong Kong. The findings confirm a four-factor set of expectations sought from applicants; these are Generic Managerial Skills; Communication and Presentation Skills; Knowledge and Experience; and Religious Value Orientation.
Kwan, P. (2012). Assessing school principal candidates: perspectives of the hiring superintendents. International Journal Of Leadership In Education, 15(3), 331-349. doi:10.1080/13603124.2011.617838
Yee, D. L. (2000). Images of school principals’ information and communications technology leadership. Journal of Information Technology for Teacher Education, 9(3), 287–302. https://doi.org/10.1080/14759390000200097
Catano, N., & Stronge, J. H. (2007). What do we expect of school principals? Congruence between principal evaluation and performance standards. International Journal of Leadership in Education, 10(4), 379–399. https://doi.org/10.1080/13603120701381782
Communication can consist of two large areas:
broadcasting information: PR, promotions, notifications etc.
two-way communication: collecting feedback, “office hours” type of communication, backchanneling, etc.
Further communication initiated by/from principals can have different audiences
Reyes, P., & Hoyle, D. (1992). Teachers’ Satisfaction With Principals’ Communication. The Journal of Educational Research, 85(3), 163–168. https://doi.org/10.1080/00220671.1992.9944433
parents: involvement, feeling of empowerment, support, volunteering
students
board members
community
Epstein, J. L. (1995). School/family/community partnerships – ProQuest. Phi Delta Kappan, 76(9), 701.
Others
Communication and Visualization
The ever-growing necessity to be able to communicate data to different audiences in digestible format.
considering the information discussed in class, split in groups of 4 and develop your institution strategy for effective and modern communication across and out of your school.
Outline:
The Gamification of the educations process is not a new concept. The advent of educational technologies, however, makes the idea timely and pertinent. In short 60 min, we will introduce the concept of gamification of the educational process and discuss real-live examples.
Learning Outcomes:
at the end of the session, participants will have an idea about gaming and gamification in education and will be able to discriminate between those two powerful concepts in education
at the end of this session, participants will be able search and select VIdeo 360 movies for their class lessons
at the end of the session, participants will be able to understand the difference between VR, AR and MR.
if you are interested in setting up a makerspace and/or similar gaming space at your school, please contact me after this workshop for more information.
How would you define gamification of the educational process?
Gaming and Gamification in academic and library settings (paper) Short URL: http://scsu.mn/1F008Re
Gamification takes game elements (such as points, badges, leaderboards, competition, achievements) and applies them to a non-game setting. It has the potential to turn routine, mundane tasks into refreshing, motivating experiences (What is GBL (Game-Based Learning)?, n.d.).
Gamification is defined as the process of applying game mechanics and game thinking to the real world to solve problems and engage users (Phetteplace & Felker, 2014, p. 19; Becker, 2013, p. 199; Kapp, 2012). Gamification requires three sets of principles: 1. Empowered Learners, 2. Problem Solving, 3. Understanding (Gee, 2005).
Apply gamification tactics to existing learning task
split in groups and develop a plan to gamify existing learning task
from the web page above, choose a movie or click on this link: https://youtu.be/nOHM8gnin8Y (to watch a black hole in video 360) Open the link on your phone and insert the phone in Google Cardboard. Watch the video using Google Cardboard.
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
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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
Ms. Joanne Lipman
October 20, 2017
Editor-in-Chief of USA Today
7950 Jones Branch Drive
McLean, VA 22108
Dear Ms. Lipman,
In our roles as the Board of Directors of the Association for Library and Information Science Education (ALISE), we are writing to express our profound disappointment with the USA Today career advice feature on October 13, 2017 entitled “Careers: 8 jobs that won’t exist in 2030,” which declared that “librarian” is the number one career among the eight jobs that inaccurate statement on two fronts: first, that the profession is declining, and second, that this alleged will disappear in 2030. This is a false and decline is a result of libraries as warehouses of printed books.
The author of this article may not realize that a professional librarian position in the U.S. and many other countries requires a Master’s degree. According to a recent article in Library Journal, 86% of recent graduates from American Library Association (ALA) accredited schools have found jobs. Another recent report (released on September 28, 2017) by Pearson, Nesta, and Oxford University predicts growth in the information professions, including librarians, curators, and archivists. They are among the top ten jobs likely to experience increased demand in 2030. The report is summarized by Library Journal in its article entitled “The Job Outlook: In 2030, Librarians Will Be in Demand.” Furthermore, your own job posting section for librarian positions does not show the decline of our profession. A close reading of the job titles should have indicated to the author that librarians do more than simply check out books.
This article demonstrates a lack of understanding of librarians’ work as information professionals. My note: but so do lack understanding a lot of librarians, paraprofessionals and administrators in libraries. They are the one, who leave the impressions reflected in the article of US Today. Information professionals IS the keyword and, as during the hype around year 2000 with Barnes & Nobles, a great number of people working in libraries continue to behave as it is the Middle Ages and care of paper-based materials the one and only responsibility a “librarian” may have. The lack of understanding regarding the wide scope of “information professionals” is profound.
Libraries provide access to print and special collections of media, and subscription-based or free electronic resources. All of these must be curated, cataloged, or organized by professional librarians to make them accessible to their users. My note: beating your own drum is good, but when failing to recognize the existence of folksonomy and its impact, do not get upset when US Today reflects the impact
College and university librarians carry out research consultations and instruct student and faculty in finding, evaluating, and using information. My note: when faculty let them do it. And administration recognizes it. It is a shaky position, which does not exclude the 2030 scenario.
Public librarians connect patrons to community resources, lead programming for children and adults, and engage in community outreach and advocacy. Special librarians work for corporations, federal and state institutions, focusing on gathering competitive intelligence and making sure their organizations have access to the information they need to make sound business or strategic decisions.
The article also inaccurately presents libraries as dedicated solely to books:
More and more people are clearing out those paperbacks and downloading e-books on their Tablets and Kindles instead. The same goes for borrowing — as books fall out of favor, libraries are not as popular as they once were. That means you’ll have a tough time finding a job if you decide to become a librarian. Many schools and universities are already moving their libraries off the shelves and onto the Internet.
In addition to providing access to books, journals, newspapers, and other media, both electronically and in print, libraries provide access to technology, from computers, laptops, and iPads to 3D printers, My note: are we? are we doing this at our library? Are the reference librarians allowing such blasphemous thoughts penetrate this library? And if they do, do they allow other professionals to collaborate with them, or “keep it for themselves?”
multimedia software, and recording studios. My note: whaaat?
Many libraries have expanded their non-print collections and are circulating a wide variety of objects including tools, musical instruments, toys, wifi hotspots, and artwork. Libraries are highly valued as community centers and safe spaces that allow people to connect with information and with each other. Research shows that libraries are one of the most trusted and valued public institutions in the country.
The article further argues that librarians and libraries are not needed because printed books are falling out of favor. However, there is considerable counter-evidence that printed books are still in demand, including the articles cited below.
Cain, S. (2017, March 14). Ebook sales continue to fall as younger generations drive appetite for print. The Guardian. Retrieved from:
Milliot, J. (2017, January 20). The Bad News About E-books: Nielsen reports units fell 16% in 2016 compared to 2015. Publishers Weekly. Retrieved from:
We respectfully request an open response from you or from the author of the article. Sincerely,
In 2015, former library dean purchased two large touch-screen monitors (I believe paid $3000 each). Shortly before that, I had offered to the campus fitting applications for touch screens (being that large screens or mobiles):
With the large touch screens, I proposed to have one of the large screens, positioned outside in the Miller Center lobby and used as a dummy terminal (50” + screens run around $700) to mount educational material (e.g. Guenter Grass’s celebration of his work: https://blog.stcloudstate.edu/ims/2015/04/15/gunter-grass-1927-2015/ ) and have students explore by actively engaging, rather than just passively absorbing information. The bus-awaiting students are excellent potential users and they visibly are NOT engaged by by the currently broadcasted information on these screens, but can be potentially engaged if such information is restructured in interactive content.
The initial library administration approval was stalled by a concern with students “opening porno sites” while the library is closed which, indeed, would have been a problem.
My 2015 inquiry with the IT technicians about freezing a browser and a specific tab, which could prevent such issues, but it did not go far (pls see solution below). Failing to secure relatively frigid environment on the touch screen, the project was quietly left to rot.
I am renewing my proposal to consider the rather expensive touch screen monitors, which have been not utilized to their potential, and test my idea to engage students in a meaningful knowledge-building by using these applications to either create content or engage with content created by others.
Further, I am proposing that I investigate with campus faculty the possibility to bring the endeavor a step further by having a regularly-meeting group to develop engaging content using these and similar apps; for their own classes or any other [campus-related] activities. The incentive can be some reward, after users and creators “vote” the best (semester? Academic year?) project. The less conspicuous benefit will be the exposure of faculty to modern technology; some of the faculty are still abiding by lecturing style, other faculty, who seek interactivity are engulfed in the “smart board” fiction. Engaging the faculty in the touch screen creation of teaching materials will allow them to expand the practice to their and their students’ mobile devices. The benefit for the library will be the “hub” of activities, where faculty can learn from each other experience[s] in the library, rather than in their own departments/school only. The reward will be an incentive from the upper administration (document to attach in PDR?). I will need both your involvement/support. Tom Hergert by helping me rally faculty interest and the administrators incentivizing faculty to participate in the initial project, until it gains momentum and recognition.
In the same fashion, as part of the aforementioned group or separate, I would like to host a regularly-meeting group of students, who besides play and entertainment, aim the same process of creating interactive learning materials for their classes/projects. Same “best voted” process by peers. My preferable reward: upper administration is leaving recommendation in the students’ Linkedin account for future employers. I will need both your involvement/support. The student union can be decisive in bringing students to this endeavor. Both of you have more cloud with the student union then only a regular faculty such as me.
In regard to the security (porn alert, see above) I have the agreement of Dr. Tirthankar Ghos with the IS Department. Dr. Ghosh will be most pleased to announce as a class project the provision of a secure environment for the touch screen monitor to be left after the group meetings for “use” by students in the library. Dr. Ghosh is, however, concerned/uncertain with the level of cooperation from IT, considering that for his students to enable such environment, they have to have the “right” access; namely behind firewalls, administrative privileges etc. Each of you will definitely be more persuasive with Phil Thorson convincing him in the merit of having IS student work with SCSU IT technician, since it is a win-win situation: the IT technician does not have to “waste time” (as in 2015) and resolve an issue and the IS student will be having a project-based, real-life learning experience by enabling the project under the supervision of the IT technician. Besides: a. student-centered, project-based learning; b. IT technician time saved, we also aim c. no silos / collaborative SCSU working environment, as promised by the reorganization process.
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.