Digital Literacy for St. Cloud State University
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more on AR in this IMS blog
https://blog.stcloudstate.edu/ims?s=artificial+intelligence
more on China education in this IMS blog
https://blog.stcloudstate.edu/ims/2018/01/06/chinas-transformation-of-higher-education/
by invitation of New Bulgarian University, Sofia, Bulgaria: https://www.nbu.bg/en
May 14, 9-11AM, New Bulgarian University.
short link: http://bit.ly/embed18
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Live stream: backchanneling: @scsutechinstruct ##NBUembed Archived Discussion |
Video 360 excerpt from the discussion:
Семинар „Embedded“ библиотекари и геймификация в библиотеките:
Съвременни американски практики“, 14 май 2018 г., 9.00 ч.-11.00 ч.,
Preliminary Information and Literature. Please do not hesitate to share in the comments section your ideas, suggestions and questions
предварителна информация и литература по дискусията. Не се колебайте да споделите мнения, препоръки и въпроси в “Comment” секцията:
https://blog.stcloudstate.edu/ims/2017/10/03/embedded-librarianship-in-online-courses/
https://blog.stcloudstate.edu/ims/2017/08/24/embedded-librarian-qualifications/
https://blog.stcloudstate.edu/ims/2015/05/04/lms-and-embedded-librarianship/
“Embedded librarianship” also mentioned in:
https://blog.stcloudstate.edu/ims/2015/05/27/handbook-of-mobile-learning/
https://blog.stcloudstate.edu/ims/2016/08/18/digital-humanities-and-libraries/
Gaming and Gamification and Education:
https://blog.stcloudstate.edu/ims/2018/04/18/engage-with-dungeons-and-dragons/
https://blog.stcloudstate.edu/ims?s=iste+standards
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For more information and for backchanneling please use the following social media
за повече въпроси и информация, както и за споделяне на вашите идеи и мисли използвайте следните канали / социални медии:
Facebook:
Twitter:
https://twitter.com/SCSUtechinstruc/status/984437858244145152
LinkedIn discussion on VR/AR
https://www.linkedin.com/groups/2811/2811-6391674579739303939
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even more info
BUT WAIT
how does embedded librarian relates to the emerging technologies in the library?
Teachers can bring VR stories into the classroom in many different ways for meaningful learning experiences. Imagine a scavenger hunt where students narrate a story based on what they find. Or consider using objects they see to identify vocabulary words or recognize letters. Students should have purpose in their viewing and it should directly connect to standards.
Starting with virtual reality, stories in apps such as Google Spotlight Storiesand YouTube 360 videos have been popular from the start.
Similar to the new movie, Ready Player One, they provide an intense experience where the viewer feels like they are in the center of the story.
Using a mobile device or tablet, the student can start the story and look around the scene based on their interest, rather than the cameras focus. New apps such as Baobab VR have continued to appear with more interactions and engagement.
A creative way to have your students create their own virtual stories is using the app Roundme. Upload your 360 image and add directional sound, links and content. Upload portals to walk the viewer into multiple scenes and then easily share the stories by link to the story.
Newer augmented reality apps that work with ARKit have taken another approach to storytelling. Augmented Stories and My Hungry Caterpillar.Qurious, a company that is working on a release blending gaming, making and storytelling in one app.
Storyfab, turns our students into the directors of the show
A new AR book, SpyQuest, has moved the immersive experience a big step forward as it helps define the story by bringing the images to life. Through the camera lens on a device, the stories make students the agents in an adventure into the world of espionage. The augmented reality experiences on the images use the accompanying app to scan the scene and provide further insight into the story.
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more on storytelling in this IMS blog
https://blog.stcloudstate.edu/ims?s=digital+storytelling
more on VR and storytelling in this IMS blog
https://blog.stcloudstate.edu/ims?s=virtual+reality+storytelling
According to the latest report from Babson Survey Research Group, nearly 6.5 million American undergraduates now take at least one course online
1. Listen to students and faculty. Every college, university, or online-learning provider has a different approach to online learning. At Indiana University, where more than 30 percent of students take at least one online course, the online education team has launched Next.IU, an innovative pilot program to solicit feedback from the campus community before making any major edtech decision. By soliciting direct feedback from students and faculty, institutions can avoid technical difficulties and secure support before rolling out the technology campus-wide.
2. Go mobile. Nine in 10 undergraduates own a smartphone, and the majority of online students complete some coursework on a mobile device. Tapping into the near-ubiquity of mobile computing on campus can help streamline the proctoring and verification process. Rather than having to log onto a desktop, students can use features like fingerprint scan and facial recognition that are already integrated into most smartphones to verify their identity directly from their mobile device.
For a growing number of students, mobile technology is the most accessible way to engage in online coursework, so mobile verification provides not only a set of advanced security tools, but also a way for universities to meet students where they are.
3. Learn from the data. Analytical approaches to online test security are still in the early stages. Schools may be more susceptible to online “heists” if they are of a certain size or administer exams in a certain way, but institutions need data to benchmark against their peers and identify pain points in their approach to proctoring.
In an initial pilot with 325,000 students, for instance, we found that cheating rose and fell with the seasons—falling from 6.62 percent to 5.49 percent from fall to spring, but rising to a new high of 6.65 percent during the summer.
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more on proctoring in this IMS blog
https://blog.stcloudstate.edu/ims?s=proctoring
Today’s vocast will be broadcasted live at:
Adobe Connect | Facebook Live | Twitter (#IMSvodcast) |
and will be archived at:
SCSU MediaSpace | YouTube (subscribe for the channel for future conversations)
Constructivism.
Student-centered learning theory and practice are based on the constructivist learning theory that emphasizes the learner’s critical role in constructing meaning from new information and prior experience.
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https://blog.stcloudstate.edu/ims/2018/02/12/first-ims-podcast-on-technology-in-education/
the Center for the Advanced Study of Technology Leadership in Education – CASTLE
If a school’s reputation and pride are built on decades or centuries of “this is how we’ve always done things here,” resistance from staff, parents, and alumni to significant changes may be fierce. In such institutions, heads of school may have to steer carefully between deeply ingrained habits and the need to modernize the information tools with which students and faculty work
Too often, when navigating faculty or parental resistance, school leaders and technology staff make reassurances that things will not have to change much in the classroom or that slow baby steps are OK. Unfortunately, this results in a different problem, which is that schools have now invested significant money, time, and energy into digital technologies but are using them sparingly and seeing little impact. In such schools, replicative uses of technology are quite common, but transformative uses that leverage the unique affordances of technology are quite rare.
many schools fail to proceed further because they don’t have a collective vision of what more transformative uses of technology might look like, nor do they have a shared understanding of and commitment to what it will take to get to such a place. As a result, faculty instruction and the learning experiences of students change little or not at all.
These schools have taken the time to involve all stakeholders—including students—in substantive conversations about what digital tools will allow them to do differently compared with previous analog practices. Their visions promote the potential of computing devices to facilitate all of those elements we now think of as essential 21st-century capacities: confidence, curiosity, enthusiasm, passion, critical thinking, problem-solving, and self-direction. Technology doesn’t simply support traditional teaching—it transforms it for deeper thinking and gives students more agency over their own learning.
Fear
Another prevalent issue preventing technology change in schools is fear—fear of change, of the unknown, of letting go of what we know best, of being learners again. But it’s also a fear of letting kids have wide access to the Internet with the possibility of cyberbullying, access to inappropriate material, and exposure to online predators or even excessive advertising. Fears, of course, need to be surfaced and addressed.
The fear drives some schools to ban cellphones, disallow students and faculty from using Facebook, and lock down Internet filters so tightly that useful websites are inaccessible. They prohibit the use of Twitter and YouTube, and they block blogs. Some educators see these types of responses as principled stands against the shortcomings and hassles of digital technologies. Others see them as rejections of the dehumanization of the education process by soulless machines. Often, however, it’s just schools clinging to the past and elevating what is comfortable or familiar over the potential of technology to help them better deliver on their school missions.
Heads of school don’t have to be skilled users themselves to be effective technology leaders, but they do have to exercise appropriate oversight and convey the message—repeatedly—that frequent, meaningful technology use in school is both important and expected. Nostalgia aside, there is no foreseeable future in which the primacy of printed text is not superseded by electronic text and multimedia. When nearly all information is digital or online, multi-modal and multimedia, accessed by mobile devices that fit in our pockets, the question should not be whether schools prepare students for a digital learning landscape, but rather how.
Control
Many educators aren’t necessarily afraid of technology, but they are so accustomed to heavily teacher-directed classrooms that they are leery about giving up control—and can’t see the value in doing so.
Although most of us recognize that mobile computers connected to the Internet may be the most powerful learning devices yet invented—and that youth are learning in powerful ways at home with these technologies—allowing students to have greater autonomy and ownership of the learning process can still seem daunting and questionable.
The “beyond” is particularly important. When we give students some voice in and choice about what and how they learn, we honor basic human needs for autonomy, we enhance students’ interest and engagement, and we truly actualize our missions of preparing lifelong learners.
The goal of instructional transformation is to empower students, not to disempower teachers. While instructor unfamiliarity with digital technologies, inquiry- or problem-based teaching techniques, or deeper learning strategies may result in some initial discomfort, these challenges can be overcome with robust support.
Support
A few workshops here and there rarely result in large-scale changes in implementation.
teacher-driven “unconferences” or “edcamps,” at which educators propose and facilitate discussion topics, can be powerful mechanisms for fostering professional dialogue and learning. Similarly, some schools offer voluntary “Tech Tuesdays” or “appy hours” to foster digital learning among interested faculty.
In addition to existing IT support, technology integration staff, or librarians/media specialists, some schools have student technology teams that are on call for assistance when needed.
A few middle schools and high schools go even further and assign teachers their own individual student technology mentors. These student-teacher pairings last all school year and comprise the first line of support for educators’ technology questions.
As teachers, heads of school, counselors, coaches, and librarians, we all now have the ability to participate in ongoing, virtual, global communities of practice.
Whether formal or informal, the focus of technology-related professional learning should be on student learning, not on the tools or devices. Independent school educators should always ask, “Technology for the purpose of what?” when considering the inclusion of digital technologies into learning activities. Technology never should be implemented just for technology’s sake.
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more on digital literacy for EDAD in this IMS blog
https://blog.stcloudstate.edu/ims?s=digital+literacy+edad
university administrators want to make sure their courses are up to standards and their students are supported.
A new report from the Learning House and Aslanian Market Research measures the opinions of 1,500 online students regarding everything from course satisfaction to study methods
institutions need to more clearly share the positive outcomes that come with completing degree and certificate programs online.”
online courses would be better if there was more contact and engagement.
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more on online students in this IMS blog
https://blog.stcloudstate.edu/ims?s=online+students
Applications for the 2018 Institute will be accepted between December 1, 2017 and January 27, 2018. Scholars accepted to the program will be notified in early March 2018.
Title:
Learning to Harness Big Data in an Academic Library
Abstract (200)
Research on Big Data per se, as well as on the importance and organization of the process of Big Data collection and analysis, is well underway. The complexity of the process comprising “Big Data,” however, deprives organizations of ubiquitous “blue print.” The planning, structuring, administration and execution of the process of adopting Big Data in an organization, being that a corporate one or an educational one, remains an elusive one. No less elusive is the adoption of the Big Data practices among libraries themselves. Seeking the commonalities and differences in the adoption of Big Data practices among libraries may be a suitable start to help libraries transition to the adoption of Big Data and restructuring organizational and daily activities based on Big Data decisions.
Introduction to the problem. Limitations
The redefinition of humanities scholarship has received major attention in higher education. The advent of digital humanities challenges aspects of academic librarianship. Data literacy is a critical need for digital humanities in academia. The March 2016 Library Juice Academy Webinar led by John Russel exemplifies the efforts to help librarians become versed in obtaining programming skills, and respectively, handling data. Those are first steps on a rather long path of building a robust infrastructure to collect, analyze, and interpret data intelligently, so it can be utilized to restructure daily and strategic activities. Since the phenomenon of Big Data is young, there is a lack of blueprints on the organization of such infrastructure. A collection and sharing of best practices is an efficient approach to establishing a feasible plan for setting a library infrastructure for collection, analysis, and implementation of Big Data.
Limitations. This research can only organize the results from the responses of librarians and research into how libraries present themselves to the world in this arena. It may be able to make some rudimentary recommendations. However, based on each library’s specific goals and tasks, further research and work will be needed.
Research Literature
“Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it…”
– Dan Ariely, 2013 https://www.asist.org/publications/bulletin/aprilmay-2017/big-datas-impact-on-privacy-for-librarians-and-information-professionals/
Big Data is becoming an omnipresent term. It is widespread among different disciplines in academia (De Mauro, Greco, & Grimaldi, 2016). This leads to “inconsistency in meanings and necessity for formal definitions” (De Mauro et al, 2016, p. 122). Similarly, to De Mauro et al (2016), Hashem, Yaqoob, Anuar, Mokhtar, Gani and Ullah Khan (2015) seek standardization of definitions. The main connected “themes” of this phenomenon must be identified and the connections to Library Science must be sought. A prerequisite for a comprehensive definition is the identification of Big Data methods. Bughin, Chui, Manyika (2011), Chen et al. (2012) and De Mauro et al (2015) single out the methods to complete the process of building a comprehensive definition.
In conjunction with identifying the methods, volume, velocity, and variety, as defined by Laney (2001), are the three properties of Big Data accepted across the literature. Daniel (2015) defines three stages in big data: collection, analysis, and visualization. According to Daniel, (2015), Big Data in higher education “connotes the interpretation of a wide range of administrative and operational data” (p. 910) and according to Hilbert (2013), as cited in Daniel (2015), Big Data “delivers a cost-effective prospect to improve decision making” (p. 911).
The importance of understanding the process of Big Data analytics is well understood in academic libraries. An example of such “administrative and operational” use for cost-effective improvement of decision making are the Finch & Flenner (2016) and Eaton (2017) case studies of the use of data visualization to assess an academic library collection and restructure the acquisition process. Sugimoto, Ding & Thelwall (2012) call for the discussion of Big Data for libraries. According to the 2017 NMC Horizon Report “Big Data has become a major focus of academic and research libraries due to the rapid evolution of data mining technologies and the proliferation of data sources like mobile devices and social media” (Adams, Becker, et al., 2017, p. 38).
Power (2014) elaborates on the complexity of Big Data in regard to decision-making and offers ideas for organizations on building a system to deal with Big Data. As explained by Boyd and Crawford (2012) and cited in De Mauro et al (2016), there is a danger of a new digital divide among organizations with different access and ability to process data. Moreover, Big Data impacts current organizational entities in their ability to reconsider their structure and organization. The complexity of institutions’ performance under the impact of Big Data is further complicated by the change of human behavior, because, arguably, Big Data affects human behavior itself (Schroeder, 2014).
De Mauro et al (2015) touch on the impact of Dig Data on libraries. The reorganization of academic libraries considering Big Data and the handling of Big Data by libraries is in a close conjunction with the reorganization of the entire campus and the handling of Big Data by the educational institution. In additional to the disruption posed by the Big Data phenomenon, higher education is facing global changes of economic, technological, social, and educational character. Daniel (2015) uses a chart to illustrate the complexity of these global trends. Parallel to the Big Data developments in America and Asia, the European Union is offering access to an EU open data portal (https://data.europa.eu/euodp/home ). Moreover, the Association of European Research Libraries expects under the H2020 program to increase “the digitization of cultural heritage, digital preservation, research data sharing, open access policies and the interoperability of research infrastructures” (Reilly, 2013).
The challenges posed by Big Data to human and social behavior (Schroeder, 2014) are no less significant to the impact of Big Data on learning. Cohen, Dolan, Dunlap, Hellerstein, & Welton (2009) propose a road map for “more conservative organizations” (p. 1492) to overcome their reservations and/or inability to handle Big Data and adopt a practical approach to the complexity of Big Data. Two Chinese researchers assert deep learning as the “set of machine learning techniques that learn multiple levels of representation in deep architectures (Chen & Lin, 2014, p. 515). Deep learning requires “new ways of thinking and transformative solutions (Chen & Lin, 2014, p. 523). Another pair of researchers from China present a broad overview of the various societal, business and administrative applications of Big Data, including a detailed account and definitions of the processes and tools accompanying Big Data analytics. The American counterparts of these Chinese researchers are of the same opinion when it comes to “think about the core principles and concepts that underline the techniques, and also the systematic thinking” (Provost and Fawcett, 2013, p. 58). De Mauro, Greco, and Grimaldi (2016), similarly to Provost and Fawcett (2013) draw attention to the urgent necessity to train new types of specialists to work with such data. As early as 2012, Davenport and Patil (2012), as cited in Mauro et al (2016), envisioned hybrid specialists able to manage both technological knowledge and academic research. Similarly, Provost and Fawcett (2013) mention the efforts of “academic institutions scrambling to put together programs to train data scientists” (p. 51). Further, Asomoah, Sharda, Zadeh & Kalgotra (2017) share a specific plan on the design and delivery of a big data analytics course. At the same time, librarians working with data acknowledge the shortcomings in the profession, since librarians “are practitioners first and generally do not view usability as a primary job responsibility, usually lack the depth of research skills needed to carry out a fully valid” data-based research (Emanuel, 2013, p. 207).
Borgman (2015) devotes an entire book to data and scholarly research and goes beyond the already well-established facts regarding the importance of Big Data, the implications of Big Data and the technical, societal, and educational impact and complications posed by Big Data. Borgman elucidates the importance of knowledge infrastructure and the necessity to understand the importance and complexity of building such infrastructure, in order to be able to take advantage of Big Data. In a similar fashion, a team of Chinese scholars draws attention to the complexity of data mining and Big Data and the necessity to approach the issue in an organized fashion (Wu, Xhu, Wu, Ding, 2014).
Bruns (2013) shifts the conversation from the “macro” architecture of Big Data, as focused by Borgman (2015) and Wu et al (2014) and ponders over the influx and unprecedented opportunities for humanities in academia with the advent of Big Data. Does the seemingly ubiquitous omnipresence of Big Data mean for humanities a “railroading” into “scientificity”? How will research and publishing change with the advent of Big Data across academic disciplines?
Reyes (2015) shares her “skinny” approach to Big Data in education. She presents a comprehensive structure for educational institutions to shift “traditional” analytics to “learner-centered” analytics (p. 75) and identifies the participants in the Big Data process in the organization. The model is applicable for library use.
Being a new and unchartered territory, Big Data and Big Data analytics can pose ethical issues. Willis (2013) focusses on Big Data application in education, namely the ethical questions for higher education administrators and the expectations of Big Data analytics to predict students’ success. Daries, Reich, Waldo, Young, and Whittinghill (2014) discuss rather similar issues regarding the balance between data and student privacy regulations. The privacy issues accompanying data are also discussed by Tene and Polonetsky, (2013).
Privacy issues are habitually connected to security and surveillance issues. Andrejevic and Gates (2014) point out in a decision making “generated by data mining, the focus is not on particular individuals but on aggregate outcomes” (p. 195). Van Dijck (2014) goes into further details regarding the perils posed by metadata and data to the society, in particular to the privacy of citizens. Bail (2014) addresses the same issue regarding the impact of Big Data on societal issues, but underlines the leading roles of cultural sociologists and their theories for the correct application of Big Data.
Library organizations have been traditional proponents of core democratic values such as protection of privacy and elucidation of related ethical questions (Miltenoff & Hauptman, 2005). In recent books about Big Data and libraries, ethical issues are important part of the discussion (Weiss, 2018). Library blogs also discuss these issues (Harper & Oltmann, 2017). An academic library’s role is to educate its patrons about those values. Sugimoto et al (2012) reflect on the need for discussion about Big Data in Library and Information Science. They clearly draw attention to the library “tradition of organizing, managing, retrieving, collecting, describing, and preserving information” (p.1) as well as library and information science being “a historically interdisciplinary and collaborative field, absorbing the knowledge of multiple domains and bringing the tools, techniques, and theories” (p. 1). Sugimoto et al (2012) sought a wide discussion among the library profession regarding the implications of Big Data on the profession, no differently from the activities in other fields (e.g., Wixom, Ariyachandra, Douglas, Goul, Gupta, Iyer, Kulkami, Mooney, Phillips-Wren, Turetken, 2014). A current Andrew Mellon Foundation grant for Visualizing Digital Scholarship in Libraries seeks an opportunity to view “both macro and micro perspectives, multi-user collaboration and real-time data interaction, and a limitless number of visualization possibilities – critical capabilities for rapidly understanding today’s large data sets (Hwangbo, 2014).
The importance of the library with its traditional roles, as described by Sugimoto et al (2012) may continue, considering the Big Data platform proposed by Wu, Wu, Khabsa, Williams, Chen, Huang, Tuarob, Choudhury, Ororbia, Mitra, & Giles (2014). Such platforms will continue to emerge and be improved, with librarians as the ultimate drivers of such platforms and as the mediators between the patrons and the data generated by such platforms.
Every library needs to find its place in the large organization and in society in regard to this very new and very powerful phenomenon called Big Data. Libraries might not have the trained staff to become a leader in the process of organizing and building the complex mechanism of this new knowledge architecture, but librarians must educate and train themselves to be worthy participants in this new establishment.
Method
The study will be cleared by the SCSU IRB.
The survey will collect responses from library population and it readiness to use and use of Big Data. Send survey URL to (academic?) libraries around the world.
Data will be processed through SPSS. Open ended results will be processed manually. The preliminary research design presupposes a mixed method approach.
The study will include the use of closed-ended survey response questions and open-ended questions. The first part of the study (close ended, quantitative questions) will be completed online through online survey. Participants will be asked to complete the survey using a link they receive through e-mail.
Mixed methods research was defined by Johnson and Onwuegbuzie (2004) as “the class of research where the researcher mixes or combines quantitative and qualitative research techniques, methods, approaches, concepts, or language into a single study” (Johnson & Onwuegbuzie, 2004 , p. 17). Quantitative and qualitative methods can be combined, if used to complement each other because the methods can measure different aspects of the research questions (Sale, Lohfeld, & Brazil, 2002).
Sampling design
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
Bail, C. A. (2014). The cultural environment: measuring culture with big data. Theory and Society, 43(3–4), 465–482. https://doi.org/10.1007/s11186-014-9216-5
Borgman, C. L. (2015). Big Data, Little Data, No Data: Scholarship in the Networked World. MIT Press.
Bruns, A. (2013). Faster than the speed of print: Reconciling ‘big data’ social media analysis and academic scholarship. First Monday, 18(10). Retrieved from http://firstmonday.org/ojs/index.php/fm/article/view/4879
Bughin, J., Chui, M., & Manyika, J. (2010). Clouds, big data, and smart assets: Ten tech-enabled business trends to watch. McKinsey Quarterly, 56(1), 75–86.
Chen, X. W., & Lin, X. (2014). Big Data Deep Learning: Challenges and Perspectives. IEEE Access, 2, 514–525. https://doi.org/10.1109/ACCESS.2014.2325029
Cohen, J., Dolan, B., Dunlap, M., Hellerstein, J. M., & Welton, C. (2009). MAD Skills: New Analysis Practices for Big Data. Proc. VLDB Endow., 2(2), 1481–1492. https://doi.org/10.14778/1687553.1687576
Daniel, B. (2015). Big Data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904–920. https://doi.org/10.1111/bjet.12230
Daries, J. P., Reich, J., Waldo, J., Young, E. M., Whittinghill, J., Ho, A. D., … Chuang, I. (2014). Privacy, Anonymity, and Big Data in the Social Sciences. Commun. ACM, 57(9), 56–63. https://doi.org/10.1145/2643132
De Mauro, A. D., Greco, M., & Grimaldi, M. (2016). A formal definition of Big Data based on its essential features. Library Review, 65(3), 122–135. https://doi.org/10.1108/LR-06-2015-0061
De Mauro, A., Greco, M., & Grimaldi, M. (2015). What is big data? A consensual definition and a review of key research topics. AIP Conference Proceedings, 1644(1), 97–104. https://doi.org/10.1063/1.4907823
Dumbill, E. (2012). Making Sense of Big Data. Big Data, 1(1), 1–2. https://doi.org/10.1089/big.2012.1503
Eaton, M. (2017). Seeing Library Data: A Prototype Data Visualization Application for Librarians. Publications and Research. Retrieved from http://academicworks.cuny.edu/kb_pubs/115
Emanuel, J. (2013). Usability testing in libraries: methods, limitations, and implications. OCLC Systems & Services: International Digital Library Perspectives, 29(4), 204–217. https://doi.org/10.1108/OCLC-02-2013-0009
Graham, M., & Shelton, T. (2013). Geography and the future of big data, big data and the future of geography. Dialogues in Human Geography, 3(3), 255–261. https://doi.org/10.1177/2043820613513121
Harper, L., & Oltmann, S. (2017, April 2). Big Data’s Impact on Privacy for Librarians and Information Professionals. Retrieved November 7, 2017, from https://www.asist.org/publications/bulletin/aprilmay-2017/big-datas-impact-on-privacy-for-librarians-and-information-professionals/
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Ullah Khan, S. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47(Supplement C), 98–115. https://doi.org/10.1016/j.is.2014.07.006
Hwangbo, H. (2014, October 22). The future of collaboration: Large-scale visualization. Retrieved November 7, 2017, from http://usblogs.pwc.com/emerging-technology/the-future-of-collaboration-large-scale-visualization/
Laney, D. (2001, February 6). 3D Data Management: Controlling Data Volume, Velocity, and Variety.
Miltenoff, P., & Hauptman, R. (2005). Ethical dilemmas in libraries: an international perspective. The Electronic Library, 23(6), 664–670. https://doi.org/10.1108/02640470510635746
Philip Chen, C. L., & Zhang, C.-Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275(Supplement C), 314–347. https://doi.org/10.1016/j.ins.2014.01.015
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Unlocking the Promise of Digital Assessment
By Stacey Newbern Dammann, EdD, and Josh DeSantis October 30, 2017
The proliferation of mobile devices and the adoption of learning applications in higher education simplifies formative assessment. Professors can, for example, quickly create a multi-modal performance that requires students to write, draw, read, and watch video within the same assessment. Other tools allow for automatic grade responses, question-embedded documents, and video-based discussion.
Integration of technology is aligned to sound formative assessment design. Formative assessment is most valuable when it addresses student understanding, progress toward competencies or standards, and indicates concepts that need further attention for mastery. Additionally, formative assessment provides the instructor with valuable information on gaps in their students’ learning which can imply instructional changes or additional coverage of key concepts. The use of tech tools can make the creation, administration, and grading of formative assessment more efficient and can enhance reliability of assessments when used consistently in the classroom. Selecting one that effectively addresses your assessment needs and enhances your teaching style is critical.
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more on digital assessment in this IMS blog
https://blog.stcloudstate.edu/ims/2017/03/15/fake-news-bib/
Setting cell phone expectations early is key to accessing the learning potential of these devices and minimizing the distraction factor.Liz Kolb September 11, 2017
Ten is now the average age when children receive their first cell phones
Ask your students questions such as:
Using a Stoplight Management Approach
Post a red button on the classroom door: the cell phone parking lot.
Post a yellow button on the classroom door
Post a green button on the classroom door
Establishing a Class Contract: Ask them to brainstorm consequences and write them into a class contract.
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more on the use of BYOD in this IMS blog
https://blog.stcloudstate.edu/ims/2017/04/03/use-of-laptops-in-the-classroom/
