Searching for "student centered learning"

30+ QR Code Resources

30+ QR Code Resources

http://www.themobilenative.org/2014/05/30-qr-code-resources.html

1. A Dummies Guide to QR Codes
2. QR Code Implementation Guide
3. Using QR Codes in the Classroom
4. QR Code Scavenger Hunt- Part Deux
5. QR Code Quest
6. Transliteracy- QR Codes and Art
7. QR Codes Help Paint a Deeper Picture Behind Art
8. Interactive Physical Education QR Skill Posters
9. QR Codes-Lesson and Resources
10. QR Codes in Higher Education
11. QR Treasure Hunt Generator
12. 40 Interesting Ways to Use QR Codes in the Classroom
13. QR Code Lessons and Activities
14. All About QR Codes in Your Classroom
15. 44 QR Code Resources for Teaching and Learning
16. Your Quick-Guide to Using QR Codes in Education
17. Learning in Hand #25: QR Codes
18. Reflection Facilitated by QR Codes
19. Crazy for QR Codes
20. 18 Innovative Uses of QR Codes
21. How QR Codes In the Classroom Can Empower Student Voice
22. 7 Fun Ways to Use QR Codes in Education
23. 3 Fun Ways to Use QR Codes for Language Learning
24. 51 Interesting Ways to Use QR Codes to Support Learning
25. QR Codes and Student Centered Learning
26. QR Code Roundup
27. QR Codes Explained and Ideas for Classroom Use
28. How to Incorporate QR Codes in the Classroom
29. QR Codes in the Classroom
30. QR Codes for Differentiated Instruction
31. Twelve Ideas for Teaching with QR Codes
32. Have You Started Using QR Codes in Your Classroom Yet?
33. Cybraryman’s QR Code Page
34. QR Codes in Education- Livebinder by Steven Anderson

AR VR engineering education

Using AR/VR for Innovative Engineering Education

The use of AR/VR in educational settings is on the rise, paving the way for new careers and a workforce trained to embrace technology.

If projections stay on track, the global spending on educational AR/VR is expected to rise from $1.8 billion to $12.6 billion over the next four years.

Screen Shot 2021-01-25 at 12.05.19 pm

the International Data Corporation (IDC) released a report indicating that the pandemic has fueled an impressive forecast of worldwide expenditures on AR/VR, which are expected to grow from $12 billion in 2020 to $72.8 billion by 2024.

rom completing spinal surgery to training at a high-tech facility, such as the University of Nebraska Medical Center’s Davis Global Center, which has AR/VR and holographic technologies among its many offerings.

University of Hong Kong–Innovation Academy

Home (innoacademy)

MIT–MIT.nano Immersion Lab

the MIT.nano Immersion Lab, an open-access facility for all MIT students, faculty, researchers and external users.

University of Michigan–Augmented Tectonics

Purdue University–Skill-XR

https://www.purdue.edu/newsroom/releases/2016/Q2/new-tool-for-virtual-and-augmented-reality-uses-deep-learning.html

UC Berkeley–InsightXR

https://cto.berkeley.edu/innovation/berkeley-changemaker-technology-innovation-grants/vrtutor

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more on immersive in this IMS blog
https://blog.stcloudstate.edu/ims?s=immersive

Educause 2020 IT issues survey

https://www.surveygizmo.com/s3/5155654/IT-Issues-2020?sguid=60122224

what i find most important:
Future IT Workforce: Deploying a broad array of modern recruitment, retention, and employment practices to develop a resilient IT talent pipeline for the institution

Digital Integrations: Ensuring system interoperability, scalability, and extensibility, as well as data integrity, security, standards, and governance, across multiple applications and platforms

Engaged Learning: Incorporating technologies that enable students to create content and engage in active learning in course curricula

Student Retention and Completion: Developing the capabilities and systems to incorporate artificial intelligence into student services to provide personalized, timely support

Administrative Simplification: Applying user-centered design, process improvement, and system reengineering to reduce redundant or unnecessary efforts and improve end-user experiences

Improved Enrollment: Using technology, data, and analytics to develop an inclusive and financially sustainable enrollment strategy to serve more and new learners by personalizing recruitment, enrollment, and learning experiences

Workforce of the Future: Using technology to develop curriculum, content, and learning experiences that prepare students for the evolving workforce

Holistic Student Success: Applying technology and data, including artificial intelligence, to understand and address the numerous contributors to student success, from finances to health and wellness to academic performance and degree planning (my note: this is what Christine Waisner, Mark Gill and Plamen Miltenoff are trying to do with their VR research)

Improved Teaching: Strengthening engagement among faculty, technologists, and researchers to achieve the true and expanding potential of technology to improve teaching

Student-Centric Higher Education: Creating a student-services ecosystem to support the entire student life cycle, from prospecting to enrollment, learning, job placement, alumni engagement, and continuing education

Finland ideas for US education

OPINION: Can this 12-step program from Finland aid U.S. education?

 Finland system consistently receives top marks from UNICEF, the OECD and the World Economic Forum.
Many U.S. states are similar in population size and demographics to Finland, and education is largely run at the state level. In the economically depressed forest region of North Karelia — on the Russian border — where we spent much of our time, the unemployment rate is nearly 15 percent, compared with under 5 percent in America and our home state of New York. However, the U.S. child poverty rate is four times higher than Finland’s.
Delegations and universities from China and around the developing world are visiting Finland to learn how to improve their own school systems.Singapore has launched a series of Finnish-style school reforms.

n Finland, we heard none of the clichés common in U.S. education reform circles, like “rigor,” “standards-based accountability,” “data-driven instruction,” “teacher evaluation through value-added measurement” or getting children “college- and career-ready” starting in kindergarten.

Instead, Finnish educators and officials constantly stressed to us their missions of helping every child reach his or her full potential and supporting all children’s well-being. “School should be a child’s favorite place,” said Heikki Happonen, an education professor at the University of Eastern Finland and an authority on creating warm, child-centered learning environments.

How can the United States improve its schools? We can start by piloting and implementing these 12 global education best practices, many of which are working extremely well for Finland:

1) Emphasize well-being.

2) Upgrade testing and other assessments. 

3) Invest resources fairly.

4) Boost learning through physical activity. 

5) Change the focus. Create an emotional atmosphere and physical environment of warmth, comfort and safety so that children are happy and eager to come to school. Teach not just basic skills, but also arts, crafts, music, civics, ethics, home economics and life skills.

6) Make homework efficient. Reduce the homework load in elementary and middle schools to no more than 30 minutes per night, and make it responsibility-based rather than stress-based.

7) Trust educators and children. Give them professional respect, creative freedom and autonomy, including the ability to experiment, take manageable risks and fail in the pursuit of success.

8) Shorten the school day. Deliver lessons through more efficient teaching and scheduling, as Finland does. Simplify curriculum standards to a framework that can fit into a single book, and leave detailed implementation to local districts.

9) Institute universal after-school programs.

10) Improve, expand and destigmatize vocational and technical education.   Encourage more students to attend schools in which they can acquire valuable career/trade skills.

11) Launch preventive special-education interventions early and aggressively. 

12) Revamp teacher training toward a medical and military model. Shift to treating the teaching profession as a critical national security function requiring government-funded, graduate-level training in research and collaborative clinical practice, as Finland does.

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more on Finland Phenomenon in this IMS blog
https://blog.stcloudstate.edu/ims?s=finland+phenomenon

transforming liaison roles in research libraries

!*!*!*!*! — this article was pitched by Mark Vargas in the fall of 2013, back then dean of LRS and discussed at a faculty meeting at LRS in the same year—- !*!*!*!

New Roles for New Times: Transforming Liaison Roles in Research Libraries

https://conservancy.umn.edu/bitstream/handle/11299/169867/TransformingLiaisonRoles.pdf?sequence=1&isAllowed=y

(p. 4) Building strong relationships with faculty and other campus professionals, and establishing collaborative partnerships within and across institutions, are necessary building blocks to librarians’ success. In a traditional liaison model, librarians use their subject knowledge to select books and journals and teach guest lectures.

“Liaisons cannot be experts themselves in each new capability, but knowing when to call in a colleague, or how to describe appropriate expert capabilities to faculty, will be key to the new liaison role.

six trends in the development of new roles for library liaisons
user engagement is a driving factor
what users do (research, teaching, and learning) rather than on what librarians do (collections, reference, library instruction).
In addition, an ALA-accredited master’s degree in library science is no longer strictly required.
In a networked world, local collections as ends in themselves make learning fragmentary and incomplete. (p. 5)
A multi-institutional approach is the only one that now makes sense.
Scholars already collaborate; libraries need to make it easier for them to do so.
but they also advise and collaborate on issues of copyright, scholarly communication, data management, knowledge management, and information literacy. The base level of knowledge that a liaison must possess is much broader than familiarity with a reference collection or facility with online searching; instead, they must constantly keep up with evolving pedagogies and research methods, rapidly developing tools, technologies, and ever-changing policies that facilitate and inform teaching, learning, and research in their assigned disciplines.
In many research libraries, programmatic efforts with information literacy have been too narrowly defined. It is not unusual for libraries to focus on freshman writing programs and a series of “one-shot” or invited guest lectures in individual courses. While many librarians have become excellent teachers, traditional one-shot, in-person instructional sessions can vary in quality depending on the training librarians have received in this arena; and they neither scale well nor do they necessarily address broader curricular goals. Librarians at many institutions are now focusing on collaborating with faculty to develop thoughtful assignments and provide online instructional materials that are built into key courses within a curriculum and provide scaffolding to help students develop library research skills over the course of their academic careers.
And many libraries stated that they lack instructional designers and/or educational technologists on their staff, limiting the development of interactive online learning modules and tutorials. (my note: or just ignore the desire by unites such as IMS to help).

(p. 7). This move away from supervision allows the librarians to focus on their liaison responsibilities rather than on the day-to-day operations of a library and its attendant personnel needs.

effectively support teaching, (1.) learning, and research; (2.) identify opportunities for further development of tools and services; (3.) and connect students, staff, and faculty to deeper expertise when needed.

At many institutions, therefore, the conversation has focused on how to supplement and support the liaison model with other staff.

At many institutions, therefore, the conversation has focused on how to supplement and support the liaison model with other staff.

the hybrid exists within the liaison structure, where liaisons also devote a portion of their time (e.g., 20% or more) to an additional area of expertise, for example digital humanities and scholarly communication, and may work with liaisons across all disciplinary areas. (my note: and at the SCSU library, the librarians firmly opposed the request for a second master’s degree)

functional specialists who do not have liaison assignments to specific academic departments but instead serve as “superliaisons” to other librarians and to the entire campus. Current specialist areas of expertise include copyright, geographic information systems (GIS), media production and integration, distributed education or e-learning, data management, emerging technologies, user experience, instructional design, and bioinformatics. (everything in italics is currently done by IMS faculty).

divided into five areas of functional specialization: information resources and collections management; information literacy, instruction, and curriculum development; discovery and access; archival and special collections; scholarly communication and the research enterprise.

E-Scholarship Collaborative, a Research Support Services Collaborative (p. 8).

p. 9. managing alerts and feeds, personal archiving, and using social networking for teaching and professional development

p. 10. new initiatives in humanistic research and teaching are changing the nature and frequency of partnerships between faculty and the Libraries. In particular, cross-disciplinary Humanities Laboratories (http://fhi.duke.edu/labs), supported by the John Hope Franklin Humanities Institute and the Andrew W. Mellon Foundation-funded Humanities Writ Large project, have allowed liaisons to partner with faculty to develop and curate new forms of scholarship.

consultations on a range of topics, such as how to use social media to effectively communicate academic research and how to mark up historical texts using the Text Encoding Initiative (TEI) guidelines

p. 10. http://www.rluk.ac.uk/news/rluk-report-the-role-of-research-libraries-in-the-creation-archiving-curation-and-preservation-of-tools-for-the-digital-humanities/
The RLUK report identified a wide skills gap in nine key areas where future involvement of liaisons is considered important now and expected to grow

p. 11. Media literacy, and facilitating the integration of media into courses, is an area in which research libraries can play a lead role at their institutions. (my note: yet still suppressed or outright denied to IMS to conducts such efforts)

Purdue Academic Course Transformation, or IMPACT (http://www.lib.purdue.edu/infolit/impact). The program’s purpose is to make foundational courses at Purdue more student-centered and participatory. Librarians are key members of interdepartmental teams that “work with Purdue instructors to redesign courses by applying evidence-based educational practices” and offer “learning solutions” that help students engage with and critically evaluate information. (my note: as offered by Keith and myself to Miguel, the vice provost for undergrads, who left; then offered to First Year Experience faculty, but ignored by Christine Metzo; then offered again to Glenn Davis, who bounced it back to Christine Metzo).

p. 15. The NCSU Libraries Fellows Program offers new librarians a two-year appointment during which they develop expertise in a functional area and contribute to an innovative initiative of strategic importance. NCSU Libraries typically have four to six fellows at a time, bringing in people with needed skills and working to find ongoing positions when they have a particularly good match. Purdue Libraries have experimented with offering two-year visiting assistant professor positions. And the University of Minnesota has hired a second CLIR fellow for a two-year digital humanities project; the first CLIR fellow now holds an ongoing position as a curator in Archives and Special Collections. The CLIR Fellowship is a postdoctoral program that hires recent PhD graduates (non-librarians), allowing them to explore alternative careers and allowing the libraries to benefit from their discipline-specific expertise.

second IMS podcast on technology in education

Second IMS podcast on technology in education: Constructivism

Today’s vocast will be broadcasted live at:

Adobe Connect      |     Facebook Live   |       Twitter (#IMSvodcast) |

and will be archived at:

SCSU MediaSpaceYouTube   (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.

  • What is it?
  • Why do we have to know about it
  • Can we just disagree and stick to behaviorism?
  • Is it about student engagement?
  • Is it about the use of technology?
  • Resources
    • https://blog.stcloudstate.edu/ims/2014/06/28/constructivism-lecture-versus-project-based-learning/
      https://blog.stcloudstate.edu/ims/2013/12/03/translating-constructivism-into-instructional-design-potential-and-limitations/
      https://blog.stcloudstate.edu/ims/2016/03/28/student-centered-learning-literature-review/
      https://blog.stcloudstate.edu/ims/2015/11/05/online-discussion-with-plovdiv-university/
      https://blog.stcloudstate.edu/ims/2015/05/27/handbook-of-mobile-learning/
      Crompton, Muilenburg and Berge’s definition for m-learning is “learning across multiple contexts, through social and content interactions, using personal electronic devices.”
    • The “context”in this definition encompasses m-learnng that is formalself-directed, and spontaneous learning, as well as learning that is context aware and context neutral.
    • therefore, m-learning can occur inside or outside the classroom, participating in a formal lesson on a mobile device; it can be self-directed, as a person determines his or her own approach to satisfy a learning goal; or spontaneous learning, as a person can use the devices to look up something that has just prompted an interest (Crompton, 2013, p. 83). (Gaming article Tallinn)Constructivist Learnings in the 1980s – Following Piage’s (1929), Brunner’s (1996) and Jonassen’s (1999) educational philosophies, constructivists proffer that knowledge acquisition develops through interactions with the environment. (p. 85). The computer was no longer a conduit for the presentation of information: it was a tool for the active manipulation of that information” (Naismith, Lonsdale, Vavoula, & Sharples, 2004, p. 12)Constructionist Learning in the 1980s – Constructionism differed from constructivism as Papert (1980) posited an additional component to constructivism: students learned best when they were actively involved in constructing social objects. The tutee position. Teaching the computer to perform tasks.Problem-Based learning in the 1990s – In the PBL, students often worked in small groups of five or six to pool knowledge and resources to solve problems. Launched the sociocultural revolution, focusing on learning in out of school contexts and the acquisition of knowledge through social interaction
    • Socio-Constructivist Learning in the 1990s. SCL believe that social and individual processes are independent in the co-construction of knowledge (Sullivan-Palinscar, 1998; Vygotsky, 1978).
    • 96-97). Keegan (2002) believed that e-learning was distance learning, which has been converted to e-learning through the use of technologies such as the WWW. Which electronic media and tools constituted e-learning: e.g., did it matter if the learning took place through a networked technology, or was it simply learning with an electronic device?
  • Discussion
    • Share with us practical examples of applying constructivist approach in your class
    • Would one hour workshop on turning existing class assignments into constructivist-based class assignments be of interest for you?

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https://blog.stcloudstate.edu/ims/2018/02/12/first-ims-podcast-on-technology-in-education/

Blockchain, Money and Empathy

On Blockchain, Money and Empathy: EdSurge Talks Trends and 2018 Predictions

By Jeffrey R. Young     Jan 30, 2018

EdSurge’s CEO, Betsy Corcoran, argued that 2017 was a year when educators and schools were trying to take control of their technology choices “We have said from the time we started writing the newsletters that not every piece of technology will work for every student, or for every school or every classroom,” she said. “It’s all about asking the right questions to figure out if there is a piece of technology that will support learning goals. What we’re starting to really see across schools, districts and teachers, people really owning those questions. They’re saying, ‘What do I want to do with my classroom? With my kids? And what are the technologies that will support me?’”

Another discussion participant asked whether colleges and universities are starting to accept cryptocurrencies like Bitcoin, or experimenting with the blockchain technology that drives those systems. Johnson said most of the hype around unversities’ blockchain experiments has centered on storing and managing credentials.

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more on blockchain and education in this IMS blog
https://blog.stcloudstate.edu/ims?s=blockchain+education

IRDL proposal

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

 

  • 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

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

Power, D. J. (2014). Using ‘Big Data’ for analytics and decision support. Journal of Decision Systems, 23(2), 222–228. https://doi.org/10.1080/12460125.2014.888848

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

Reilly, S. (2013, December 12). What does Horizon 2020 mean for research libraries? Retrieved November 7, 2017, from http://libereurope.eu/blog/2013/12/12/what-does-horizon-2020-mean-for-research-libraries/

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

Weiss, A. (2018). Big-Data-Shocks-An-Introduction-to-Big-Data-for-Librarians-and-Information-Professionals. Rowman & Littlefield Publishers. Retrieved from https://rowman.com/ISBN/9781538103227/Big-Data-Shocks-An-Introduction-to-Big-Data-for-Librarians-and-Information-Professionals

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

 

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more on big data





pedagogically sound Minecraft examples

FridayLive!! Oct 27 THIS WEEK 2:00 PM EDT 

Minecraft for Higher Ed? Try it. Pros, Cons, Recommendations? 

Description: Why Minecraft, the online video game? How can Minecraft improve learning for higher education?
We’ll begin with a live demo in which all can participate (see “Minecraft for Free”).
We’ll review “Examples, Not Rumors” of successful adaptations and USES of Minecraft for teaching/learning in higher education. Especially those submitted in advance
And we’ll try to extract from these activities a few recommendations/questions/requests re Minecraft in higher education.

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Examples:

Minecraft Education Edition: https://education.minecraft.net/
(more info: https://blog.stcloudstate.edu/ims/2017/05/23/minecraft-education-edition/)

K12: 

Minecraft empathy skillshttp://www.gettingsmart.com/wp-content/uploads/2017/04/How-Minecraft-Supports-SEL.pdf 

coding w MineCraft

Minecraft for Math

Higher Ed: 

Minecraft Higher Education?

Using MCEE in Higher Education

Why NOT to use minecraft in education:

https://higheredrevolution.com/why-educators-probably-shouldn-t-use-minecraft-in-their-classrooms-989f525c6e62

College Students Get Virtual Look at the Real World with ‘Minecraft’

Carnegie Mellon University uses the game-based learning tool to help students demonstrate engineering skills. SEP182017

https://edtechmagazine.com/higher/article/2017/09/college-students-get-virtual-look-real-world-minecraft

Using Minecraft in Higher Education

https://groups.google.com/forum/#!topic/minecraft-teachers/cED6MM0E0bQ

Using MinecraftEdu – Part 1 – Introduction

https://www.youtube.com/watch?v=Lsfd9J5UgVk

Physics with Minecraft example

Chemistry with Minecraft example

Biology

other disciplines

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Does learning really happen w Minecraft?

Callaghan, N. (2016). Investigating the role of Minecraft in educational learning environments. Educational Media International53(4), 244-260. doi:10.1080/09523987.2016.1254877

http://login.libproxy.stcloudstate.edu/login?qurl=http%3a%2f%2fsearch.ebscohost.com%2flogin.aspx%3fdirect%3dtrue%26db%3dkeh%26AN%3d119571817%26site%3dehost-live%26scope%3dsite

Noelene Callaghan dissects the evolution in Australian education from a global perspective. She rightfully draws attention (p. 245) to inevitable changes in the educational world, which still remain ignored: e.g., the demise of “traditional” LMS (Educase is calling for their replacement with digital learning environments https://blog.stcloudstate.edu/ims/2017/07/06/next-gen-digital-learning-environment/ and so does the corporate world of learning: https://blog.stcloudstate.edu/ims/2017/03/28/digital-learning/ ), the inevitability of BYOD (mainly by the “budget restrictions and sustainability challenges” (p. 245); by the assertion of cloud computing, and, last but not least, by the gamification of education.

p. 245 literature review. In my paper, I am offering more comprehensive literature review. While Callaghan focuses on the positive, my attempt is to list both pros and cons: http://scsu.mn/1F008Re

 

  1. 246 General use of massive multiplayer online role playing games (MMORPGs)

levels of interaction have grown dramatically and have led to the creation of general use of massive multiplayer online role playing games (MMORPGs)

  1. 247 In teaching and learning environments, affordances associated with edugames within a project-based learning (PBL) environment permit:
  • (1)  Learner-centered environments
  • (2)  Collaboration
  • (3)  Curricular content
  • (4)  Authentic tasks
  • (5)  Multiple expression modes
  • (6)  Emphasis on time management
  • (7)  Innovative assessment (Han & Bhattacharya, 2001).

These affordances develop both social and cognitive abilities of students

 

Nebel, S., Schneider, S., Beege, M., Kolda, F., Mackiewicz, V., & Rey, G. (2017). You cannot do this alone! Increasing task interdependence in cooperative educational videogames to encourage collaboration. Educational Technology Research & Development65(4), 993-1014. doi:10.1007/s11423-017-9511-8

http://login.libproxy.stcloudstate.edu/login?qurl=http%3a%2f%2fsearch.ebscohost.com%2flogin.aspx%3fdirect%3dtrue%26db%3dkeh%26AN%3d124132216%26site%3dehost-live%26scope%3dsite

Abrams, S. S., & Rowsell, J. (2017). Emotionally Crafted Experiences: Layering Literacies in Minecraft. Reading Teacher70(4), 501-506.

Nebel, S., Schneider, S., & Daniel Rey, G. (2016). Mining Learning and Crafting Scientific Experiments: A Literature Review on the Use of Minecraft in Education and Research. Source: Journal of Educational Technology & Society, 19(192), 355–366. Retrieved from http://www.jstor.org/stable/jeductechsoci.19.2.355

Cipollone, M., Schifter, C. C., & Moffat, R. A. (2014). Minecraft as a Creative Tool: A Case Study. International Journal Of Game-Based Learning4(2), 1-14.

http://login.libproxy.stcloudstate.edu/login?qurl=http%3a%2f%2fsearch.ebscohost.com%2flogin.aspx%3fdirect%3dtrue%26db%3deric%26AN%3dEJ1111251%26site%3dehost-live%26scope%3dsite

Niemeyer, D. J., & Gerber, H. R. (2015). Maker culture and Minecraft : implications for the future of learning. Educational Media International52(3), 216-226. doi:10.1080/09523987.2015.1075103

http://login.libproxy.stcloudstate.edu/login?qurl=http%3a%2f%2fsearch.ebscohost.com%2flogin.aspx%3fdirect%3dtrue%26db%3dkeh%26AN%3d111240626%26site%3dehost-live%26scope%3dsite

Nebel, S., Schneider, S., & Daniel Rey, G. (2016). Mining Learning and Crafting Scientific Experiments: A Literature Review on the Use of Minecraft in Education and Research. Journal of Educational Technology & Society, 19(192), 355–366. Retrieved from http://www.jstor.org/stable/jeductechsoci.19.2.355

 

Wilkinson, B., Williams, N., & Armstrong, P. (2013). Improving Student Understanding, Application and Synthesis of Computer Programming Concepts with Minecraft. In The European Conference on Technology in the Classroom 2013. Retrieved from http://iafor.info/archives/offprints/ectc2013-offprints/ECTC2013_0477.pdf

Berg Marklund, B., & Alklind Taylor, A.-S. (2015). Teachers’ Many Roles in Game-Based Learning Projects. In Academic Conferences International Limited (pp. 359–367). Retrieved from https://search.proquest.com/openview/15e084a1c52fdda188c27b9d2de6d361/1?pq-origsite=gscholar&cbl=396495

Uusi-Mäkelä, M., & Uusi-Mäkelä, M. (2014). Immersive Language Learning with Games: Finding Flow in MinecraftEdu. EdMedia: World Conference on Educational Media and Technology (Vol. 2014). Association for the Advancement of Computing in Education (AACE). Retrieved from https://www.learntechlib.org/noaccess/148409/

Birt, J., & Hovorka, D. (2014). Effect of mixed media visualization on learner perceptions and outcomes. In 25th Australasian Conference on Information Systems (pp. 1–10). Retrieved from http://epublications.bond.edu.au/fsd_papers/74

Al Washmi, R., Bana, J., Knight, I., Benson, E., Afolabi, O., Kerr, A., Hopkins, G. (2014). Design of a Math Learning Game Using a Minecraft Mod. https://doi.org/10.13140/2.1.4660.4809
https://www.researchgate.net/publication/267135810_Design_of_a_Math_Learning_Game_Using_a_Minecraft_Mod
https://docs.google.com/document/d/1uch2iC_CGsESdF9lpATGwWkamNbqQ7JOYEu_D-V03LQ/edit?usp=sharing

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more on Minecraft in this IMS blog
https://blog.stcloudstate.edu/ims?s=minecraft

interactivity for the library

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):

Both applications fit perfect the idea of interactivity in teaching (and learning) – https://blog.stcloudstate.edu/ims?s=interactivity

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.

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