GOTTACATCHEMALL:EXPLORING POKEMON GO IN SEARCH OF LEARNING ENHANCEMENT OBJECTS
Annamaria Cacchione, Emma Procter-Legg and Sobah Abbas Petersen
Universidad Complutense de Madrid, Facultad de Filologia, Av.da Complutense sn, 28040 Madrid, Spain Independent; Abingdon, Oxon, UK SINTEF Technology and Society, Trondheim, Norway
The Augmented Reality Game, Pokemon Go, took the world by storm in the summer of 2016. City landscapes were decorated with amusing, colourful objects called Pokemon, and the holiday activities were enhanced by catching these wonderful creatures. In light of this, it is inevitable for mobile language learning researchers to reflect on the impact oft his game on learning and how it may be leveraged to enhance the design of mobile and ubiquitous technologies for mobile and situated language learning. This paper analyses the game Pokemon Go and the players’ experiences accordingto a framework developed for evaluating mobile language learning and discusses how Pokemon Go can help to meetsome of the challenges faced by earlier research activities.
A comparison between PG and Geocashing will illustrate the evolution of the concept of location-based games a concept that is very close to that of situated learning that we have explored in several previous works.
Pokémon Go is a free, location-based augmented reality game developed for mobile devices. Players useGPS on their mobile device to locate, capture, battle, and train virtual creatures (a.k.a. Pokémon), whichappear on screen overlaying the image seen through the device’s camera. This makes it seem like thePokemon are in the same real-world location as the player
“Put simply, augmented reality is a technology that overlays computer generated visuals over the real worldthrough a device camera bringing your surroundings to life and interacting with sensors such as location and heart rate to provide additional information”(Ramirez, 2014).
Apply the evaluation framework developed in 2015 for mobile learning applications(Cacchione, Procter-Legg, Petersen, & Winter, 2015). The framework is composed of a set offactors of different nature neuroscientific, technological, organisational and pedagogical and aim toprovide a comprehensive account of what plays a major role in ensuring effective learning via mobile devices
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:
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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:
Guardtime – This company is creating “keyless” signature systems using blockchain which is currently used to secure the health records of one million Estonian citizens.
REMME is a decentralized authentication system which aims to replace logins and passwords with SSL certificates stored on a blockchain.
Healthcare
Gem – This startup is working with the Centre for Disease Control to put disease outbreak data onto a blockchain which it says will increase the effectiveness of disaster relief and response.
SimplyVital Health – Has two health-related blockchain products in development, ConnectingCare which tracks the progress of patients after they leave the hospital, and Health Nexus, which aims to provide decentralized blockchain patient records.
MedRec – An MIT project involving blockchain electronic medical records designed to manage authentication, confidentiality and data sharing.
Financial services
ABRA – A cryptocurrency wallet which uses the Bitcoin blockchain to hold and track balances stored in different currencies.
Bank Hapoalim – A collaboration between the Israeli bank and Microsoft to create a blockchain system for managing bank guarantees.
Barclays – Barclays has launched a number of blockchain initiatives involving tracking financial transactions, compliance and combating fraud. It states that “Our belief …is that blockchain is a fundamental part of the new operating system for the planet.”
Maersk – The shipping and transport consortium has unveiled plans for a blockchain solution for streamlining marine insurance.
Aeternity – Allows the creation of smart contracts which become active when network consensus agrees that conditions have been met – allowing for automated payments to be made when parties agree that conditions have been met, for example.
Augur – Allows the creation of blockchain-based predictions markets for the trading of derivatives and other financial instruments in a decentralized ecosystem.
Manufacturing and industrial
Provenance – This project aims to provide a blockchain-based provenance record of transparency within supply chains.
Jiocoin – India’s biggest conglomerate, Reliance Industries, has said that it is developing a blockchain-based supply chain logistics platform along with its own cryptocurrency, Jiocoin.
Hijro – Previously known as Fluent, aims to create a blockchain framework for collaborating on prototyping and proof-of-concept.
SKUChain – Another blockchain system for allowing tracking and tracing of goods as they pass through a supply chain.
Blockverify – A blockchain platform which focuses on anti-counterfeit measures, with initial use cases in the diamond, pharmaceuticals and luxury goods markets.
Transactivgrid – A business-led community project based in Brooklyn allowing members to locally produce and cell energy, with the goal of reducing costs involved in energy distribution.
STORJ.io – Distributed and encrypted cloud storage, which allows users to share unused hard drive space.
Government
Dubai – Dubai has set sights on becoming the world’s first blockchain-powered state. In 2016 representatives of 30 government departments formed a committee dedicated to investigating opportunities across health records, shipping, business registration and preventing the spread of conflict diamonds.
Estonia – The Estonian government has partnered with Ericsson on an initiative involving creating a new data center to move public records onto the blockchain. 20
South Korea – Samsung is creating blockchain solutions for the South Korean government which will be put to use in public safety and transport applications.
Govcoin – The UK Department of Work and Pensions is investigating using blockchain technology to record and administer benefit payments.
Democracy.earth – This is an open-source project aiming to enable the creation of democratically structured organizations, and potentially even states or nations, using blockchain tools.
Followmyvote.com – Allows the creation of secure, transparent voting systems, reducing opportunities for voter fraud and increasing turnout through improved accessibility to democracy.
Charity
Bitgive – This service aims to provide greater transparency to charity donations and clearer links between giving and project outcomes. It is working with established charities including Save The Children, The Water Project and Medic Mobile.
Retail
OpenBazaar – OpenBazaar is an attempt to build a decentralized market where goods and services can be traded with no middle-man.
Loyyal – This is a blockchain-based universal loyalty framework, which aims to allow consumers to combine and trade loyalty rewards in new ways, and retailers to offer more sophisticated loyalty packages.
Blockpoint.io – Allows retailers to build payment systems around blockchain currencies such as Bitcoin, as well as blockchain derived gift cards and loyalty schemes.
Real Estate
Ubiquity – This startup is creating a blockchain-driven system for tracking the complicated legal process which creates friction and expense in real estate transfer.
Transport and Tourism
IBM Blockchain Solutions – IBM has said it will go public with a number of non-finance related blockchain initiatives with global partners in 2018. This video envisages how efficiencies could be driven in the vehicle leasing industry.
Arcade City – An application which aims to beat Uber at their own game by moving ride sharing and car hiring onto the blockchain.
La’Zooz – A community-owned platform for synchronizing empty seats with passengers in need of a lift in real-time.
Webjet – The online travel portal is developing a blockchain solution to allow stock of empty hotel rooms to be efficiently tracked and traded, with payment fairly routed to the network of middle-men sites involved in filling last-minute vacancies.
Media
Kodak – Kodak recently sent its stock soaring after announcing that it is developing a blockchain system for tracking intellectual property rights and payments to photographers.
Ujomusic – Founded by singer-songwriter Imogen Heap to record and track royalties for musicians, as well as allowing them to create a record of ownership of their work.
It is exciting to see all these developments. I am sure not all of these will make it into successful long-term ventures but if they indicate one thing, then it is the vast potential the blockchain technology is offering.
1) Google Speech API – best speech technology, recently announced to be available for commercial use. Currently in beta status. Google also has separate APIs for Android OS and Javascript API for Chrome.
8) CMU Sphinx – Speech Recognition Toolkit – offline speech recognition, due to low resource requirements can be used on mobile. OpenEars – Pocketsphinx on iOS, there are also APIs for Node.js, Ruby, Java, Android bindings.
The Federal Communications Commission released a plan on Tuesday to dismantle landmark regulations that ensure equal access to the internet, clearing the way for internet service companies to charge users more to see certain content and to curb access to some websites.
The proposal, made by the F.C.C. chairman, Ajit Pai, is a sweeping repeal of rules put in place by the Obama administration. The rules prohibit high-speed internet service providers, or I.S.P.s, from stopping or slowing down the delivery of websites. They also prevent the companies from charging customers extra fees for high-quality streaming and other services.
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FCC chairman defends net neutrality repeal plan
An analysis of the millions of comments conducted by the data company Gravwell in October found that just 17.4 percent of the comments to the FCC on the net neutrality rules came from real people.
Because many internet services for mobile devices include limits on data use, the changes will be visible there first. In one dramatic scenario, internet services would begin to resemble cable-TV packages, where subscriptions could be limited to a few dozen sites and services. Or, for big spenders, a few hundred. Fortunately, that’s not a likely scenario. Instead, expect a gradual shift towards subscriptions that provide unlimited access to certain preferred providers while charging extra for everything else.
Even Verizon’s “unlimited” plans impose limits. The company’s cheapest unlimited mobile plan limits video streaming quality to 480p resolution, which is DVD quality, on phones and 720p resolution, the lower tier of HD quality, on tablets. Customers can upgrade to a more expensive plan that enables 720p resolution on phones and 1080p on tablets, but the higher quality 4K video standard is effectively forbidden.
Meanwhile, Comcast customers in 28 states face 1 terabyte data caps. Going over that limit costs subscribers as much as an additional $50 a month. As 4K televisions become more common, more households may hit the limit. That could prompt some to stick with a traditional pay-TV package from Comcast.
Republican FCC Chair Ajit Pai argues that Federal Trade Commission will be able to protect consumers and small business from abuses by internet providers once the agency’s current rules are off the books. But that’s not clear.
The good news is the internet won’t change overnight, if it all. Blake Reid, a clinical professor at Colorado Law, says the big broadband providers will wait to see how the inevitable legal challenges to the new FCC order shakeout. They’ll probably keep an eye on 2018 and even 2020 elections as well.
2018 Special Focus: Education in a Time of Austerity and Social Turbulence 21–23 June 2018 University of Athens, Athens, Greece http://thelearner.com/2018-conference
Theme 8: Technologies in Learning
Technology and human values: learning through and about technology
Crossing the digital divide: access to learning in, and about, the digital world
New tools for learning: online digitally mediated learning
Virtual worlds, virtual classrooms: interactive, self-paced and autonomous learning
Ubiquitous learning: using the affordances of the new mediaDistance learning: reducing the distance
Theme 9: Literacies Learning
Defining new literacies
Languages of power: literacy’s role in social access
Instructional responses to individual differences in literacy learning
The visual and the verbal: Multiliteracies and multimodal communications
Literacy in learning: language in learning across the subject areas
The changing role of libraries in literacies learning
Languages education and second language learning
Multilingual learning for a multicultural world
The arts and design in multimodal learning
The computer, internet, and digital media: educational challenges and responses
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PROPOSAL: Paper presentation in a Themed Session
Title
Virtual Reality and Gamification in the Educational Process: The Experience from an Academic Library
short description
VR, AR and Mixed Reality, as well as gaming and gamification are proposed as sandbox opportunity to transition from a lecture-type instruction to constructivist-based methods.
long description
The NMC New Horizon Report 2017 predicts a rapid application of Video360 in K12. Millennials are leaving college, Gen Z students are our next patrons. Higher Education needs to meet its new students on “their playground.” A collaboration by a librarian and VR specialist is testing the opportunities to apply 360 degree movies and VR in academic library orientation. The team seeks to bank on the inheriting interest of young patrons toward these technologies and their inextricable part of a rapidly becoming traditional gaming environment. A “low-end,” inexpensive and more mobile Google Cardboard solution was preferred to HTC Vive, Microsoft HoloLens or comparable hi-end VR, AR and mixed reality products.
The team relies on the constructivist theory of assisting students in building their knowledge in their own pace and on their own terms, rather than being lectured and/or being guided by a librarian during a traditional library orientation tour. Using inexpensive Google Cardboard goggles, students can explore a realistic set up of the actual library and familiarize themselves with its services. Students were polled on the effectiveness of such approach as well as on their inclination to entertain more comprehensive version of library orientation. Based on the lessons from this experiment, the team intends to pursue also a standardized approach to introducing VR to other campus services, thus bringing down further the cost of VR projects on campus. The project is considered a sandbox for academic instruction across campus. The same concept can be applied for [e.g., Chemistry, Physics, Biology) lab tours; for classes, which anticipate preliminary orientation process.
Following the VR orientation, the traditional students’ library instruction, usually conducted in a room, is replaced by a dynamic gamified library instruction. Students are split in groups of three and conduct a “scavenger hunt”; students use a jQuery-generated Web site on their mobile devices to advance through “hoops” of standard information literacy test. E.g., they need to walk to the Reference Desk, collect specific information and log their findings in the Web site. The idea follows the strong interest in the educational world toward gaming and gamification of the educational process. This library orientation approach applies the three principles for gamification: empowers learners; teaches problem solving and increases understanding.
Similarly to the experience with VR for library orientation, this library instruction process is used as a sandbox and has been successfully replicated by other instructors in their classes.
Media literacy. Differentiated instruction. Media literacy guide.
Fake news as part of media literacy. Visual literacy as part of media literacy. Media literacy as part of digital citizenship.
Web design / web development
the roles of HTML5, CSS, Java Script, PHP, Bootstrap, JQuery, React and other scripting languages and libraries. Heat maps and other usability issues; website content strategy. THE MODEL-VIEW-CONTROLLER (MVC) design pattern
Social media for institutional use. Digital Curation. Social Media algorithms. Etiquette Ethics. Mastodon
I hosted a LITA webinar in the fall of 2016 (four weeks); I can accommodate any information from that webinar for the use of the IM students
OER and instructional designer’s assistance to book creators.
I can cover both the “library part” (“free” OER, copyright issues etc) and the support / creative part of an OER book / textbook
“Big Data.” Data visualization. Large scale visualization. Text encoding. Analytics, Data mining. Unizin. Python, R in academia.
I can introduce the students to the large idea of Big Data and its importance in lieu of the upcoming IoT, but also departmentalize its importance for academia, business, etc. From infographics to heavy duty visualization (Primo X-Services API. JSON, Flask).
NetNeutrality, Digital Darwinism, Internet economy and the role of your professional in such environment
I can introduce students to the issues, if not familiar and / or lead a discussion on a rather controversial topic
Digital assessment. Digital Assessment literacy.
I can introduce students to tools, how to evaluate and select tools and their pedagogical implications
Wikipedia
a hands-on exercise on working with Wikipedia. After the session, students will be able to create Wikipedia entries thus knowing intimately the process of Wikipedia and its information.
Effective presentations. Tools, methods, concepts and theories (cognitive load). Presentations in the era of VR, AR and mixed reality. Unity.
I can facilitate a discussion among experts (your students) on selection of tools and their didactically sound use to convey information. I can supplement the discussion with my own findings and conclusions.
eConferencing. Tools and methods
I can facilitate a discussion among your students on selection of tools and comparison. Discussion about the their future and their place in an increasing online learning environment
Digital Storytelling. Immersive Storytelling. The Moth. Twine. Transmedia Storytelling
I am teaching a LIB 490/590 Digital Storytelling class. I can adapt any information from that class to the use of IM students
VR, AR, Mixed Reality.
besides Mark Gill, I can facilitate a discussion, which goes beyond hardware and brands, but expand on the implications for academia and corporate education / world
Instructional design. ID2ID
I can facilitate a discussion based on the Educause suggestions about the profession’s development
Microcredentialing in academia and corporate world. Blockchain
IT in K12. How to evaluate; prioritize; select. obsolete trends in 21 century schools. K12 mobile learning
Podcasting: past, present, future. Beautiful Audio Editor.
a definition of podcasting and delineation of similar activities; advantages and disadvantages.
Gender, race and age in education. Digital divide. Xennials, Millennials and Gen Z. generational approach to teaching and learning. Young vs old Millennials. Millennial employees.
Applications for the 2018 Institute will be accepted between December 1, 2017 and January 27, 2018. Scholars accepted to the program will be notified in early March 2018.
Title:
Learning to Harness Big Data in an Academic Library
Abstract (200)
Research on Big Data per se, as well as on the importance and organization of the process of Big Data collection and analysis, is well underway. The complexity of the process comprising “Big Data,” however, deprives organizations of ubiquitous “blue print.” The planning, structuring, administration and execution of the process of adopting Big Data in an organization, being that a corporate one or an educational one, remains an elusive one. No less elusive is the adoption of the Big Data practices among libraries themselves. Seeking the commonalities and differences in the adoption of Big Data practices among libraries may be a suitable start to help libraries transition to the adoption of Big Data and restructuring organizational and daily activities based on Big Data decisions. Introduction to the problem. Limitations
The redefinition of humanities scholarship has received major attention in higher education. The advent of digital humanities challenges aspects of academic librarianship. Data literacy is a critical need for digital humanities in academia. The March 2016 Library Juice Academy Webinar led by John Russel exemplifies the efforts to help librarians become versed in obtaining programming skills, and respectively, handling data. Those are first steps on a rather long path of building a robust infrastructure to collect, analyze, and interpret data intelligently, so it can be utilized to restructure daily and strategic activities. Since the phenomenon of Big Data is young, there is a lack of blueprints on the organization of such infrastructure. A collection and sharing of best practices is an efficient approach to establishing a feasible plan for setting a library infrastructure for collection, analysis, and implementation of Big Data.
Limitations. This research can only organize the results from the responses of librarians and research into how libraries present themselves to the world in this arena. It may be able to make some rudimentary recommendations. However, based on each library’s specific goals and tasks, further research and work will be needed.
Big Data is becoming an omnipresent term. It is widespread among different disciplines in academia (De Mauro, Greco, & Grimaldi, 2016). This leads to “inconsistency in meanings and necessity for formal definitions” (De Mauro et al, 2016, p. 122). Similarly, to De Mauro et al (2016), Hashem, Yaqoob, Anuar, Mokhtar, Gani and Ullah Khan (2015) seek standardization of definitions. The main connected “themes” of this phenomenon must be identified and the connections to Library Science must be sought. A prerequisite for a comprehensive definition is the identification of Big Data methods. Bughin, Chui, Manyika (2011), Chen et al. (2012) and De Mauro et al (2015) single out the methods to complete the process of building a comprehensive definition.
In conjunction with identifying the methods, volume, velocity, and variety, as defined by Laney (2001), are the three properties of Big Data accepted across the literature. Daniel (2015) defines three stages in big data: collection, analysis, and visualization. According to Daniel, (2015), Big Data in higher education “connotes the interpretation of a wide range of administrative and operational data” (p. 910) and according to Hilbert (2013), as cited in Daniel (2015), Big Data “delivers a cost-effective prospect to improve decision making” (p. 911).
The importance of understanding the process of Big Data analytics is well understood in academic libraries. An example of such “administrative and operational” use for cost-effective improvement of decision making are the Finch & Flenner (2016) and Eaton (2017) case studies of the use of data visualization to assess an academic library collection and restructure the acquisition process. Sugimoto, Ding & Thelwall (2012) call for the discussion of Big Data for libraries. According to the 2017 NMC Horizon Report “Big Data has become a major focus of academic and research libraries due to the rapid evolution of data mining technologies and the proliferation of data sources like mobile devices and social media” (Adams, Becker, et al., 2017, p. 38).
Power (2014) elaborates on the complexity of Big Data in regard to decision-making and offers ideas for organizations on building a system to deal with Big Data. As explained by Boyd and Crawford (2012) and cited in De Mauro et al (2016), there is a danger of a new digital divide among organizations with different access and ability to process data. Moreover, Big Data impacts current organizational entities in their ability to reconsider their structure and organization. The complexity of institutions’ performance under the impact of Big Data is further complicated by the change of human behavior, because, arguably, Big Data affects human behavior itself (Schroeder, 2014).
De Mauro et al (2015) touch on the impact of Dig Data on libraries. The reorganization of academic libraries considering Big Data and the handling of Big Data by libraries is in a close conjunction with the reorganization of the entire campus and the handling of Big Data by the educational institution. In additional to the disruption posed by the Big Data phenomenon, higher education is facing global changes of economic, technological, social, and educational character. Daniel (2015) uses a chart to illustrate the complexity of these global trends. Parallel to the Big Data developments in America and Asia, the European Union is offering access to an EU open data portal (https://data.europa.eu/euodp/home ). Moreover, the Association of European Research Libraries expects under the H2020 program to increase “the digitization of cultural heritage, digital preservation, research data sharing, open access policies and the interoperability of research infrastructures” (Reilly, 2013).
The challenges posed by Big Data to human and social behavior (Schroeder, 2014) are no less significant to the impact of Big Data on learning. Cohen, Dolan, Dunlap, Hellerstein, & Welton (2009) propose a road map for “more conservative organizations” (p. 1492) to overcome their reservations and/or inability to handle Big Data and adopt a practical approach to the complexity of Big Data. Two Chinese researchers assert deep learning as the “set of machine learning techniques that learn multiple levels of representation in deep architectures (Chen & Lin, 2014, p. 515). Deep learning requires “new ways of thinking and transformative solutions (Chen & Lin, 2014, p. 523). Another pair of researchers from China present a broad overview of the various societal, business and administrative applications of Big Data, including a detailed account and definitions of the processes and tools accompanying Big Data analytics. The American counterparts of these Chinese researchers are of the same opinion when it comes to “think about the core principles and concepts that underline the techniques, and also the systematic thinking” (Provost and Fawcett, 2013, p. 58). De Mauro, Greco, and Grimaldi (2016), similarly to Provost and Fawcett (2013) draw attention to the urgent necessity to train new types of specialists to work with such data. As early as 2012, Davenport and Patil (2012), as cited in Mauro et al (2016), envisioned hybrid specialists able to manage both technological knowledge and academic research. Similarly, Provost and Fawcett (2013) mention the efforts of “academic institutions scrambling to put together programs to train data scientists” (p. 51). Further, Asomoah, Sharda, Zadeh & Kalgotra (2017) share a specific plan on the design and delivery of a big data analytics course. At the same time, librarians working with data acknowledge the shortcomings in the profession, since librarians “are practitioners first and generally do not view usability as a primary job responsibility, usually lack the depth of research skills needed to carry out a fully valid” data-based research (Emanuel, 2013, p. 207).
Borgman (2015) devotes an entire book to data and scholarly research and goes beyond the already well-established facts regarding the importance of Big Data, the implications of Big Data and the technical, societal, and educational impact and complications posed by Big Data. Borgman elucidates the importance of knowledge infrastructure and the necessity to understand the importance and complexity of building such infrastructure, in order to be able to take advantage of Big Data. In a similar fashion, a team of Chinese scholars draws attention to the complexity of data mining and Big Data and the necessity to approach the issue in an organized fashion (Wu, Xhu, Wu, Ding, 2014).
Bruns (2013) shifts the conversation from the “macro” architecture of Big Data, as focused by Borgman (2015) and Wu et al (2014) and ponders over the influx and unprecedented opportunities for humanities in academia with the advent of Big Data. Does the seemingly ubiquitous omnipresence of Big Data mean for humanities a “railroading” into “scientificity”? How will research and publishing change with the advent of Big Data across academic disciplines?
Reyes (2015) shares her “skinny” approach to Big Data in education. She presents a comprehensive structure for educational institutions to shift “traditional” analytics to “learner-centered” analytics (p. 75) and identifies the participants in the Big Data process in the organization. The model is applicable for library use.
Being a new and unchartered territory, Big Data and Big Data analytics can pose ethical issues. Willis (2013) focusses on Big Data application in education, namely the ethical questions for higher education administrators and the expectations of Big Data analytics to predict students’ success. Daries, Reich, Waldo, Young, and Whittinghill (2014) discuss rather similar issues regarding the balance between data and student privacy regulations. The privacy issues accompanying data are also discussed by Tene and Polonetsky, (2013).
Privacy issues are habitually connected to security and surveillance issues. Andrejevic and Gates (2014) point out in a decision making “generated by data mining, the focus is not on particular individuals but on aggregate outcomes” (p. 195). Van Dijck (2014) goes into further details regarding the perils posed by metadata and data to the society, in particular to the privacy of citizens. Bail (2014) addresses the same issue regarding the impact of Big Data on societal issues, but underlines the leading roles of cultural sociologists and their theories for the correct application of Big Data.
Library organizations have been traditional proponents of core democratic values such as protection of privacy and elucidation of related ethical questions (Miltenoff & Hauptman, 2005). In recent books about Big Data and libraries, ethical issues are important part of the discussion (Weiss, 2018). Library blogs also discuss these issues (Harper & Oltmann, 2017). An academic library’s role is to educate its patrons about those values. Sugimoto et al (2012) reflect on the need for discussion about Big Data in Library and Information Science. They clearly draw attention to the library “tradition of organizing, managing, retrieving, collecting, describing, and preserving information” (p.1) as well as library and information science being “a historically interdisciplinary and collaborative field, absorbing the knowledge of multiple domains and bringing the tools, techniques, and theories” (p. 1). Sugimoto et al (2012) sought a wide discussion among the library profession regarding the implications of Big Data on the profession, no differently from the activities in other fields (e.g., Wixom, Ariyachandra, Douglas, Goul, Gupta, Iyer, Kulkami, Mooney, Phillips-Wren, Turetken, 2014). A current Andrew Mellon Foundation grant for Visualizing Digital Scholarship in Libraries seeks an opportunity to view “both macro and micro perspectives, multi-user collaboration and real-time data interaction, and a limitless number of visualization possibilities – critical capabilities for rapidly understanding today’s large data sets (Hwangbo, 2014).
The importance of the library with its traditional roles, as described by Sugimoto et al (2012) may continue, considering the Big Data platform proposed by Wu, Wu, Khabsa, Williams, Chen, Huang, Tuarob, Choudhury, Ororbia, Mitra, & Giles (2014). Such platforms will continue to emerge and be improved, with librarians as the ultimate drivers of such platforms and as the mediators between the patrons and the data generated by such platforms.
Every library needs to find its place in the large organization and in society in regard to this very new and very powerful phenomenon called Big Data. Libraries might not have the trained staff to become a leader in the process of organizing and building the complex mechanism of this new knowledge architecture, but librarians must educate and train themselves to be worthy participants in this new establishment.
Method
The study will be cleared by the SCSU IRB.
The survey will collect responses from library population and it readiness to use and use of Big Data. Send survey URL to (academic?) libraries around the world.
Data will be processed through SPSS. Open ended results will be processed manually. The preliminary research design presupposes a mixed method approach.
The study will include the use of closed-ended survey response questions and open-ended questions. The first part of the study (close ended, quantitative questions) will be completed online through online survey. Participants will be asked to complete the survey using a link they receive through e-mail.
Mixed methods research was defined by Johnson and Onwuegbuzie (2004) as “the class of research where the researcher mixes or combines quantitative and qualitative research techniques, methods, approaches, concepts, or language into a single study” (Johnson & Onwuegbuzie, 2004 , p. 17). Quantitative and qualitative methods can be combined, if used to complement each other because the methods can measure different aspects of the research questions (Sale, Lohfeld, & Brazil, 2002).
Sampling design
Online survey of 10-15 question, with 3-5 demographic and the rest regarding the use of tools.
1-2 open-ended questions at the end of the survey to probe for follow-up mixed method approach (an opportunity for qualitative study)
data analysis techniques: survey results will be exported to SPSS and analyzed accordingly. The final survey design will determine the appropriate statistical approach.
Project Schedule
Complete literature review and identify areas of interest – two months
Prepare and test instrument (survey) – month
IRB and other details – month
Generate a list of potential libraries to distribute survey – month
Contact libraries. Follow up and contact again, if necessary (low turnaround) – month
Collect, analyze data – two months
Write out data findings – month
Complete manuscript – month
Proofreading and other details – month
Significance of the work
While it has been widely acknowledged that Big Data (and its handling) is changing higher education (https://blog.stcloudstate.edu/ims?s=big+data) as well as academic libraries (https://blog.stcloudstate.edu/ims/2016/03/29/analytics-in-education/), it remains nebulous how Big Data is handled in the academic library and, respectively, how it is related to the handling of Big Data on campus. Moreover, the visualization of Big Data between units on campus remains in progress, along with any policymaking based on the analysis of such data (hence the need for comprehensive visualization).
This research will aim to gain an understanding on: a. how librarians are handling Big Data; b. how are they relating their Big Data output to the campus output of Big Data and c. how librarians in particular and campus administration in general are tuning their practices based on the analysis.
Based on the survey returns (if there is a statistically significant return), this research might consider juxtaposing the practices from academic libraries, to practices from special libraries (especially corporate libraries), public and school libraries.
References:
Adams Becker, S., Cummins M, Davis, A., Freeman, A., Giesinger Hall, C., Ananthanarayanan, V., … Wolfson, N. (2017). NMC Horizon Report: 2017 Library Edition.
Andrejevic, M., & Gates, K. (2014). Big Data Surveillance: Introduction. Surveillance & Society, 12(2), 185–196.
Asamoah, D. A., Sharda, R., Hassan Zadeh, A., & Kalgotra, P. (2017). Preparing a Data Scientist: A Pedagogic Experience in Designing a Big Data Analytics Course. Decision Sciences Journal of Innovative Education, 15(2), 161–190. https://doi.org/10.1111/dsji.12125
Bughin, J., Chui, M., & Manyika, J. (2010). Clouds, big data, and smart assets: Ten tech-enabled business trends to watch. McKinsey Quarterly, 56(1), 75–86.
Cohen, J., Dolan, B., Dunlap, M., Hellerstein, J. M., & Welton, C. (2009). MAD Skills: New Analysis Practices for Big Data. Proc. VLDB Endow., 2(2), 1481–1492. https://doi.org/10.14778/1687553.1687576
Daniel, B. (2015). Big Data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904–920. https://doi.org/10.1111/bjet.12230
Daries, J. P., Reich, J., Waldo, J., Young, E. M., Whittinghill, J., Ho, A. D., … Chuang, I. (2014). Privacy, Anonymity, and Big Data in the Social Sciences. Commun. ACM, 57(9), 56–63. https://doi.org/10.1145/2643132
De Mauro, A. D., Greco, M., & Grimaldi, M. (2016). A formal definition of Big Data based on its essential features. Library Review, 65(3), 122–135. https://doi.org/10.1108/LR-06-2015-0061
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The EDUCAUSE Learning Initiative has just launched its 2018 Key Issues in Teaching and Learning Survey, so vote today: http://www.tinyurl.com/ki2018.
Each year, the ELI surveys the teaching and learning community in order to discover the key issues and themes in teaching and learning. These top issues provide the thematic foundation or basis for all of our conversations, courses, and publications for the coming year. Longitudinally they also provide the way to track the evolving discourse in the teaching and learning space. More information about this annual survey can be found at https://www.educause.edu/eli/initiatives/key-issues-in-teaching-and-learning.
ACADEMIC TRANSFORMATION (Holistic models supporting student success, leadership competencies for academic transformation, partnerships and collaborations across campus, IT transformation, academic transformation that is broad, strategic, and institutional in scope)
ACCESSIBILITY AND UNIVERSAL DESIGN FOR LEARNING (Supporting and educating the academic community in effective practice; intersections with instructional delivery modes; compliance issues)
ADAPTIVE TEACHING AND LEARNING (Digital courseware; adaptive technology; implications for course design and the instructor’s role; adaptive approaches that are not technology-based; integration with LMS; use of data to improve learner outcomes)
COMPETENCY-BASED EDUCATION AND NEW METHODS FOR THE ASSESSMENT OF STUDENT LEARNING (Developing collaborative cultures of assessment that bring together faculty, instructional designers, accreditation coordinators, and technical support personnel, real world experience credit)
DIGITAL AND INFORMATION LITERACIES (Student and faculty literacies; research skills; data discovery, management, and analysis skills; information visualization skills; partnerships for literacy programs; evaluation of student digital competencies; information evaluation)
EVALUATING TECHNOLOGY-BASED INSTRUCTIONAL INNOVATIONS (Tools and methods to gather data;data analysis techniques; qualitative vs. quantitative data; evaluation project design; using findings to change curricular practice; scholarship of teaching and learning; articulating results to stakeholders; just-in-time evaluation of innovations). here is my bibliographical overview on Big Data (scroll down to “Research literature”: https://blog.stcloudstate.edu/ims/2017/11/07/irdl-proposal/ )
EVOLUTION OF THE TEACHING AND LEARNING SUPPORT PROFESSION (Professional skills for T&L support; increasing emphasis on instructional design; delineating the skills, knowledge, business acumen, and political savvy for success; role of inter-institutional communities of practices and consortia; career-oriented professional development planning)
FACULTY DEVELOPMENT (Incentivizing faculty innovation; new roles for faculty and those who support them; evidence of impact on student learning/engagement of faculty development programs; faculty development intersections with learning analytics; engagement with student success)
GAMIFICATION OF LEARNING (Gamification designs for course activities; adaptive approaches to gamification; alternate reality games; simulations; technological implementation options for faculty)
INSTRUCTIONAL DESIGN (Skills and competencies for designers; integration of technology into the profession; role of data in design; evolution of the design profession (here previous blog postings on this issue: https://blog.stcloudstate.edu/ims/2017/10/04/instructional-design-3/); effective leadership and collaboration with faculty)
INTEGRATED PLANNING AND ADVISING FOR STUDENT SUCCESS (Change management and campus leadership; collaboration across units; integration of technology systems and data; dashboard design; data visualization (here previous blog postings on this issue: https://blog.stcloudstate.edu/ims?s=data+visualization); counseling and coaching advising transformation; student success analytics)
LEARNING ANALYTICS (Leveraging open data standards; privacy and ethics; both faculty and student facing reports; implementing; learning analytics to transform other services; course design implications)
LEARNING SPACE DESIGNS (Makerspaces; funding; faculty development; learning designs across disciplines; supporting integrated campus planning; ROI; accessibility/UDL; rating of classroom designs)
MICRO-CREDENTIALING AND DIGITAL BADGING (Design of badging hierarchies; stackable credentials; certificates; role of open standards; ways to publish digital badges; approaches to meta-data; implications for the transcript; Personalized learning transcripts and blockchain technology (here previous blog postings on this issue: https://blog.stcloudstate.edu/ims?s=blockchain)
MOBILE LEARNING (Curricular use of mobile devices (here previous blog postings on this issue:
MULTI-DIMENSIONAL TECHNOLOGIES (Virtual, augmented, mixed, and immersive reality; video walls; integration with learning spaces; scalability, affordability, and accessibility; use of mobile devices; multi-dimensional printing and artifact creation)
NEXT-GENERATION DIGITAL LEARNING ENVIRONMENTS AND LMS SERVICES (Open standards; learning environments architectures (here previous blog postings on this issue: https://blog.stcloudstate.edu/ims/2017/03/28/digital-learning/; social learning environments; customization and personalization; OER integration; intersections with learning modalities such as adaptive, online, etc.; LMS evaluation, integration and support)
ONLINE AND BLENDED TEACHING AND LEARNING (Flipped course models; leveraging MOOCs in online learning; course development models; intersections with analytics; humanization of online courses; student engagement)
OPEN EDUCATION (Resources, textbooks, content; quality and editorial issues; faculty development; intersections with student success/access; analytics; licensing; affordability; business models; accessibility and sustainability)
PRIVACY AND SECURITY (Formulation of policies on privacy and data protection; increased sharing of data via open standards for internal and external purposes; increased use of cloud-based and third party options; education of faculty, students, and administrators)
WORKING WITH EMERGING LEARNING TECHNOLOGY (Scalability and diffusion; effective piloting practices; investments; faculty development; funding; evaluation methods and rubrics; interoperability; data-driven decision-making)
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.
Multi-Modal Assessments – create multiple-choice and open-ended items that are distributed digitally and assessed automatically. Student responses can be viewed instantaneously and downloaded to a spreadsheet for later use.
Formative (http://www.goformative.com) allows professors to upload charts or graphic organizers that students can draw on with a stylus. Formative also allows professors to upload document “worksheets” which can then be augmented with multiple-choice and open-ended questions.
Nearpod (http://www.nearpod.com) allows professors to upload their digital presentations and create digital quizzes to accompany them. Nearpod also allows professors to share three-dimensional field trips and models to help communicate ideas.
Video-Based Assessments – Question-embedded videos are an outstanding way to improve student engagement in blended or flipped instructional contexts. Using these tools allows professors to identify if the videos they use or create are being viewed by students.
Playposit (http://www.playposit.com) are two leaders in this application category. A second type of video-based assessment allows professors to sustain discussion-board like conversation with brief videos.
Flipgrid (http://www.flipgrid.com), for example, allows professors to posit a video question to which students may respond with their own video responses.
Quizzing Assessments – ools that utilize close-ended questions that provide a quick check of student understanding are also available.
Kahoot (http://www.kahoot.com) are relatively quick and convenient to use as a wrap up to instruction or a review of concepts taught.
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.
