Open Discussion: Instruments and Methods for Formative Assessment: by invitation of teachers from Plovdiv region | Тема: Инструменти и методи за актуални училищни занятия
Where | Къде: СУ „Димитър Матевски“ https://goo.gl/maps/rojNjE3dk4s and online ( виртуално) When | Кога: 2. май, 2018, 14 часа | May 2, 2018, 2PM local time (Bulgaria) Who | Кой: преподаватели и педагози | teachers and faculty How | Как: използвайте “обратна връзка” за споделяне на вашите идеи | use the following hashtag for backchanneling#BGtechEd
Intro | Представяне – 5мин. Who are we (please share short intro about your professional interests) | Кои сме ние: споделете накратко професионалните си интереси (използвайте “comment” section под този блог) http://web.stcloudstate.edu/pmiltenoff/faculty/
Reality Check (before we do tech) | минута за откровение (преди да започнем с технологии):
who is our audience | кого учим/обучаваме? https://blog.stcloudstate.edu/ims/2018/04/21/in-memoriam-avicii/ https://blog.stcloudstate.edu/ims/2018/04/17/edtech-implementation-fails/
why technology application fails | защо се проваля използването на технологии в обучението?
Understanding Purpose | какъв е смисълът
Insufficient Modeling of Best Practices | недостатъчен или несподелен опит
Bad First Impressions | лоши първи впечатления
Real-World Usability Challenges | ежедневни проблеми
The Right Data to Track Progress | кои данни определят успеха
Share your thoughts for the fails | Сподели твоите мисли за провала
Тема1. Сравняване на Kahoot, Edpuzzle и Apester – 1-1, 1/2 час продължителност Topic 1: A comparison of Kahoot, Apester and EdPuzzle
Дискусия, относно методиката на използване. Споделяне на опит кога и как го използват колегите от България и САЩ (други страни?).
Short demonstration and discussion regarding methodology of use. Sharing experience of use.
Споделяне на опит | ideas and experience exchange.
Comparison to other tools (e.g. flipped classroom advantage to Kahoot; difference from EdPuzzle, similarities to EdPuzzle) | съпоставяне с други инструменти: например, обърната класна стая – предимство пред Кахут; разлики и прилики с ЕдПъзил и тн)
Създаване на акаунт | account creation and building of learning objects
Comparison to other tools (e.g. flipped classroom advantage to Kahoot; difference from EdPuzzle, similarities to EdPuzzle) | съпоставяне с други инструменти: например, обърната класна стая – предимство пред Кахут; разлики и прилики с Еиптстър и тн)
Тема 2. Виртуална реалност в учебния процес – теория и практика- 1-1, 1/2 час продължителност Topic 2. Virtual reality in teaching and learning – theory and hands-on
When a student is brilliant on the street corner but falling asleep in class, something is wrong with the schooling system Ако учащ се е страхотен на ъгъла на улицата, но се проваля или заспива в клас, тогава нещо е грешно с учебната система https://blog.stcloudstate.edu/ims/2018/04/17/education-teched-frenemies/
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.
Is anyone out there using CrazyEgg, Hotjar, Mouseflow or the like as a source of analytic data?
If so, I’d love to hear about what you’re using, how you’re using it, what you’ve been able to get out of it. I’m convinced that it will be useful for informing content contributors about how their content is being (or more likely not being) consumed by users — but I’m particularly interested in other ways to utilize the tools and the data they provide.
Thanks so much! Amy
————
Amy Kimura
Web Services Librarian, Shared User Services
Rutgers University Libraries amy.kimura@rutgers.edu
p: 848.932.5920
Here is the 2016 session and contact information to the three fellows, who did an excellent presentation not only how, but why exactly these tools: http://sched.co/69f2
Here is the link to the 2017 session, which seems closest to your question. http://sched.co/953o Again, the two presenters most probably will be able to help you with your questions, if they have not seen already your posting on the LITA listserv and responded.
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.
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Daries, J. P., Reich, J., Waldo, J., Young, E. M., Whittinghill, J., Ho, A. D., … Chuang, I. (2014). Privacy, Anonymity, and Big Data in the Social Sciences. Commun. ACM, 57(9), 56–63. https://doi.org/10.1145/2643132
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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
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Lorenzo, G., Lledó, A., Roig, R., Lorenzo, A., & Pomares, J. (2016). New Educational Challenges and Innovations: Students with Disability in Immersive Learning Environments. In Virtual Learning. InTech. https://doi.org/10.5772/65219
10. The Virtualized Library: A Librarian’s Introduction to Docker and Virtual Machines
This session will introduce two major types of virtualization, virtual machines using tools like VirtualBox and Vagrant, and containers using Docker. The relative strengths and drawbacks of the two approaches will be discussed along with plenty of hands-on time. Though geared towards integrating these tools into a development workflow, the workshop should be useful for anyone interested in creating stable and reproducible computing environments, and examples will focus on library-specific tools like Archivematica and EZPaarse. With virtualization taking a lot of the pain out of installing and distributing software, alleviating many cross-platform issues, and becoming increasingly common in library and industry practices, now is a great time to get your feet wet.
(One three-hour session)
11. Digital Empathy: Creating Safe Spaces Online
User research is often focused on measures of the usability of online spaces. We look at search traffic, run card sorting and usability testing activities, and track how users navigate our spaces. Those results inform design decisions through the lens of information architecture. This is important, but doesn’t encompass everything a user needs in a space.
This workshop will focus on the other component of user experience design and user research: how to create spaces where users feel safe. Users bring their anxieties and stressors with them to our online spaces, but informed design choices can help to ameliorate that stress. This will ultimately lead to a more positive interaction between your institution and your users.
The presenters will discuss the theory behind empathetic design, delve deeply into using ethnographic research methods – including an opportunity for attendees to practice those ethnographic skills with student participants – and finish with the practical application of these results to ongoing and future projects.
(One three-hour session)
14. ARIA Basics: Making Your Web Content Sing Accessibility
https://dequeuniversity.com/assets/html/jquery-summit/html5/slides/landmarks.html
Are you a web developer or create web content? Do you add dynamic elements to your pages? If so, you should be concerned with making those dynamic elements accessible and usable to as many as possible. One of the most powerful tools currently available for making web pages accessible is ARIA, the Accessible Rich Internet Applications specification. This workshop will teach you the basics for leveraging the full power of ARIA to make great accessible web pages. Through several hands-on exercises, participants will come to understand the purpose and power of ARIA and how to apply it for a variety of different dynamic web elements. Topics will include semantic HTML, ARIA landmarks and roles, expanding/collapsing content, and modal dialog. Participants will also be taught some basic use of the screen reader NVDA for use in accessibility testing. Finally, the lessons will also emphasize learning how to keep on learning as HTML, JavaScript, and ARIA continue to evolve and expand.
Participants will need a basic background in HTML, CSS, and some JavaScript.
(One three-hour session)
18. Learning and Teaching Tech
Tech workshops pose two unique problems: finding skilled instructors for that content, and instructing that content well. Library hosted workshops are often a primary educational resource for solo learners, and many librarians utilize these workshops as a primary outreach platform. Tackling these two issues together often makes the most sense for our limited resources. Whether a programming language or software tool, learning tech to teach tech can be one of the best motivations for learning that tech skill or tool, but equally important is to learn how to teach and present tech well.
This hands-on workshop will guide participants through developing their own learning plan, reviewing essential pedagogy for teaching tech, and crafting a workshop of their choice. Each participant will leave with an actionable learning schedule, a prioritized list of resources to investigate, and an outline of a workshop they would like to teach.
(Two three-hour sessions)
23. Introduction to Omeka S
Omeka S represents a complete rewrite of Omeka Classic (aka the Omeka 2.x series), adhering to our fundamental principles of encouraging use of metadata standards, easy web publishing, and sharing cultural history. New objectives in Omeka S include multisite functionality and increased interaction with other systems. This workshop will compare and contrast Omeka S with Omeka Classic to highlight our emphasis on 1) modern metadata standards, 2) interoperability with other systems including Linked Open Data, 3) use of modern web standards, and 4) web publishing to meet the goals medium- to large-sized institutions.
In this workshop we will walk through Omeka S Item creation, with emphasis on LoD principles. We will also look at the features of Omeka S that ease metadata input and facilitate project-defined usage and workflows. In accordance with our commitment to interoperability, we will describe how the API for Omeka S can be deployed for data exchange and sharing between many systems. We will also describe how Omeka S promotes multiple site creation from one installation, in the interest of easy publishing with many objects in many contexts, and simplifying the work of IT departments.
(One three-hour session)
24. Getting started with static website generators
Have you been curious about static website generators? Have you been wondering who Jekyll and Hugo are? Then this workshop is for you
But this article isn’t about setting up a domain name and hosting for your website. It’s for the step after that, the actual making of that site. The typical choice for a lot of people would be to use something like WordPress. It’s a one-click install on most hosting providers, and there’s a gigantic market of plugins and themes available to choose from, depending on the type of site you’re trying to build. But not only is WordPress a bit overkill for most websites, it also gives you a dynamically generated site with a lot of moving parts. If you don’t keep all of those pieces up to date, they can pose a significant security risk and your site could get hijacked.
The alternative would be to have a static website, with nothing dynamically generated on the server side. Just good old HTML and CSS (and perhaps a bit of Javascript for flair). The downside to that option has been that you’ve been relegated to coding the whole thing by hand yourself. It’s doable, but you just want a place to share your work. You shouldn’t have to know all the idiosyncrasies of low-level web design (and the monumental headache of cross-browser compatibility) to do that.
Static website generators are tools used to build a website made up only of HTML, CSS, and JavaScript. Static websites, unlike dynamic sites built with tools like Drupal or WordPress, do not use databases or server-side scripting languages. Static websites have a number of benefits over dynamic sites, including reduced security vulnerabilities, simpler long-term maintenance, and easier preservation.
In this hands-on workshop, we’ll start by exploring static website generators, their components, some of the different options available, and their benefits and disadvantages. Then, we’ll work on making our own sites, and for those that would like to, get them online with GitHub pages. Familiarity with HTML, git, and command line basics will be helpful but are not required.
(One three-hour session)
26. Using Digital Media for Research and Instruction
To use digital media effectively in both research and instruction, you need to go beyond just the playback of media files. You need to be able to stream the media, divide that stream into different segments, provide descriptive analysis of each segment, order, re-order and compare different segments from the same or different streams and create web sites that can show the result of your analysis. In this workshop, we will use Omeka and several plugins for working with digital media, to show the potential of video streaming, segmentation and descriptive analysis for research and instruction.
(One three-hour session)
28. Spark in the Dark 101 https://zeppelin.apache.org/
This is an introductory session on Apache Spark, a framework for large-scale data processing (https://spark.apache.org/). We will introduce high level concepts around Spark, including how Spark execution works and it’s relationship to the other technologies for working with Big Data. Following this introduction to the theory and background, we will walk workshop participants through hands-on usage of spark-shell, Zeppelin notebooks, and Spark SQL for processing library data. The workshop will wrap up with use cases and demos for leveraging Spark within cultural heritage institutions and information organizations, connecting the building blocks learned to current projects in the real world.
(One three-hour session)
29. Introduction to Spotlight https://github.com/projectblacklight/spotlight http://www.spotlighttechnology.com/4-OpenSource.htm
Spotlight is an open source application that extends the digital library ecosystem by providing a means for institutions to reuse digital content in easy-to-produce, attractive, and scholarly-oriented websites. Librarians, curators, and other content experts can build Spotlight exhibits to showcase digital collections using a self-service workflow for selection, arrangement, curation, and presentation.
This workshop will introduce the main features of Spotlight and present examples of Spotlight-built exhibits from the community of adopters. We’ll also describe the technical requirements for adopting Spotlight and highlight the potential to customize and extend Spotlight’s capabilities for their own needs while contributing to its growth as an open source project.
(One three-hour session)
31. Getting Started Visualizing your IoT Data in Tableau https://www.tableau.com/
The Internet of Things is a rising trend in library research. IoT sensors can be used for space assessment, service design, and environmental monitoring. IoT tools create lots of data that can be overwhelming and hard to interpret. Tableau Public (https://public.tableau.com/en-us/s/) is a data visualization tool that allows you to explore this information quickly and intuitively to find new insights.
This full-day workshop will teach you the basics of building your own own IoT sensor using a Raspberry Pi (https://www.raspberrypi.org/) in order to gather, manipulate, and visualize your data.
All are welcome, but some familiarity with Python is recommended.
(Two three-hour sessions)
32. Enabling Social Media Research and Archiving
Social media data represents a tremendous opportunity for memory institutions of all kinds, be they large academic research libraries, or small community archives. Researchers from a broad swath of disciplines have a great deal of interest in working with social media content, but they often lack access to datasets or the technical skills needed to create them. Further, it is clear that social media is already a crucial part of the historical record in areas ranging from events your local community to national elections. But attempts to build archives of social media data are largely nascent. This workshop will be both an introduction to collecting data from the APIs of social media platforms, as well as a discussion of the roles of libraries and archives in that collecting.
Assuming no prior experience, the workshop will begin with an explanation of how APIs operate. We will then focus specifically on the Twitter API, as Twitter is of significant interest to researchers and hosts an important segment of discourse. Through a combination of hands-on and demos, we will gain experience with a number of tools that support collecting social media data (e.g., Twarc, Social Feed Manager, DocNow, Twurl, and TAGS), as well as tools that enable sharing social media datasets (e.g., Hydrator, TweetSets, and the Tweet ID Catalog).
The workshop will then turn to a discussion of how to build a successful program enabling social media collecting at your institution. This might cover a variety of topics including outreach to campus researchers, collection development strategies, the relationship between social media archiving and web archiving, and how to get involved with the social media archiving community. This discussion will be framed by a focus on ethical considerations of social media data, including privacy and responsible data sharing.
Time permitting, we will provide a sampling of some approaches to social media data analysis, including Twarc Utils and Jupyter Notebooks.
Top 10 IT Issues, 2017: Foundations for Student Success
Susan Grajek and the 2016–2017 EDUCAUSE IT Issues Panel Tuesday, January 17, 2017http://er.educause.edu/articles/2017/1/top-10-it-issues-2017-foundations-for-student-successThe 2017 EDUCAUSE Top 10 IT Issues are all about student success
Developing a holistic, agile approach to reduce institutional exposure to information security threats
That program should encompass people, process, and technologies:
Educate users
Develop processes to identify and protect the most sensitive data
Implement technologies to encrypt data and find and block advanced threats coming from outside the network via from any type of device
Who Outside the IT Department Should Care Most about This Issue?
End-users, to understand how to avoid exposing their credentials
Unit heads, to protect institutional data
Senior leaders, to hold people accountable
Institutional leadership, to endorse, fund, and advocate for good information security
Issue #2: Student Success and Completion
Effectively applying data and predictive analytics to improve student success and completion
Predictive analytics allows us to track trends, discover gaps and inefficiencies, and displace “best guess” scenarios based on implicitly developed stories about students.
Issue #3: Data-Informed Decision Making
Ensuring that business intelligence, reporting, and analytics are relevant, convenient, and used by administrators, faculty, and students
Higher education information systems generate vast amounts of data daily (including the classroom/LMS). This potentially rich source of information is underused. Even though most institutions have created reports, dashboards, and other distillations of data, these are not necessarily useful or used to inform strategic objectives such as student success or institutional efficiency.
Issue #4: Strategic Leadership
Repositioning or reinforcing the role of IT leadership as a strategic partner with institutional leadership
CIOs have two challenges in this regard. The first is getting to the table. Contemporary requirements for IT leaders position them well for strategic leadership.18 Those requirements include expertise in management and business practices, project portfolio management, negotiation, and change leadership. However, business-savvy CIOs can alienate some academics, particularly those opposed to administrators as leaders. Worse, not all CIOs are well-equipped for a position at the executive table.
Issue #5: Sustainable Funding
Developing IT funding models that sustain core services, support innovation, and facilitate growth
Two complications have deepened the IT funding challenge in recent years. The first is that information technology is now incontrovertibly core to the mission and function of colleges and universities. The second complication is that at most institutions, digital investments and technology refreshes have been funded with capital expenditures. Yet IT services and infrastructure are moving outside the institution, generally to the cloud, and cloud funding depends on ongoing expenditures rather than one-time investments.
Issue #6: Data Management and Governance
Improving the management of institutional data through data standards, integration, protection, and governance
Data management and governance is not an IT issue. It requires a broad, top-down approach because all departments need to buy in and agree. All stakeholders (data owners as well as IR, IT, and institutional leaders) must collaboratively develop a common set of data definitions and a common understanding of what data is needed, in what format, and for what purposes. This coordination, or governance, will enable constituents to communicate with confidence about the data (e.g., “the single version of truth”) and the standards (e.g., APLU, IPEDS, CDS) under which it is collected.
Institutions often choose to approach data management from three perspectives: (1) accuracy, (2) usability, and (3) privacy. The IT organization has a role to play in creating and maintaining data warehouses, integrating systems to facilitate data exchange, and maintaining standards for data privacy and security.
Issue #7: Higher Education Affordability
Prioritizing IT investments and resources in the context of increasing demand and limited resources
Uncoordinated, redundant expenditures supplant other needed investments, such as consistent classroom technology or dedicated information security staff. Planning needs to occur at the institutional or departmental level, but it also needs a place to coalesce and be assessed regionally, nationally, and in some cases, globally, because there isn’t enough money to do everything that institutional leaders, faculty, and others want or even need to do. Public systems are making some headway in sharing services, but for the most part, local optimization supersedes collaboration and compromise.
Issue #8: Sustainable Staffing
Ensuring adequate staffing capacity and staff retention as budgets shrink or remain flat and as external competition grows
As institutions become more dependent on their IT organizations, IT organizations are more dependent on the expertise and quality of their workforce. New hires need to be great hires, and great staff need to want to stay. Each new hire can change the culture and effectiveness of the IT organizations
Issue #9: Next-Gen Enterprise IT
Developing and implementing enterprise IT applications, architectures, and sourcing strategies to achieve agility, scalability, cost-effectiveness, and effective analytics
Buildings should outlive alumni; technology shouldn’t. IT leaders are examining core enterprise applications, including ERPs (traditionally, suites of financial, HR, and student information systems) and LMSs, for their ability to meet current and future needs.
Issue #10: Digital Transformation of Learning
Collaborating with faculty and academic leadership to apply technology to teaching and learning in ways that reflect innovations in pedagogy and the institutional mission
According to Michael Feldstein and Phil Hill, personalized learning applies technology to three processes: content (moving content delivery out of the classroom and allowing students to set their pace of learning); tutoring (allowing interactive feedback to both students and faculty); and contact time (enabling faculty to observe students’ work and coach them more).
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more on IT in this IMS blog https://blog.stcloudstate.edu/ims?s=information+technology
Join the Blended Librarians Online Learning Community for the second webcast in a series of conversations with Blended Librarians. This session explores the role of Blended Librarians by discussing with our panel how they developed their skills, how they obtained their positions, what their work is like, what their challenges are and what they enjoy about being a Blended Librarian. This panel conversation takes place on Thursday, March 2, 2017 at 3 p.m. EST with our guests J. Lindsay O’Neill, Francesca Marineo, Kristin (Miller) Woodward, Julie Hartwell, and Amanda Clossen.
Panelists
Lindsay O’Neill is the Instructional Design Librarian at California State University, Fullerton’s Pollak Library, where she designs and develops tutorials related to information literacy and library research using Articulate Storyline, Adobe Captivate, and Camtasia. She is also a faculty member in CSUF’s Master of Science in Instructional Design and Technology program. Lindsay regularly consults on effective pedagogy, instructional design, educational technology, open licensing, and accessibility. Lindsay holds a Master in Education, specializing in Educational Technology/Instructional Design, as well as a Master of Library and Information Science.
Francesca Marineo is an instructional design librarian at Nevada State College. She received her MLIS from the University of California, Los Angeles, where she discovered her profound passion for information literacy instruction. Currently pursuing a Master in Educational Psychology, she focuses on improving teaching and learning in higher education through innovative pedagogy and data-driven design.
Kristin Woodward is Online Programs and Instructional Design Coordinator at UWM Libraries. In this role Kristin consults with faculty and teaching staff to build information competencies and library resources into the framework of online, hybrid and competency based courses. Kristin also serves as the campus lead for the student-funded Open Textbook and OER Project as well as the library team lead for Scholarly Communication.
Julie Hartwell is an Instructional Design Librarian at the University of Missouri-Kansas City’s Miller Nichols Library. She serves as liaison to the Sociology, Criminal Justice, and Instructional Design departments. She contributes to the creation of library learning objects and instruction for the library’s Research Essentials program. She is a content creator and instructional designer for the New Literacies Alliance, an inter-institutional information literacy consortium. Julie is a Quality Matters Peer Reviewer. She received her masters of library and information science from the University of Iowa.
Amanda Clossen has been working as the Learning Design Librarian at Penn State University Libraries for the past five years. In this position, she has worked on projects spanning the micro to macro aspects of learning design. She has created award-winning videos, overseen Penn State’s transition from an in-house guide product to LibGuides, and was deeply involved in integrating the Libraries in the new LMS, Canvas. Her research interests include accessibility, video usability, and concept based teaching.
From printed newspapers to born-digital news, libraries and other cultural heritage institutions have a central role in ensuring future access to news content. This conference will examine issues and challenges in collecting and preserving the news and making it available to users. Do access and preservation have different prerequisites? In addition, the conference will explore how news media is used and transformed by researchers and the public.
Can we recognize variable user needs? Do we offer the most suitable APIs?
Proposals should address the main theme and related topics, including but not limited to:
Users’ experiences with digital newspaper collections and their usability expectations
Case studies of patron services for digitized and born-digital news (e.g., management systems, reading devices, printout services, etc.)
How digitized news collections are being used in the digital humanities, by researchers, and by the public
The importance and possibilities of citizen science
Long-term sustainability planning for news collections and the role of institutional commitment in preservation and sustainability planning
How institutions make digital newspaper collections freely accessible
Rules, regulations, or legislation for mandatory deposit of news content, paper or otherwise
Legal deposit libraries offering access to in-copyright digitized newspapers
National Libraries co-operating with newspaper publishing houses in digitization, access, etc.
Data research that benefits preservation practice and planning
Changing collection building in a social media and online world
New methods for media monitoring
Harvesting and preservation of web-only news content
Issues around suppression of digitized/digital news content and take down orders
Other proposals relevant to the main conference theme will also be considered.
Note: Papers from this conference will be considered for a special issue of IFLA Journal. All authors will be invited to use feedback from the conference to revise their work and submit it for peer review in collaboration with the IFLA Journal editorial committee and the conference organizing committee.
Submission Guidelines
Proposal abstracts should be submitted as an MS Word file. Proposal abstracts must be submitted by 27 January 2017, must be in English, and should clearly
include:
Title of proposed paper
Abstract of proposed paper (no more than 300 words)
Name(s) of presenter(s) plus position and/or title
Employer / affiliated institution
Contact information including e-mail address and telephone number
Short biographical statement(s) of presenter(s)
Proposal abstracts should be emailed to all conference committee members:
Selected presenters will be notified by 3 February 2017. To discuss any matter relating to this Call for Papers, please contact the conference committee members listed above.
Accepted papers
Complete accepted papers should be 3000-6000 words in length and be an original submission not published elsewhere.
Complete accepted papers and accompanying presentation slides must be submitted by 17 April 2017.
Final papers should be written in English.
The papers will be made available on the Conference Website and the News Media Section Website under theCreative Commons Attribution 4.0 license.
Approximately 20 minutes will be allowed for the presentation of the paper.
Registration
Registration information will be posted on the Conference Website at the beginning of 2017.
Important dates
27 January 2017 Proposal abstracts due
3 February 2017 Acceptance notices sent to authors
10 February 2017 Start of registration
10 April 2017 Completed papers and presentations submitted
27-28 April 2017 Conference
Please note The Programme Committee regrets that it has no funding to assist prospective authors and the submission of an abstract must be on the understanding that the costs of attending the conference including registration, travel, accommodation and other expenses, are the responsibility of the presenters of the accepted papers, or their institutions. No financial support can be provided by IFLA, but a special invitation can be issued to authors.
