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IRDL proposal

Applications for the 2018 Institute will be accepted between December 1, 2017 and January 27, 2018. Scholars accepted to the program will be notified in early March 2018.

Title:

Learning to Harness Big Data in an Academic Library

Abstract (200)

Research on Big Data per se, as well as on the importance and organization of the process of Big Data collection and analysis, is well underway. The complexity of the process comprising “Big Data,” however, deprives organizations of ubiquitous “blue print.” The planning, structuring, administration and execution of the process of adopting Big Data in an organization, being that a corporate one or an educational one, remains an elusive one. No less elusive is the adoption of the Big Data practices among libraries themselves. Seeking the commonalities and differences in the adoption of Big Data practices among libraries may be a suitable start to help libraries transition to the adoption of Big Data and restructuring organizational and daily activities based on Big Data decisions.
Introduction to the problem. Limitations

The redefinition of humanities scholarship has received major attention in higher education. The advent of digital humanities challenges aspects of academic librarianship. Data literacy is a critical need for digital humanities in academia. The March 2016 Library Juice Academy Webinar led by John Russel exemplifies the efforts to help librarians become versed in obtaining programming skills, and respectively, handling data. Those are first steps on a rather long path of building a robust infrastructure to collect, analyze, and interpret data intelligently, so it can be utilized to restructure daily and strategic activities. Since the phenomenon of Big Data is young, there is a lack of blueprints on the organization of such infrastructure. A collection and sharing of best practices is an efficient approach to establishing a feasible plan for setting a library infrastructure for collection, analysis, and implementation of Big Data.
Limitations. This research can only organize the results from the responses of librarians and research into how libraries present themselves to the world in this arena. It may be able to make some rudimentary recommendations. However, based on each library’s specific goals and tasks, further research and work will be needed.

 

 

Research Literature

“Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it…”
– Dan Ariely, 2013  https://www.asist.org/publications/bulletin/aprilmay-2017/big-datas-impact-on-privacy-for-librarians-and-information-professionals/

Big Data is becoming an omnipresent term. It is widespread among different disciplines in academia (De Mauro, Greco, & Grimaldi, 2016). This leads to “inconsistency in meanings and necessity for formal definitions” (De Mauro et al, 2016, p. 122). Similarly, to De Mauro et al (2016), Hashem, Yaqoob, Anuar, Mokhtar, Gani and Ullah Khan (2015) seek standardization of definitions. The main connected “themes” of this phenomenon must be identified and the connections to Library Science must be sought. A prerequisite for a comprehensive definition is the identification of Big Data methods. Bughin, Chui, Manyika (2011), Chen et al. (2012) and De Mauro et al (2015) single out the methods to complete the process of building a comprehensive definition.

In conjunction with identifying the methods, volume, velocity, and variety, as defined by Laney (2001), are the three properties of Big Data accepted across the literature. Daniel (2015) defines three stages in big data: collection, analysis, and visualization. According to Daniel, (2015), Big Data in higher education “connotes the interpretation of a wide range of administrative and operational data” (p. 910) and according to Hilbert (2013), as cited in Daniel (2015), Big Data “delivers a cost-effective prospect to improve decision making” (p. 911).

The importance of understanding the process of Big Data analytics is well understood in academic libraries. An example of such “administrative and operational” use for cost-effective improvement of decision making are the Finch & Flenner (2016) and Eaton (2017) case studies of the use of data visualization to assess an academic library collection and restructure the acquisition process. Sugimoto, Ding & Thelwall (2012) call for the discussion of Big Data for libraries. According to the 2017 NMC Horizon Report “Big Data has become a major focus of academic and research libraries due to the rapid evolution of data mining technologies and the proliferation of data sources like mobile devices and social media” (Adams, Becker, et al., 2017, p. 38).

Power (2014) elaborates on the complexity of Big Data in regard to decision-making and offers ideas for organizations on building a system to deal with Big Data. As explained by Boyd and Crawford (2012) and cited in De Mauro et al (2016), there is a danger of a new digital divide among organizations with different access and ability to process data. Moreover, Big Data impacts current organizational entities in their ability to reconsider their structure and organization. The complexity of institutions’ performance under the impact of Big Data is further complicated by the change of human behavior, because, arguably, Big Data affects human behavior itself (Schroeder, 2014).

De Mauro et al (2015) touch on the impact of Dig Data on libraries. The reorganization of academic libraries considering Big Data and the handling of Big Data by libraries is in a close conjunction with the reorganization of the entire campus and the handling of Big Data by the educational institution. In additional to the disruption posed by the Big Data phenomenon, higher education is facing global changes of economic, technological, social, and educational character. Daniel (2015) uses a chart to illustrate the complexity of these global trends. Parallel to the Big Data developments in America and Asia, the European Union is offering access to an EU open data portal (https://data.europa.eu/euodp/home ). Moreover, the Association of European Research Libraries expects under the H2020 program to increase “the digitization of cultural heritage, digital preservation, research data sharing, open access policies and the interoperability of research infrastructures” (Reilly, 2013).

The challenges posed by Big Data to human and social behavior (Schroeder, 2014) are no less significant to the impact of Big Data on learning. Cohen, Dolan, Dunlap, Hellerstein, & Welton (2009) propose a road map for “more conservative organizations” (p. 1492) to overcome their reservations and/or inability to handle Big Data and adopt a practical approach to the complexity of Big Data. Two Chinese researchers assert deep learning as the “set of machine learning techniques that learn multiple levels of representation in deep architectures (Chen & Lin, 2014, p. 515). Deep learning requires “new ways of thinking and transformative solutions (Chen & Lin, 2014, p. 523). Another pair of researchers from China present a broad overview of the various societal, business and administrative applications of Big Data, including a detailed account and definitions of the processes and tools accompanying Big Data analytics.  The American counterparts of these Chinese researchers are of the same opinion when it comes to “think about the core principles and concepts that underline the techniques, and also the systematic thinking” (Provost and Fawcett, 2013, p. 58). De Mauro, Greco, and Grimaldi (2016), similarly to Provost and Fawcett (2013) draw attention to the urgent necessity to train new types of specialists to work with such data. As early as 2012, Davenport and Patil (2012), as cited in Mauro et al (2016), envisioned hybrid specialists able to manage both technological knowledge and academic research. Similarly, Provost and Fawcett (2013) mention the efforts of “academic institutions scrambling to put together programs to train data scientists” (p. 51). Further, Asomoah, Sharda, Zadeh & Kalgotra (2017) share a specific plan on the design and delivery of a big data analytics course. At the same time, librarians working with data acknowledge the shortcomings in the profession, since librarians “are practitioners first and generally do not view usability as a primary job responsibility, usually lack the depth of research skills needed to carry out a fully valid” data-based research (Emanuel, 2013, p. 207).

Borgman (2015) devotes an entire book to data and scholarly research and goes beyond the already well-established facts regarding the importance of Big Data, the implications of Big Data and the technical, societal, and educational impact and complications posed by Big Data. Borgman elucidates the importance of knowledge infrastructure and the necessity to understand the importance and complexity of building such infrastructure, in order to be able to take advantage of Big Data. In a similar fashion, a team of Chinese scholars draws attention to the complexity of data mining and Big Data and the necessity to approach the issue in an organized fashion (Wu, Xhu, Wu, Ding, 2014).

Bruns (2013) shifts the conversation from the “macro” architecture of Big Data, as focused by Borgman (2015) and Wu et al (2014) and ponders over the influx and unprecedented opportunities for humanities in academia with the advent of Big Data. Does the seemingly ubiquitous omnipresence of Big Data mean for humanities a “railroading” into “scientificity”? How will research and publishing change with the advent of Big Data across academic disciplines?

Reyes (2015) shares her “skinny” approach to Big Data in education. She presents a comprehensive structure for educational institutions to shift “traditional” analytics to “learner-centered” analytics (p. 75) and identifies the participants in the Big Data process in the organization. The model is applicable for library use.

Being a new and unchartered territory, Big Data and Big Data analytics can pose ethical issues. Willis (2013) focusses on Big Data application in education, namely the ethical questions for higher education administrators and the expectations of Big Data analytics to predict students’ success.  Daries, Reich, Waldo, Young, and Whittinghill (2014) discuss rather similar issues regarding the balance between data and student privacy regulations. The privacy issues accompanying data are also discussed by Tene and Polonetsky, (2013).

Privacy issues are habitually connected to security and surveillance issues. Andrejevic and Gates (2014) point out in a decision making “generated by data mining, the focus is not on particular individuals but on aggregate outcomes” (p. 195). Van Dijck (2014) goes into further details regarding the perils posed by metadata and data to the society, in particular to the privacy of citizens. Bail (2014) addresses the same issue regarding the impact of Big Data on societal issues, but underlines the leading roles of cultural sociologists and their theories for the correct application of Big Data.

Library organizations have been traditional proponents of core democratic values such as protection of privacy and elucidation of related ethical questions (Miltenoff & Hauptman, 2005). In recent books about Big Data and libraries, ethical issues are important part of the discussion (Weiss, 2018). Library blogs also discuss these issues (Harper & Oltmann, 2017). An academic library’s role is to educate its patrons about those values. Sugimoto et al (2012) reflect on the need for discussion about Big Data in Library and Information Science. They clearly draw attention to the library “tradition of organizing, managing, retrieving, collecting, describing, and preserving information” (p.1) as well as library and information science being “a historically interdisciplinary and collaborative field, absorbing the knowledge of multiple domains and bringing the tools, techniques, and theories” (p. 1). Sugimoto et al (2012) sought a wide discussion among the library profession regarding the implications of Big Data on the profession, no differently from the activities in other fields (e.g., Wixom, Ariyachandra, Douglas, Goul, Gupta, Iyer, Kulkami, Mooney, Phillips-Wren, Turetken, 2014). A current Andrew Mellon Foundation grant for Visualizing Digital Scholarship in Libraries seeks an opportunity to view “both macro and micro perspectives, multi-user collaboration and real-time data interaction, and a limitless number of visualization possibilities – critical capabilities for rapidly understanding today’s large data sets (Hwangbo, 2014).

The importance of the library with its traditional roles, as described by Sugimoto et al (2012) may continue, considering the Big Data platform proposed by Wu, Wu, Khabsa, Williams, Chen, Huang, Tuarob, Choudhury, Ororbia, Mitra, & Giles (2014). Such platforms will continue to emerge and be improved, with librarians as the ultimate drivers of such platforms and as the mediators between the patrons and the data generated by such platforms.

Every library needs to find its place in the large organization and in society in regard to this very new and very powerful phenomenon called Big Data. Libraries might not have the trained staff to become a leader in the process of organizing and building the complex mechanism of this new knowledge architecture, but librarians must educate and train themselves to be worthy participants in this new establishment.

 

Method

 

The study will be cleared by the SCSU IRB.
The survey will collect responses from library population and it readiness to use and use of Big Data.  Send survey URL to (academic?) libraries around the world.

Data will be processed through SPSS. Open ended results will be processed manually. The preliminary research design presupposes a mixed method approach.

The study will include the use of closed-ended survey response questions and open-ended questions.  The first part of the study (close ended, quantitative questions) will be completed online through online survey. Participants will be asked to complete the survey using a link they receive through e-mail.

Mixed methods research was defined by Johnson and Onwuegbuzie (2004) as “the class of research where the researcher mixes or combines quantitative and qualitative research techniques, methods, approaches, concepts, or language into a single study” (Johnson & Onwuegbuzie, 2004 , p. 17).  Quantitative and qualitative methods can be combined, if used to complement each other because the methods can measure different aspects of the research questions (Sale, Lohfeld, & Brazil, 2002).

 

Sampling design

 

  • Online survey of 10-15 question, with 3-5 demographic and the rest regarding the use of tools.
  • 1-2 open-ended questions at the end of the survey to probe for follow-up mixed method approach (an opportunity for qualitative study)
  • data analysis techniques: survey results will be exported to SPSS and analyzed accordingly. The final survey design will determine the appropriate statistical approach.

 

Project Schedule

 

Complete literature review and identify areas of interest – two months

Prepare and test instrument (survey) – month

IRB and other details – month

Generate a list of potential libraries to distribute survey – month

Contact libraries. Follow up and contact again, if necessary (low turnaround) – month

Collect, analyze data – two months

Write out data findings – month

Complete manuscript – month

Proofreading and other details – month

 

Significance of the work 

While it has been widely acknowledged that Big Data (and its handling) is changing higher education (https://blog.stcloudstate.edu/ims?s=big+data) as well as academic libraries (https://blog.stcloudstate.edu/ims/2016/03/29/analytics-in-education/), it remains nebulous how Big Data is handled in the academic library and, respectively, how it is related to the handling of Big Data on campus. Moreover, the visualization of Big Data between units on campus remains in progress, along with any policymaking based on the analysis of such data (hence the need for comprehensive visualization).

 

This research will aim to gain an understanding on: a. how librarians are handling Big Data; b. how are they relating their Big Data output to the campus output of Big Data and c. how librarians in particular and campus administration in general are tuning their practices based on the analysis.

Based on the survey returns (if there is a statistically significant return), this research might consider juxtaposing the practices from academic libraries, to practices from special libraries (especially corporate libraries), public and school libraries.

 

 

References:

 

Adams Becker, S., Cummins M, Davis, A., Freeman, A., Giesinger Hall, C., Ananthanarayanan, V., … Wolfson, N. (2017). NMC Horizon Report: 2017 Library Edition.

Andrejevic, M., & Gates, K. (2014). Big Data Surveillance: Introduction. Surveillance & Society, 12(2), 185–196.

Asamoah, D. A., Sharda, R., Hassan Zadeh, A., & Kalgotra, P. (2017). Preparing a Data Scientist: A Pedagogic Experience in Designing a Big Data Analytics Course. Decision Sciences Journal of Innovative Education, 15(2), 161–190. https://doi.org/10.1111/dsji.12125

Bail, C. A. (2014). The cultural environment: measuring culture with big data. Theory and Society, 43(3–4), 465–482. https://doi.org/10.1007/s11186-014-9216-5

Borgman, C. L. (2015). Big Data, Little Data, No Data: Scholarship in the Networked World. MIT Press.

Bruns, A. (2013). Faster than the speed of print: Reconciling ‘big data’ social media analysis and academic scholarship. First Monday, 18(10). Retrieved from http://firstmonday.org/ojs/index.php/fm/article/view/4879

Bughin, J., Chui, M., & Manyika, J. (2010). Clouds, big data, and smart assets: Ten tech-enabled business trends to watch. McKinsey Quarterly, 56(1), 75–86.

Chen, X. W., & Lin, X. (2014). Big Data Deep Learning: Challenges and Perspectives. IEEE Access, 2, 514–525. https://doi.org/10.1109/ACCESS.2014.2325029

Cohen, J., Dolan, B., Dunlap, M., Hellerstein, J. M., & Welton, C. (2009). MAD Skills: New Analysis Practices for Big Data. Proc. VLDB Endow., 2(2), 1481–1492. https://doi.org/10.14778/1687553.1687576

Daniel, B. (2015). Big Data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904–920. https://doi.org/10.1111/bjet.12230

Daries, J. P., Reich, J., Waldo, J., Young, E. M., Whittinghill, J., Ho, A. D., … Chuang, I. (2014). Privacy, Anonymity, and Big Data in the Social Sciences. Commun. ACM, 57(9), 56–63. https://doi.org/10.1145/2643132

De Mauro, A. D., Greco, M., & Grimaldi, M. (2016). A formal definition of Big Data based on its essential features. Library Review, 65(3), 122–135. https://doi.org/10.1108/LR-06-2015-0061

De Mauro, A., Greco, M., & Grimaldi, M. (2015). What is big data? A consensual definition and a review of key research topics. AIP Conference Proceedings, 1644(1), 97–104. https://doi.org/10.1063/1.4907823

Dumbill, E. (2012). Making Sense of Big Data. Big Data, 1(1), 1–2. https://doi.org/10.1089/big.2012.1503

Eaton, M. (2017). Seeing Library Data: A Prototype Data Visualization Application for Librarians. Publications and Research. Retrieved from http://academicworks.cuny.edu/kb_pubs/115

Emanuel, J. (2013). Usability testing in libraries: methods, limitations, and implications. OCLC Systems & Services: International Digital Library Perspectives, 29(4), 204–217. https://doi.org/10.1108/OCLC-02-2013-0009

Graham, M., & Shelton, T. (2013). Geography and the future of big data, big data and the future of geography. Dialogues in Human Geography, 3(3), 255–261. https://doi.org/10.1177/2043820613513121

Harper, L., & Oltmann, S. (2017, April 2). Big Data’s Impact on Privacy for Librarians and Information Professionals. Retrieved November 7, 2017, from https://www.asist.org/publications/bulletin/aprilmay-2017/big-datas-impact-on-privacy-for-librarians-and-information-professionals/

Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Ullah Khan, S. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47(Supplement C), 98–115. https://doi.org/10.1016/j.is.2014.07.006

Hwangbo, H. (2014, October 22). The future of collaboration: Large-scale visualization. Retrieved November 7, 2017, from http://usblogs.pwc.com/emerging-technology/the-future-of-collaboration-large-scale-visualization/

Laney, D. (2001, February 6). 3D Data Management: Controlling Data Volume, Velocity, and Variety.

Miltenoff, P., & Hauptman, R. (2005). Ethical dilemmas in libraries: an international perspective. The Electronic Library, 23(6), 664–670. https://doi.org/10.1108/02640470510635746

Philip Chen, C. L., & Zhang, C.-Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275(Supplement C), 314–347. https://doi.org/10.1016/j.ins.2014.01.015

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

Provost, F., & Fawcett, T. (2013). Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data, 1(1), 51–59. https://doi.org/10.1089/big.2013.1508

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

Reyes, J. (2015). The skinny on big data in education: Learning analytics simplified. TechTrends: Linking Research & Practice to Improve Learning, 59(2), 75–80. https://doi.org/10.1007/s11528-015-0842-1

Schroeder, R. (2014). Big Data and the brave new world of social media research. Big Data & Society, 1(2), 2053951714563194. https://doi.org/10.1177/2053951714563194

Sugimoto, C. R., Ding, Y., & Thelwall, M. (2012). Library and information science in the big data era: Funding, projects, and future [a panel proposal]. Proceedings of the American Society for Information Science and Technology, 49(1), 1–3. https://doi.org/10.1002/meet.14504901187

Tene, O., & Polonetsky, J. (2012). Big Data for All: Privacy and User Control in the Age of Analytics. Northwestern Journal of Technology and Intellectual Property, 11, [xxvii]-274.

van Dijck, J. (2014). Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society; Newcastle upon Tyne, 12(2), 197–208.

Waller, M. A., & Fawcett, S. E. (2013). Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management. Journal of Business Logistics, 34(2), 77–84. https://doi.org/10.1111/jbl.12010

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

West, D. M. (2012). Big data for education: Data mining, data analytics, and web dashboards. Governance Studies at Brookings, 4, 1–0.

Willis, J. (2013). Ethics, Big Data, and Analytics: A Model for Application. Educause Review Online. Retrieved from https://docs.lib.purdue.edu/idcpubs/1

Wixom, B., Ariyachandra, T., Douglas, D. E., Goul, M., Gupta, B., Iyer, L. S., … Turetken, O. (2014). The current state of business intelligence in academia: The arrival of big data. CAIS, 34, 1.

Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97–107. https://doi.org/10.1109/TKDE.2013.109

Wu, Z., Wu, J., Khabsa, M., Williams, K., Chen, H. H., Huang, W., … Giles, C. L. (2014). Towards building a scholarly big data platform: Challenges, lessons and opportunities. In IEEE/ACM Joint Conference on Digital Libraries (pp. 117–126). https://doi.org/10.1109/JCDL.2014.6970157

 

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more on big data





Software Carpentry Workshop

Minnesota State University Moorhead – Software Carpentry Workshop

https://www.eventbrite.com/e/minnesota-state-university-moorhead-software-carpentry-workshop-registration-38516119751

Reservation code: 680510823  Reservation for: Plamen Miltenoff

Hagen Hall – 600 11th St S – Room 207 – Moorhead

pad.software-carpentry.org/2017-10-27-Moorhead

http://www.datacarpentry.org/lessons/

https://software-carpentry.org/lessons/

++++++++++++++++

Friday

Jeff – certified Bash Python, John

http://bit.do/msum_swc

https://ntmoore.github.io/2017-10-27-Moorhead/

what is shall and what does it do. language close to computers, fast.

what is “bash” . cd, ls

shell job is a translator between the binory code, the middle name. several types of shells, with slight differences. one natively installed on MAC and Unix. born-again shell

bash commands: cd change director, ls – list; ls -F if it does not work: man ls (manual for LS); colon lower left corner tells you can scrool; q for escape; ls -ltr

arguments is colloquially used with different names. options, flags, parameters

cd ..  – move up one directory .      pwd : see the content      cd data_shell/   – go down one directory

cd ~  – brings me al the way up .        $HOME (universally defined variable

the default behavior of cd is to bring to home directory.

the core shall commands accept the same shell commands (letters)

$ du -h .     gives me the size of the files. ctrl C to stop

$ clear . – clear the entire screen, scroll up to go back to previous command

man history $ history $! pwd (to go to pwd . $ history | grep history (piping)

$ cat (and the file name) – standard output

$ cat ../

+++++++++++++++
how to edit and delete files

to create new folder: $ mkdir . – make directory

text editors – nano, vim (UNIX text editors) .      $ nano draft.txt .  ctrl O (save) ctr X (exit) .
$ vim . shift  esc (key)  and in command line – wq (write quit) or just “q”

$ mv draft.txt ../data . (move files)

to remove $ rm thesis/:     $ man rm

copy files       $cp    $ touch . (touches the file, creates if new)

remove $ rm .    anything PSEUDO is dangerous   Bash profile: cp -i

*- wild card, truncate       $ ls analyzed      (list of the analyized directory)

stackoverflow web site .

+++++++++++++++++

head command .  $head basilisk.day (check only the first several lines of a large file

$ for filename in basilisk.dat unicorn.dat . (making a loop = multiline)

> do (expecting an action) do

> head -n 3 $filename . (3 is for the first three line of the file to be displayed and -n is for the number)

> done

for doing repetitive functions

also

$ for filename in *.dat ; do head -n 3$x; done

$ for filename in *.dat ; do echo $filename do head -n 3$x; done

$ echo $filename (print statement)

how to loop

$ for filename in *.dat ; do echo $filename ; echo head -n 3 $filename ; done

ctrl c or apple comd dot to get out of the loop

http://swcarpentry.github.io/shell-novice/02-filedir/

also

$ for filename in *.dat

> do

> $filename

> head -n  10 (first ten files ) $filename | tail  -n 20 (last twenty lines)

$ for filename  in *.dat

do
>> echo  $filename
>> done

$ for filename in *.dat
>> do
>> cp $filename orig_$filename
>>done\

history > something else

$ head something.else

+++++++++++++

another function: word count

$ wc *.pdb  (protein databank)

$ head cubane.pdb

if i don;t know how to read the outpun $ man wc

the difference between “*” and “?”

$ wc -l *.pdb

$

wc -l *.pdb > lenghts.txs

cat lenghts.txt

$ for fil in *.txt
>>> do
>>> wc -l $fil

by putting a $ sign use that not the actual text.

++++++++++++

nano middle.sh . The entire point of shell is to automate

$ bash (exectubale) to run the program middle.sh

rwx – rwx – rwx . (owner – group -anybody)

bash middle.sh

$ file middle.sh

$path .

$ echo $PATH | tr “:” “\n”

/usr/local/bin

/usr/bin

/bin

/usr/sbin

/sbin

/Applications/VMware Fusion.app/Contents/Public

/usr/local/munki

$ export PATH=$PWD:$PATH

(this is to make sure that the last version of Python is running)

$ ls ~ . (hidden files)        

$ ls -a ~

$ touch .bach_profile .bashrc

$history | grep PATH

   19   echo $PATH

   44  echo #PATH | tr “:” “\n”

   45   echo $PATH | tr “:” “\n”

   46   export PATH=$PWD:$PATH

   47  echo #PATH | tr “:” “\n”

   48   echo #PATH | tr “:” “\n”

   55  history | grep PATH

 

wc -l “$@” | sort -n ($@  – encompasses eerything. will process every single file in the list of files

 

$ chmod (make it executable)

 

$ find . -type d . (find only directories, recursively, ) 

$ find . -type f (files, instead of directories)

$ find . -name ‘*.txt’ . (find files by name, don’t forget single quotes)

$ wc -l $(find . -name ‘*.txt’)  – when searching among direcories on different level

$ find . -name ‘*.txt’ | xargs wc -l    –  same as above ; two ways to do one and the same

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Saturday

Python

Link to the Python Plotting : https://swcarpentry.github.io/python-novice-gapminder

C and C++. scripting purposes in microbiology (instructor). libraries, packages alongside Python, which can extend its functionality. numpy and scipy (numeric and science python). Python for academic libraries?

going out of python $ quit () .      python expect beginning and end parenthesis

new terminal needed after installation. anaconda 5.0.1

python 3 is complete redesign, not only an update.

http://swcarpentry.github.io/python-novice-gapminder/setup/

jupyter crashes in safari. open in chrome. spg engine maybe

https://swcarpentry.github.io/python-novice-gapminder/01-run-quit/

to start python in the terminal $ python

>> variable = 3

>> variable +10

several data types.

stored in JSON format.

command vs edit code.  code cell is the gray box. a text cell is plain text

markdown syntax. format working with git and github .  search explanation in https://swcarpentry.github.io/python-novice-gapminder/01-run-quit/

hackMD https://hackmd.io/ (use your GIthub account)

PANDOC – translates different data formats. https://pandoc.org/

print is a function

in what cases i will run my data trough Python instead of SPSS?

python is a 0 based language. starts counting with 0 – Java, C, P

atom_name = ‘helium ‘
print(atom_name[0])                  string slicing and indexing is tricky

atom_name = ‘helium ‘
print(atom_name[0:6])
vs
atom_name = ‘helium ‘
print(atom_name[7])                python does not know how to slice it
synthax of python is        start : end : countby/step
string versus list .   string is in a single quote, list will have brakets
strings allow me to work not only w values, revers the string
atom_name = ‘helium lithium beryllium’
print(atom_name[::-1])
muillyreb muihtil muileh
Atom_name = ‘helium’
len (atom_name)                                     6 .             case sensitive
to clean the memory, restart the kernel
objects in Python have different types. adopt a class, value may have class inherent in its defintion
print (type(’42’)) .   Python tells me that it is a string
print (type(42)) .    tells e it is a string
LaTex
to combine integer and letter: print (str(1) + ‘A’)
converting a string to integer . : print (1 + int(’55’)) .    all the same type
translation table. numerical representation of a string
float
print (‘half is’, 1 / 2.0)
built in functions and help
print is a function, lenght is a function (len); type, string, int, max, round,
Python does not explain well why the code breaks
ASCI character set – build in Python conversation
libraries – package: https://swcarpentry.github.io/python-novice-gapminder/06-libraries/
function “import”
 Saturdady afternoon
reading .CSV in Python
http://swcarpentry.github.io/python-novice-gapminder/files/python-novice-gapminder-data.zip
**For windows users only: set up git https://swcarpentry.github.io/workshop-template/#git 
python is object oriented and i can define the objects
python creates its own types of objects (which we model) and those are called “DataFrame”
method applied it is an attribute to data that already exists. – difference from function
data.info() . is function – it does not take any arguments
whereas
data.columns . is a method
print (data.T) .  transpose.  not easy in Excel, but very easy in Python
print (data.describe()) .
/Users/plamen_local/anaconda3/lib/python3.6/site-packages/pandas/__init__.py
%matplotlib inline teling Jupyter notebook

import pandas

data = pandas.read_csv(‘/Users/plamen_local/Desktop/data/gapminder_gdp_oceania.csv’ , index_col=’country’)
data.loc[‘Australia’].plot()
plt.xticks(rotation=10)

GD plot 2 is the most well known library.

xelatex is a PDF engine.  reST restructured text like Markdown.  google what is the best PDF engine with Jupyter

four loops .  any computer language will have the concept of “for” loop. In Python: 1. whenever we create a “for” loop, that line must end with a single colon

2. indentation.  any “if” statement in the “for” loop, gets indented

Privacy & Security in Today’s Library

Privacy & Security in Today’s Library by Amigos Library Services

The virtuality of privacy and security on the from Plamen Miltenoff

From: Jodie Borgerding [mailto:Borgerding@amigos.org]
Sent: Wednesday, July 05, 2017 3:07 PM
To: Miltenoff, Plamen <pmiltenoff@stcloudstate.edu>
Cc: Nicole Walsh <WALSH@AMIGOS.ORG>
Subject: Proposal Submission for Privacy & Security Conference

Hi Plamen,

Thank you for your recent presentation proposal for the online conference, Privacy & Security in Today’s Library, presented by Amigos Library Services. Your proposal, The role of the library in teaching with technology unsupported by campus IT: the privacy and security issues of the “third-party,” has been accepted. I just wanted to confirm that you are still available to present on September 21, 2017 and if you have a time preference for your presentation (11 am, 12 pm, or 2 pm Central). If you are no longer able to participate, please let me know.

Nicole will be touch with you shortly with additional details and a speaker’s agreement.

Please let me know if you have any questions.

Thanks!
___________________

Jodie Borgerding Consulting & Education Services Manager Amigos Library Services 1190 Meramec Station Road, Suite 207 | Ballwin, MO  63021-6902 800-843-8482 x2897 | 972-340-2897(direct) http://www.amigos.org | borgerding@amigos.org

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Bio

Dr. Plamen Miltenoff is an Information Specialist and Professor at St. Cloud State University. His education includes several graduate degrees in history and Library and Information Science and terminal degrees in education and psychology.

His professional interests encompass social media, multimedia, Web development and design, gaming and gamification, and learning environments (LEs).

Dr. Miltenoff organized and taught classes such as LIB 290 “Social Media in Global Context” (http://web.stcloudstate.edu/pmiltenoff/lib290/) and LIB 490/590 “Digital Storytelling” (http://web.stcloudstate.edu/pmiltenoff/lib490/) where issues of privacy and security are discussed.

Twitter handle @SCSUtechinstruc

Facebook page: https://www.facebook.com/InforMediaServices/

The virtuality of privacy and security on the modern campus:

The role of the library in teaching with technology unsupported by campus IT: the privacy and security issues of the “third-party software” teaching and learning

Abstract/Summary of Your Proposed Session

The virtualization reality changes rapidly all aspects of learning and teaching: from equipment to methodology, just when faculty have finalized their syllabus, they have to start a new, if they want to keep abreast with content changes and upgrades and engagement of a very different student fabric – Millennials.

Mainframes are replaced by microcomputers, microcomputers by smart phones and tablets, hard drives by cloud storage and wearables by IoT. The pace of hardware, software and application upgrade is becoming unbearable for students and faculty. Content creation and methodology becomes useless by the speed of becoming obsolete. In such environment, faculty students and IT staff barely can devote time and energy to deal with the rapidly increasing vulnerability connected with privacy and security.

In an effort to streamline ever-becoming-scarce resources, campus IT “standardizes” campus use of applications. Those are the applications, which IT chooses to troubleshoot campus-wide. Those are the applications recommended to faculty and students to use.

In an unprecedented burgeoning amount of applications, specifically for mobile devices, it is difficult to constraint faculty and students to use campus IT sanctioned applications, especially considering the rapid pace of such applications becoming obsolete. Faculty and students often “stray” away and go with their own choice. Such decision exposes faculty and students, personally, and the campus, institutionally, at risk. In a recent post by THE Journal, attention on campuses is drown to the fact that cyberattacks shift now from mobile devices to IoT and campus often are struggling even with their capability to guarantee cybersecurity of mobile devices on campus. Further, the use of third-party application might be in conflict with the FERPA campus-mandated policies. Such policies are lengthy and complex to absorb, both by faculty and students and often are excessively restrictive in terms of innovative ways to improve methodology and pedagogy of teaching and learning. The current procedure of faculty and students proposing new applications is a lengthy and cumbersome bureaucratic process, which often render the end-users’ proposals obsolete by the time the process is vetted.

Where/what is the balance between safeguarding privacy on campus and fostering security without stifling innovation and creativity? Can the library be the campus hub for education about privacy and security, the sandbox for testing and innovation and the body to expedite decision-making?

Abstract

The pace of changes in teaching and learning is becoming impossible to sustain: equipment evolves in accelerated pace, the methodology of teaching and learning cannot catch up with the equipment changes and atop, there are constant content updates. In an even-shrinking budget, faculty, students and IT staff barely can address the issues above, less time and energy left to address the increasing concerns about privacy and security.

In an unprecedented burgeoning amount of applications, specifically for mobile devices, it is difficult to constraint faculty and students to use campus IT sanctioned applications, especially considering the rapid pace of such applications becoming obsolete. Faculty and students often “stray” away and go with their own choice. Such decision exposes faculty and students, personally, and the campus, institutionally, at risk. In a recent post by THE Journal (https://blog.stcloudstate.edu/ims/2017/06/06/cybersecurity-and-students/), attention on campuses is drawn to the fact of cyberattacks shifting from mobile devices to IoT but campus still struggling to guarantee cybersecurity of mobile devices on campus. Further, the use of third-party applications might be in conflict with the FERPA campus-mandated policies. Such policies are lengthy and complex to absorb, both by faculty and students and often are excessively restrictive in terms of innovative ways to improve methodology and pedagogy of teaching and learning. The current procedure of faculty and students proposing new applications is a lengthy and cumbersome bureaucratic process, which often render the end-users’ proposals obsolete by the time the process is vetted.

Where/what is the balance between safeguarding privacy on campus and fostering security without stifling innovation and creativity? Can the library be the campus hub for education about privacy and security, the sandbox for testing and innovation and the body to expedite decision-making?

https://blog.stcloudstate.edu/ims/2017/06/06/cybersecurity-and-students/

Anything else you would like to add

3 take-aways from this session:

  • Discuss and form an opinion about the education-pertinent issues of privacy and security from the broad campus perspective, versus the narrow library one
  • Discuss and form an opinion about the role of the library on campus in terms of the greater issues of privacy and security

Re-examine the thin red line of the balance between standardization and innovation; between the need for security and privacy protection a

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presentation:
https://www.slideshare.net/aidemoreto/the-virtuality-of-privacy-and-security-on-the 

chat – slide 4, privacy. please take 2 min and share your definition of privacy on campus. Does it differ between faculty and students?  what are the main characteristics to determine privacy

chat – slide 5, security. please take 2 min and share your definition of security on campus regarding electronic activities. Who’s responsibility is security? IT issue [only]?

poles: slide 6, technology unsupported by campus IT, is it worth considering? 1. i am a great believer in my freedom of choice 2. I firmly follow rules and this applies to the use of computer tools and applications 3. Whatever…

chat –  slide 6, why third party applications? pros and cons. E.g. pros – familiarity with third party versus campus-required

pole, slide 6, appsmashing. App smashing is the ability to combine mobile apps in your teaching process. How do you feel about it? 1. The force is with us 2. Nonsense…

pole slide 7 third party apps and the comfort of faculty. How do you see the freedom of using third party apps? 1. All I want, thank you 2. I would rather follow the rules 3. Indifference is my middle name

pole slide 8 Technology standardization? 1. yes, 2. no, 3. indifferent

chat slide 9 if the two major issues colliding in this instance are: standardization versus third party and they have impact on privacy and security, how would you argue for the one or the other?

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notes from the conference

 

 

Measuring Library Vendor Cyber Security: Seven Easy Questions Every Librarian Can Ask

http://journal.code4lib.org/articles/11413

Bill Walker: http://www.amigos.org/innovating_metadata

 

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

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

leap motion

Leap Motion’s new technology lets you reach out and touch things in VR

http://mashable.com/2016/12/05/leap-motion-mobile-vr

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

Save

storyboard in course authoring

Storyboarding: A Simple Way To Get Professional In Course Authoring

Storyboarding: A Simple Way To Get Professional In Course Authoring

7 Tip To Get Started With Storyboarding

  1. Pick the tool you will be using for creating storyboards (MS Office tools: Word, Excel, PPT).
  2. Create a sketch of the title page with the name of the course and 3-5 page mockups containing the course sets out to accomplish, e.g.
    1. What Ιs Gamification?
    2. Game Thinking.
    3. Game Elements.
    4. Motivation & Psychology.
    5. Gamification Design Framework.
  3. After you have outlined the principal structure of the course, it’s time to go deeper and describe the structure of every section of the course.
  4. Try to visualize the general layout of every page.
  5. Enumerate all screens in the storyboard, e.g. 1/16, 2/16, 3/16, and so on.
  6. Lay out the screens of your storyboard in order and try following the story they tell. Look at them through your learners’ eyes. Is all information delivered in a logical order? Did you leave out something important? Are your notes clear enough so that you will be able to build a complete course using your storyboard for reference a week later? Have you touched upon all important areas?
  7. If you need to present your storyboard to your client (student) or boss (student) for review, it pays to show it to a good friend first and ask for feedback. Ask them to read your storyboard and then retell what they took away from it in their own words.

in Geenio (https://www.geen.io/) this mode is called the Pathboard, and entering it allows you to see the structure of your whole course, the sequence in which pages and tests are presented, as well as the connections between them. Some course editors not only provide you an overview of your course’s structure, but enable you to edit the course’s structure and add additional elements to it as well.

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More on storyboard importance for your hybrid/online course design
:

online learning and course design

Five tips for designing an online course:

  1. State your objective: Each lesson should have one concise, action-oriented learning objective to ensure your lesson design process is focused.
  2. Think as a private tutor: Learners today are inundated with media tailored to them and they expect learning to be tailored as well. So think about how the tools available, including new technologies, will help create meaningful learning moments for all your students.
  3. Storyboard before you build: Being able to see a complete lesson, especially one that integrates various mediums, is essential to creating a successful learning experience.
  4. Build towards high-order thinking: Technology in education can go beyond multiple-choice questions and document repositories. Don’t be afraid to integrate tools that let learners create and share.
  5. Remember you’re learning too: Reviewing learner results from a lesson shouldn’t just be about their score, but also evaluating how effectively the lesson was developed and executed so your teaching can adapt and learn as well.

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https://blog.stcloudstate.edu/ims/2016/04/06/guide-to-project-based-learning/

tools for iOS, Android etc.

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elearning infographics

 

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https://blog.stcloudstate.edu/ims/2014/04/20/how-to-gamify-your-classroom-in-6-easy-steps/

  • Clarify your desired learning outcomes first
  • Make them measurable
  • Choose a ‘big idea’
  • Storyboard the game. Make sure there’s room for failure and multiple courses of action.
  • Design learning activities
  • Build teams
  • THEN apply the game dynamics

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Videos In Your Classroom

 

video editing for mobile devices

WeVideo

http://www.educatorstechnology.com/2014/07/this-is-how-to-create-videos-in-google.html

mobile app available here: https://chrome.google.com/webstore/detail/wevideo-video-editor-and/okgjbfikepgflmlelgfgecmgjnmnmnnb

Google is now elbowing with YouTube Capture (free) , Splice (free), iMovie (paid), Adobe Premiere Clip (free) – Android, iOS and several others for the basic video editing turf on mobile devices. Its big pitch – being directly connected to Google Drive.

Additional mobile apps for video editing: VidTrim (free) Pro ($1.99) – Android only; SloPro – iOS only; Magisto (available for both iOS and Android); Vee for Video ($1.99) – iOS; Lumify, (free), VideoGrade (5.99) – iOS; KineMaster (free) – Android; PowerDirector (free) – Android; Pinnacle Studio Pro ($14.99) – iOS; Movie Edit Touch (free) –  Windows:
https://www.gazelle.com/thehorn/2013/11/25/best-video-editing-apps-for-ios-android/
http://www.stuff.tv/features/10-best-video-editing-apps-mobile
http://mashable.com/2012/07/25/video-editing-apps
http://mashable.com/2013/06/05/video-edit-apps/#4Y3PWgcp2ZqQ
http://www.hongkiat.com/blog/ios-android-video-editing-apps/

10 Best Video Editing Apps for Phones and Tablets: http://www.tomsguide.com/us/pictures-story/511-Video-Editor-Android-iOS-Video-Filters.html

More on the topic in this blog:

How to Use the Free YouTube Video Editor

5 Options for Creating Screen Capture Images and Videos – Including on Chromebooks

LMS and embedded librarianship

Tumbleson, B. E., & Burke, J. (. J. (2013). Embedding librarianship in learning management systems: A how-to-do-it manual for librarians. Neal-Schuman, an imprint of the American Library Association.

Embedding librarianship in learning management systems:

https://scsu.mplus.mnpals.net/vufind/Record/007650037

see also:

Kvenild, C., & Calkins, K. (2011). Embedded Librarians: Moving Beyond One-Shot Instruction – Books / Professional Development – Books for Academic Librarians – ALA Store. ACRL. Retrieved from http://www.alastore.ala.org/detail.aspx?ID=3413

p. 20 Embedding Academic and Research Libraries in the Curriculum: 2014-nmc-horizon-report-library-EN

xi. the authors are convinced that LMS embedded librarianship is becoming he primary and most productive method for connecting with college and university students, who are increasingly mobile.

xii. reference librarians engage the individual, listen, discover what is wanted and seek to point the stakeholder in profitable directions.
Instruction librarians, in contrast, step into the classroom and attempt to lead a group of students in new ways of searching wanted information.
Sometimes that instruction librarian even designs curriculum and teaches their own credit course to guide information seekers in the ways of finding, evaluating, and using information published in various formats.
Librarians also work in systems, emerging technologies, and digital initiatives in order to provide infrastructure or improve access to collections and services for tend users through the library website, discovery layers, etc. Although these arenas seemingly differ, librarians work as one.

xiii. working as an LMS embedded librarian is both a proactive approach to library instruction using available technologies and enabling a 24/7 presence.

1. Embeddedness involves more that just gaining perspective. It also allows the outsider to become part of the group through shared learning experiences and goals. 3. Embedded librarianship in the LMS is all about being as close as possible to where students are receiving their assignments and gaining instruction and advice from faculty members. p. 6 When embedded librarians provide ready access to scholarly electronic collections, research databases, and Web 2.0 tools and tutorials, the research experience becomes less frustrating and more focused for students. Undergraduate associate this familiar online environment with the academic world.

p. 7 describes embedding a reference librarian, which LRS reference librarians do, “partnership with the professor.” However, there is room for “Research Consultations” (p. 8). While “One-Shot Library Instruction Sessions” and “Information Literacy Credit Courses” are addressed (p. 809), the content of these sessions remains in the old-fashioned lecturing type of delivering the information.

p. 10-11. The manuscript points out clearly the weaknesses of using a Library Web site. The authors fail to see that the efforts of the academic librarians must go beyond Web page and seek how to easy the information access by integrating the power of social media with the static information residing on the library web page.

p. 12 what becomes disturbingly clear is that faculty focus on the mechanics of the research paper over the research process. Although students are using libraries, 70 % avoid librarians. Urging academic librarians to “take an active role and initiate the dialogue with faculty to close a divide that may be growing between them and faculty and between them and students.”
Four research context with which undergraduates struggle: big picture, language, situational context and information gathering.

p. 15 ACRL standards One and Three: librarians might engage students who rely on their smartphones, while keeping in mind that “[s]tudents who retrieve information on their smartphones may also have trouble understanding or evaluating how the information on their phone is ‘produced, organized, and disseminated’ (Standard One).
Standard One by its definition seems obsolete. If information is formatted for desktops, it will be confusing when on smart phones, And by that, it is not mean to adjust the screen size, but change the information delivery from old fashioned lecturing to more constructivist forms. e.ghttp://web.stcloudstate.edu/pmiltenoff/bi/

p. 15 As for Standard Two, which deals with effective search strategies, the LMS embedded librarian must go beyond Boolean operators and controlled vocabulary, since emerging technologies incorporate new means of searching. As unsuccessfully explained to me for about two years now at LRS: hashtag search, LinkedIn groups etc, QR codes, voice recognition etc.

p. 16. Standard Five. ethical and legal use of information.

p. 23 Person announced in 2011 OpenClass compete with BB, Moodle, Angel, D2L, WebCT, Sakai and other
p. 24 Common Features: content, email, discussion board, , synchronous chat and conferencing tools (Wimba and Elluminate for BB)

p. 31 information and resources which librarians could share via LMS
– post links to dbases and other resources within the course. LIB web site, LibGuides or other subject-related course guidelines
– information on research concepts can be placed in a similar fashion. brief explanation of key information literacy topics (e.g difference between scholarly and popular periodical articles, choosing or narrowing research topics, avoiding plagiarism, citing sources properly whining required citations style, understanding the merits of different types of sources (Articles book’s website etc)
– Pertinent advice the students on approaching the assignment and got to rheank needed information
– Tutorials on using databases or planning searches step-by-step screencast navigating in search and Candida bass video search of the library did you a tour of the library

p. 33 embedded librarian being copied on the blanked emails from instructor to students.
librarian monitors the discussion board

p. 35 examples: students place specific questions on the discussion board and are assured librarian to reply by a certain time
instead of F2F instruction, created a D2L module, which can be placed in any course. videos, docls, links to dbases, links to citation tools etc. Quiz, which faculty can use to asses the the students

p. 36 discussion forum just for the embedded librarian. for the students, but faculty are encouraged to monitor it and provide content- or assignment-specific input
video tutorials and searching tips
Contact information email phone active IM chat information on the library’s open hours

p. 37 questions to consider
what is the status of the embedded librarian: T2, grad assistant

p. 41 pilot program. small scale trial which is run to discover and correct potential problems before
One or two faculty members, with faculty from a single department
Pilot at Valdosta State U = a drop-in informatil session with the hope of serving the information literacy needs of distance and online students, whereas at George Washington U, librarian contacted a distance education faculty member to request embedding in his upcoming online Mater’s course
p. 43 when librarians sense that current public services are not being fully utilized, it may signal that a new approach is needed.
pilots permit tinkering. they are all about risk-taking to enhance delivery

p. 57 markeing LMS ebedded Librarianship

library collections, services and facilities because faculty may be uncertain how the service benefits their classroom teaching and learning outcomes.
my note per
“it is incumbent upon librarians to promote this new mode of information literacy instruction.” it is so passe. in the times when digital humanities is discussed and faculty across campus delves into digital humanities, which de facto absorbs digital literacy, it is shortsighted for academic librarians to still limit themselves into “information literacy,” considering that lip service is paid for for librarians being the leaders in the digital humanities movement. If academic librarians want to market themselves, they have to think broad and start with topics, which ARE of interest for the campus faculty (digital humanities included) and then “push” their agenda (information literacy). One of the reasons why academic libraries are sinking into oblivion is because they are sunk already in 1990-ish practices (information literacy) and miss the “hip” trends, which are of interest for faculty and students. The authors (also paying lip services to the 21st century necessities), remain imprisoned to archaic content. In the times, when multi (meta) literacies are discussed as the goal for library instruction, they push for more arduous marketing of limited content. Indeed, marketing is needed, but the best marketing is by delivering modern and user-sought content.
the stigma of “academic librarians keep doing what they know well, just do it better.” Lip-services to change, and life-long learning. But the truth is that the commitment to “information literacy” versus the necessity to provide multi (meta) literacites instruction (Reframing Information Literacy as a metaliteracy) is minimizing the entire idea of academic librarians reninventing themselves in the 21st century.
Here is more: NRNT-New Roles for New Times

p. 58 According to the Burke and Tumbleson national LMS embedded librarianship survey, 280 participants yielded the following data regarding embedded librarianship:

  • traditional F2F LMS courses – 69%
  • online courses – 70%
  • hybrid courses – 54%
  • undergraduate LMS courses 61%
  • graduate LMS courses 42%

of those respondents in 2011, 18% had the imitative started for four or more years, which place the program in 2007. Thus, SCSU is almost a decade behind.

p. 58 promotional methods:

  • word of mouth
  • personal invitation by librarians
  • email by librarians
  • library brochures
  • library blogs

four years later, the LRS reference librarians’ report https://magic.piktochart.com/output/5704744-libsmart-stats-1415 has no mentioning of online courses, less to say embedded librarianship

my note:
library blog
was offered numerous times to the LRS librarians and, consequently to the LRS dean, but it was brushed away, as were brushed away the proposals for modern institutional social media approach (social media at LRS does not favor proficiency in social media but rather sees social media as learning ground for novices, as per 11:45 AM visit to LRS social media meeting of May 6, 2015). The idea of the blog advantages to static HTML page was explained in length, but it was visible that the advantages are not understood, as it is not understood the difference of Web 2.0 tools (such as social media) and Web 1.0 tools (such as static web page). The consensus among LRS staff and faculty is to keep projecting Web 1.0 ideas on Web 2.0 tools (e.g. using Facebook as a replacement of Adobe Dreamweaver: instead of learning how to create static HTML pages to broadcast static information, use Facebook for fast and dirty announcement of static information). It is flabbergasting to be rejected offering a blog to replace Web 1.0 in times when the corporate world promotes live-streaming (http://www.socialmediaexaminer.com/live-streaming-video-for-business/) as a way to  promote services (academic librarians can deliver live their content)

p. 59 Marketing 2.0 in the information age is consumer-oriented. Marketing 3.0 in the values-driven era, which touches the human spirit (Kotler, Katajaya, and Setiawan 2010, 6).
The four Ps: products and services, place, price and promotion. Libraries should consider two more P’s: positioning and politics.

Mathews (2009) “library advertising should focus on the lifestyle of students. the academic library advertising to students today needs to be: “tangible, experiential, relatebale, measurable, sharable and surprising.” Leboff (2011, p. 400 agrees with Mathews: the battle in the marketplace is not longer for transaction, it is for attention. Formerly: billboards, magazines, newspapers, radio, tv, direct calls. Today: emphasize conversation, authenticity, values, establishing credibility and demonstrating expertise and knowledge by supplying good content, to enhance reputation (Leboff, 2011, 134). translated for the embedded librarians: Google goes that far; students want answers to their personal research dillemas and questions. Being a credentialed information specialist with years of experience is no longer enough to win over an admiring following. the embedded librarian must be seen as open and honest in his interaction with students.
p. 60  becoming attractive to end-users is the essential message in advertising LMS embedded librarianship. That attractivness relies upon two elements: being noticed and imparting values (Leboff, 2011, 99)

p. 61 connecting with faculty

p. 62 reaching students

  • attending a synchronous chat sessions
  • watching a digital tutorial
  • posting a question in a discussion board
  • using an instant messaging widget

be careful not to overload students with too much information. don’t make contact too frequently and be perceived as an annoyance and intruder.

p. 65. contemporary publicity and advertising is incorporating storytelling. testimonials differ from stories

p. 66 no-cost marketing. social media

low-cost marketing – print materials, fliers, bookmarks, posters, floor plans, newsletters, giveaways (pens, magnets, USB drives), events (orientations, workshops, contests, film viewings), campus media, digital media (lib web page, blogs, podcasts, social networking cites

p. 69 Instructional Content and Instructional Design
p. 70 ADDIE Model

ADDIE model ADDIE model

Analysis: the requirements for the given course, assignments.
Ask instructors expectations from students vis-a-vis research or information literacy activities
students knowledge about the library already related to their assignments
which are the essential resources for this course
is this a hybrid or online course and what are the options for the librarian to interact with the students.
due date for the research assignment. what is the timeline for completing the assignment
when research tips or any other librarian help can be inserted

copy of the syllabus or any other assignment document

p. 72 discuss the course with faculty member. Analyze the instructional needs of a course. Analyze students needs. Create list of goals. E.g.: how to find navigate and use the PschInfo dbase; how to create citations in APA format; be able to identify scholarly sources and differentiate them from popular sources; know other subject-related dbases to search; be able to create a bibliography and use in-text citations in APA format

p. 74 Design (Addie)
the embedded component is a course within a course. Add pre-developed IL components to the broader content of the course. multiple means of contact information for the librarians and /or other library staff. link to dbases. link to citation guidance and or tutorial on APA citations. information on how to distinguish scholarly and popular sources. links to other dbases. information and guidance on bibliographic and in-text citations n APA either through link, content written within the course a tutorial or combination. forum or a discussion board topic to take questions. f2f lib instruction session with students
p. 76 decide which resources to focus on and which skills to teach and reinforce. focus on key resources

p. 77 development (Addie).
-building content;the “landing” page at LRS is the subject guides page.  resources integrated into the assignment pages. video tutorials and screencasts

-finding existing content; google search of e.g.: “library handout narrowing topic” or “library quiz evaluating sources,” “avoiding plagiarism,” scholarly vs popular periodicals etc

-writing narrative content. p. 85

p. 87 Evaluation (Addie)

formative: to change what the embedded librarian offers to improve h/er services to students for the reminder of the course
summative at the end of the course:

p. 89  Online, F2F and Hybrid Courses

p. 97 assessment impact of embedded librarian.
what is the purpose of the assessment; who is the audience; what will focus on; what resources are available
p. 98 surveys of faculty; of students; analysis of student research assignments; focus groups of students and faculty

p. 100 assessment methods: p. 103/4 survey template
https://www.ets.org/iskills/about
https://www.projectsails.org/ (paid)
http://www.trails-9.org/
http://www.library.ualberta.ca/augustana/infolit/wassail/
p. 106 gathering LMS stats. Usability testing
examples: p. 108-9, UofFL : pre-survey and post-survey of studs perceptions of library skills, discussion forum analysis and interview with the instructor

p. 122 create an LMS module for reuse (standardized template)
p. 123 subject and course LibGuides, digital tutorials, PPTs,
research mind maps, charts, logs, or rubrics
http://creately.com/blog/wp-content/uploads/2012/12/Research-Proposal-mind-map-example.png
http://www.library.arizona.edu/help/tutorials/mindMap/sample.php  (excellent)
or paper-based if needed: Concept Map Worksheet
Productivity Tools for Graduate Students: MindMapping http://libguides.gatech.edu/c.php

rubrics:
http://www.cornellcollege.edu/LIBRARY/faculty/focusing-on-assignments/tools-for-assessment/research-paper-rubric.shtml
http://gvsu.edu/library/instruction/research-guidance-rubric-for-assignment-design-4.htm
Creating Effective Information Literacy Assignments http://www.lib.jmu.edu/instruction/assignments.aspx

course handouts
guides on research concepts http://library.olivet.edu/subject-guides/english/college-writing-ii/research-concepts/
http://louisville.libguides.com/c.php
Popular versus scholar http://www.library.arizona.edu/help/tutorials/scholarly/guide.html

list of frequently asked q/s:
blog posts
banks of reference q/s

p. 124. Resistance or Receptivity

p. 133 getting admin access to LMS for the librarians.

p. 136 mobile students, dominance of born-digital resources

 

 

 

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Summey T, Valenti S. But we don’t have an instructional designer: Designing online library instruction using isd techniques. Journal Of Library & Information Services In Distance Learning [serial online]. January 1, 2013;Available from: Scopus®, Ipswich, MA. Accessed May 11, 2015.
http://login.libproxy.stcloudstate.edu/login?qurl=http%3a%2f%2fsearch.ebscohost.com%2flogin.aspx%3fdirect%3dtrue%26db%3dedselc%26AN%3dedselc.2-52.0-84869866367%26site%3deds-live%26scope%3dsite

instructional designer library instruction using ISD techniques

Shank, J. (2006). The blended librarian: A job announcement analysis of the newly emerging position of instructional design librarian. College And Research Libraries, 67(6), 515-524.
http://login.libproxy.stcloudstate.edu/login?qurl=http%3a%2f%2fsearch.ebscohost.com%2flogin.aspx%3fdirect%3dtrue%26db%3dedselc%26AN%3dedselc.2-52.0-33845291135%26site%3deds-live%26scope%3dsite

The Blended Librarian_ A Job Announcement Analysis of the Newly Emerging Position of Instructional Design Librarian

Macklin, A. (2003). Theory into practice: Applying David Jonassen’s work in instructional design to instruction programs in academic libraries. College And Research Libraries, 64(6), 494-500.
http://login.libproxy.stcloudstate.edu/login?qurl=http%3a%2f%2fsearch.ebscohost.com%2flogin.aspx%3fdirect%3dtrue%26db%3dedselc%26AN%3dedselc.2-52.0-7044266019%26site%3deds-live%26scope%3dsite

Theory into Practice_ Applying David Jonassen_s Work in Instructional Design to Instruction Programs in Academic Libraries

Walster, D. (1995). Using Instructional Design Theories in Library and Information Science Education. Journal of Education for Library and Information Science, (3). 239.
http://login.libproxy.stcloudstate.edu/login?qurl=http%3a%2f%2fsearch.ebscohost.com%2flogin.aspx%3fdirect%3dtrue%26db%3dedsjsr%26AN%3dedsjsr.10.2307.40323743%26site%3deds-live%26scope%3dsite

Using Instructional Design Theories in Library and Information Science Education

Mackey, T. )., & Jacobson, T. ). (2011). Reframing information literacy as a metaliteracy. College And Research Libraries, 72(1), 62-78.
http://login.libproxy.stcloudstate.edu/login?qurl=http%3a%2f%2fsearch.ebscohost.com%2flogin.aspx%3fdirect%3dtrue%26db%3dedselc%26AN%3dedselc.2-52.0-79955018169%26site%3deds-live%26scope%3dsite

Reframing Information Literacy as a metaliteracy

Nichols, J. (2009). The 3 directions: Situated information literacy. College And Research Libraries, 70(6), 515-530.
http://login.libproxy.stcloudstate.edu/login?qurl=http%3a%2f%2fsearch.ebscohost.com%2flogin.aspx%3fdirect%3dtrue%26db%3dedselc%26AN%3dedselc.2-52.0-73949087581%26site%3deds-live%26scope%3dsite

The 3 Directions_ Situated literacy

 

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Journal of Library & Information Services in Distance Learning (J Libr Inform Serv Dist Learn)

https://www.researchgate.net/journal/1533-290X_Journal_of_Library_Information_Services_in_Distance_Learning

http://conference.acrl.org/

http://www.loex.org/conferences.php

http://www.ala.org/lita/about/igs/distance/lit-igdl

————

https://magic.piktochart.com/output/5704744-libsmart-stats-1415

IPython notebook

Library Juice Academy

course_intro

I also encourage students to download and install Python on their own systems. Python is a
mature and robust language with a great many third party distributions and versions, such as Ipython.
One I recommend is Active State Python. Active State produces refined and well supported
distributions with easy to use installers. Their basic, individual distribution is free. You can find it at
http://www.activestate.com/activepython/downloads
https://host.lja-computing.net:8888/notebooks/profile_intro_programming_p1/Intro_Programming_Lesson1_pmiltenoff.ipynb
  • Integers: A signed or unsigned whole number running from -32,768 to 32,768 or from 0 to 65,535 if not signed. Integers are used anytime something needs to be counted.
  • Long Integer: Any whole number outside the above range. Python doesn’t distinguish between the two though many languages do. Practically, Python’s integers range from −2,147,483,648 to 2,147,483,648 or 0 to 0 to 4,294,967,295. Most of us will be very happy with this many whole numbers to choose from.
  • Real and Floating Point Numbers: Real numbers are signed or unsigned numbers including decimals. The numbers 2,3,4 are Integers and Real Numbers. The numbers 2.1, 2.9,3.9 are Real Numbers, but not Integers. Real Numbers can include representations of irrational numbers such as pi. Real numbers must be rational, that is a decimal number that terminates after a finite number of decimals. You will sometimes encounter the term Floating Point Numbers. This is a technical term referring to the way that large Real Numbers are represented in a computer. Python hides this detail from you so Real and Floating Point are used intercangeably in this language.
  • Binary Numbers: And Octal and Hexadecimal. These are numbers used internally by computers. You will run into these values fairly often. For instance, when you see color values in HTML such as “FFFFFF” or “0000FF”,
Hexadecimal and Octal are used because humans can read them without too much trouble and they are compromise between what computers process and what we can read. Any time you see something in Octal or Hexadecimal, you are looking at something that interfaces with the lower levels of a computer. You will most commonly use Hexadecimal numbers when dealing with Unicode character encodings. Python will interpret any number which begins with a leading zero as binary unless formatting commands have been used.
Numbers such as 7i are referred to as complex. They have a real part, the 7, and an imaginary part, i. Chance are you won’t use complex numbers unless you’re working with scientific data.
A String consists of a sequence of characters. The term String refers to how this data type is represented internally. You store text in Strings. Text can by anything, letters, words, sentences, paragraphs, numbers, just about anything.
Lists are close cousins to Strings, though you may never need to think of them that way. A list is just that, a list of things. Lists may contain any number of numbers or any number of strings. List may even contain any number of other lists. Lists are compared to arrays, but they are not the same thing. In most uses, the function the same so the difference, for our purposes, is moot. Strings are like lists in that, internally, the computer works with strings in an identical manner to lists. This is why the operations on Strings are so different from numbers.
The last main data type in the Python programming language is the dictionary. Dictionaries are map types, known in other languages as hashes, and in computer science as Associative Arrays. The best way to think of what the dictionary does is to consider a Library of Congress Call Number(something this audience is familiar with). The call number is what’s called a Key. It connects to a record which contains information about a book. The combination of keys and records, called values, comprises a dictionary. A single key will connect to a discrete group of values such as the items in this record. Dictionaries will be touched on in the next lesson in some detail in the next course. These are fairly advanced data structures and require a solid understanding a programming fundamentals in order to be used properly.

Statements, an Overview

Programs consist of statements. A statement is a unit of executable code. Think of a statement like a sentence. In a nutshell, statements are how you do things in a program. Writing a program consists of breaking down a problem you want to solve into smaller pieces that you can represent as mathematical propositions and then solve. The statement is where this process gets played out. Statements themselves consist of some number of expressions involving data. Let’s see how this works.

An expression would be something like 2+2=4. This expression, however is not a complete statements. Ask Python to evaluate it and you will get the error “SyntaxError: can’t assign to operator”. What’s going on here? Basically we didn’t provide a complete statement. If we want to see the sum of 2+2 we have to write a complete statement that tells the interpreter what to do and what to do it with. The verb here is ‘print’ and the object is ‘2+2’. Ask Python to evaluate ‘print 2+2’ and it will show ‘4’. We could also throw in subject and do something a bit more detailed: ‘Sum=2+2’. In this case we are assigning the value of 2+2 to the variable, Sum. We can then do all sorts of things with Sum. We can print it. We can add other numbers to it, hand it off to a function and so on. For instance, might want to know the root of Sum. In which case we might write something like ‘print sqrt(sum)’ which will display ‘2’.

A shell is essentially a user interface that provides you access to a system’s features. Normally, this means access to an Operating System. In cases like this, the shell provides you access to the Python programming environment.

Anything preceed by a “#” is not interpreted or executed by the programming shell. Comments are used widely to document programs. One school of programming holds that code should be so clear that comments are uncessary.

Operations on Numbers

Expressions are discrete statements in programming that do something. They typically occupy one line of code, though programmers will sometimes squeeze more in. This is generally bad form and can really make your program a mess. Expressions consist of operations and data or rather data and operations on them. So, what can you do with numbers? Here is a concise list of the basic operations for integers and real numbers of all types:

Arithemetic:

  • Addition: z= x + y
  • Subtraction: z = x – y
  • Multiplication: z = x * y. Here the asterisk serves as the ‘X’ multiplication symbol from grade school.
  • Division: z = x/y. Division.
  • Exponents: z = x ** y or xy, x to the y power.

Operations have an order of precedence which follows the algebraic order of precedence. The order can be remembered by the old Algebra mnenomic, Please Excuse My Dear Aunt Sally which is remeinds you that the order of operations is:

  1. Parentheses
  2. Exponents
  3. Multiplication
  4. Division
  5. Addition
  6. Subtraction

Operations on Strings

Strings are strange creatures as I’ve noted before. They have their own operations and the arithmetic operations you saw earlier don’t behave the same way with strings.

Putting Expressions Together to Make Statements

As I noted earlier, all computer languages, and natural languages, possess pragmatics, larger scale structures which reduce ambiguity by providing context. This is a fancy way of saying just as sentences posses rules of syntax to make able to be comprehended, larger documents have similar rules. Computer Programs are no different. Here’s a break down of the structure of programs in Python, in a general sense.

  1. Programs consist of one or more modules.
  2. Modules consist of one or more statements.
  3. Statements consist of one or more expressions.
  4. Expressions create and/or manipulate objects(and variables of all kinds).

Modules and Programs are for the next class in the series, though we will survey these larger structures next lesson. For now, we’ll focus on statements and expressions. Actually, we’ve already started with expressions above. In Python, statements can do three things.

  • Assign a variable
  • Change a variable
  • Take an action

Variable Names and Reserved Words

Now that we’ve seen some variable assignments, let’s talk about best practices. First off, aside from reserved words, variable names can be almost any combination of letters, numbers and punctuation marks. You, however, should never ever, use the following punctuation marks in variable names:

      • +
      • !
      • @
      • ^
      • %
      • (
      • )
      • .
      • ?
      • /
      • :
      • ;

*

These punctuation marks tends to be operators and characters that have special meanings in most computer languages. The other issue is reserved words. What are “reserved words”? They are words that Python interprets as commands. Pythons reservers the following words.:

  • True: A special value set aside for boolean values
  • False: The other special value set aside for boolean vaules
  • None: The logical equivalent of 0
  • and: a way of combining logical conditions
  • as: describes how modules are imported
  • assert: a way of forcing something to take on a certain value. Used in debugging of large programs
  • break: breaks out of a loop and goes on with the rest of the program
  • class: declares a class for object oriented design. For now, just remember not to use this variable name
  • continue: returns to the top of the loop and keeps on going again
  • def: declares functions which allow you to modularize your code.
  • elif: else if, a cotnrol structure we’ll see next lesson
  • else: as above
  • except: another control structure
  • finally: a loop control structure
  • for: a loop control structure
  • from: used to import modules
  • global: a scoping statement
  • if: a control structure/li>
  • in: used in for each loops
  • is: a logical operator
  • lamda: like def, but weird. It defines a function in a single line. I will not teach this becuase it is icky. If you ever learn Perl you will see this sort of thing a lot and you will hate it, but that’s just my personal opinion.
  • nonlocal: a scoping command
  • not: a logical operator
  • or: another logical operator
  • pass: does nothing. Used as placeholder
  • raise: raises an error. This is used to write custom error messages. Your programs may have conditions which would be considered invalid based on our business situation. The interpreter may not consider them errors, but you might not want your user to do something so you ‘raise’ an exception and stop the program.
  • return: tells a function to return a value
  • try: this is part of an error testing statement
  • while: starts a while loop
  • with: a context manager. This will be covered in the course after the next one in this series
  • yield: works like return
Variable names should be meaningful. Let’s say I have to track a person’s driver license number. explanatory names like ‘driverLicenseNumber’.

  • Use case to make your variable names readable. Python is case sensitive, meaning a variable named ‘cat’ is different from named ‘Cat’. If you use more than one word to name variable, start of lower case the change case on the second word. For instance “bigCats = [‘Tiger’,’Lion’,’Cougar’, ‘Desmond’]”. The common practice used by programmers in many settings is that variables start with lowercase and functions(methods and so on) start with upper case. This is called “Camel Case” for its lumpy, the humpy appearance. Now, as it happens, there is something of a religious debate over this. Many Python programmers prefer to keep everything lower case and join words in a name by underscores such as “big_cats”. Use whichever is easiest or looks the nicest to you.
  • Variable names should be unique. Do not reuse names. This will cause confusion later on.
  • Python conventions. Python, as with any other programming language, has culture built up around it. That means there are some conventions surrounding variable naming. Two leading underscores, __X, denote system variables which have special meaning to the interpreter. So avoid using this for your own variables. There may be a time and place, but that’s for an advanced prorgramming course. A single underscore _X indicates to other programmers that this a fundamental variable and that they mess with it at their own peril.
  • Avoid starting variable names with a number. This may or may not return an error. It can also mislead anyone reading your program.
  • “A foolish consistency is the hobgoblin of little minds”. But not to programming minds. Consistency helps the readability of code a great deal. Once you start a system, stick with it.

Statement Syntax

Putting together valid statements can be a little hard at first. There’s a grammar to them. Thus far, we’ve mainly been workign with expressions such as “x = x+1”. You can think of expression as nouns. We’ve clearly defined x, but how do we look inside? For that we need to give it a verb, the print command. We would then write “print x”. However we can skip the middle statement and print an expression such as “print x + 1”. The interpreter evaluates this per the order of operations I laid out earlier. However, once that expression is evaluated, it then applies the verb, “print”, to that expression.

Print is a function that comes with the Python distribution. There are many more and you can create your own. We’ll cover that a bit in next lesson. Let’s look at little more at the grammar of a statement. Consider:

x = sin(b)

Assume that b has been defined elsewhere. x is the subject, b is the object and sin is the verb. Python will go to the right side of the equal sign first. It will then go to the inside of the function and evaluate what’s there first. It then evaluates the value of the function and finishes by setting x to that value. What about something like this?

x=sin(x+3/y)

Python evaluates from the inside out according to the rules of operation. Very complex statements can be built up this way.

x = sin(log((x + 3)/(e**2)))
Regardless of what this expression evaluates to (I don’t actually know), Python starts with the innermost parentheses, then works through the value of e squared then adds 3 to x and divides the result by e squared. With that worked out, it takes the logarithm of the result and takessthe sine of that before setting x to the final result.What you cannot do is execute more than one statement on a line. No more than one verb on a line. In this context, a verb is an assignment, or a command acting on an expression
markdown cell
code cell

Call up your copy of Think Python or go to the website at http://www.greenteapress.com/thinkpython/html/. Read Chapter 2. This will reiterate much of what I’ve presnted here, but this will help cement the content into you minds. Skip section 2.6 because IPython treats everything as script mode. IPyton provides you with the illusion of interactive, but everything happens asynchronously. This means that any action you type in will not instantaneously resolve as it would if you were running Python interactively on your computer. You will have to use print statements to see the results of your work.

Your assignment consists of the following:

  • Exercise 1 from Chapter 2 of Think Python. If you type an integer with a leading zero, you might get a confusing error:
    <<< zipcode = 02492

    SyntaxError: invalid token
    Other numbers seem to work, but the results are bizarre:
    <<< zipcode = 02132
    <<< zipcode
    1114
    Can you figure out what is going on? Hint: display the values 01, 010, 0100 and 01000.

  • Exercise 3 from Chapter 2 of Think Python.Assume that we execute the following assignment statements:
    width = 17
    height = 12.0
    delimiter = ‘.’
    For each of the following expressions, write the value of the expression and the type (of the value of the expression).

    width/2
    width/2.0
    height/3
    1 + 2 5
    delimiter
    5

  • Exercise 4 from Capter 2 of Think Python. Practice using the Python interpreter as a calculator:
    1. The volume of a sphere with radius r is 4/3 π r3. What is the volume of a sphere with radius 5? Hint: 392.7 is wrong!
    2. Suppose the cover price of a book is $24.95, but bookstores get a 40% discount. Shipping costs $3 for the first copy and 75 cents for each additional copy. What is the total wholesale cost for 60 copies?
    3/ If I leave my house at 6:52 am and run 1 mile at an easy pace (8:15 per mile), then 3 miles at tempo (7:12 per mile) and 1 mile at easy pace again, what time do I get home for breakfast?

In your IPython notebook Create a markdown cell and write up your exercise in there. Just copy it from the textbook or from the above write up. Next ceate a code cell and do your work in there. Please, comment your work thoroughly. You cannot provide too many comments. Use print statements to see the outcome of your work.

24 Good iPad Math Apps for Elementary Students

24 Good iPad Math Apps for Elementary Students

http://www.educatorstechnology.com/2013/12/24-good-ipad-math-apps-for-elementary.html
1- Motion Math

Feed your fish and play with numbers! Practice mental addition and subtraction with Motion Math: Hungry Fish, a delightful learning game that’s fun for children and grownups.

2- Geoboard

The Geoboard is a tool for exploring a variety of mathematical topics introduced in the elementary and middle grades. Learners stretch bands around pegs to form line segments and polygons and make discoveries about perimeter, area, angles, congruence, fractions, and more.

3- Math Vs Zombies

The world is overrun with zombies. You are a part of a squad of highly trained scientists who can save us. Using your math skills and special powers you can treat infected zombies to contain the threat.

4- Mystery Math Town

It’s part math drills, part seek and find game and totally engaging. Kids ages five and up should find this both fun and challenging. Parents should rejoice that finally there is a way to get kids to want to do more math. – Smart Apps for Kids

5- Montessori Numbers

Montessori Numbers offers a sequence of guided activities that gradually help children reinforce their skills. Each activity offers several levels of increasing complexity

6- Free Kids Counting Game

FREE and fun picture math games for kids designed by the iKidsPad team. This free iPad math app dynamically generates thousand of beginning counting games with different themes and number levels. Great interactive and challenging games helps young children build up basic counting skills and number recognition.

7- Math Puppy

Math Puppy will take you on a journey of educational fun like never before!
From toddlers to grade school, for Children of all ages – Math Puppy is the perfect way to build up your math skills. Your child will be able to enjoy a constructive, supportive, interactive fun filled environment while mastering the arts of basic math.

8- Mathmateer

While your rocket is floating weightlessly in space, the real fun begins! Play one of the many fun math missions. Each mission has touchable objects floating in space, including stars, coins, 3D shapes and more! Earn a bronze, silver or gold medal and also try to beat your high score. Missions range in difficulty from even/odd numbers all the way to square roots, so kids and their parents will enjoy hours of fun while learning math.

9- Math Ninja

Use your math skills to defend your treehouse against a hungry tomato and his robotic army in this fun action packed game! Choose between ninja stars, smoke bombs, or ninja magic – and choose your upgrades wisely!

10- Everyday Mathematics

The Equivalent Fractions game by McGraw Hill offers a quick and easy way to practice and reinforce fraction concepts and relationships. This game runs on the iPad, iPhone, and iPod Touch.

11- Elementary School Math

Based on the classroom hit Middle School Math HD, Elementary School Math HD is a stunningly beautiful and powerfully engaging application built for today’s technology-driven elementary school classroom. Emphasizing game-playing and skill development, the eight modules in Elementary School Math HD have been carefully designed by classroom teachers to provide the perfect balance between fun and the practice of fundamental skills

12- Math Tappers

MathTappers: Multiples is a simple game designed first to help learners to make sense of multiplication and division with whole numbers, and then to support them in developing fluency while maintaining
accuracy.

13- Ordered Fractions

Two Player Bluetooth Math Game! You can use two devices and play competitively or cooperatively with your classmates or parents. Ordered Fractions provides a comprehensive tool that offers an innovative method of learning about comparing and ordering fractions.

14- SlateMath

SlateMath is an iPad app that develops mathematical intuition and skills through playful interaction. The app’s 38 activities prepare children for kindergarten and first grade math.  SlateMath forms the foundation of numbers, digit writing, counting, addition, order relation, patterns, parity and problem solving.

15- NumberStax

Number Stax is a puzzle game to test your number skills! Drop numbers and operators in the correct places to match the number or expression shown at the top of the screen to score. You can’t remove tiles but you can swap them around. You can freeze the game at any time, but remember to watch the clock! Eliminate tiles, score points, and earn bonuses and achievements for as long as possible until your grid is full! Remember, the longer you play the faster it gets. Share with your family and friends to see who can get the highest score.

16- Grid Drawing

With a sheet of paper, a grid and a template, your children will be able to draw 32 drawings on iPad, 16 on the iPhone in the Lite version, more than 120 (60 on iPhone) in the full version.

17- Motion Math

Developed at the Stanford School of Education, Motion Math HD follows a star that has fallen from space, and must bound back up, up, up to its home in the stars. Moving fractions to their correct place on the number line is the only way to return. By playing Motion Math, learners improve their ability to perceive and estimate fractions in multiple forms.

18- 5th Grade Math

Splash Math is a fun and innovative way to practice math. With 9 chapters covering an endless supply of problems, it is by far the most comprehensive math workbook in the app store.

19- Sushi Monster

Meet Sushi Monster! Scholastic’s new game to practice, reinforce, and extend math fact fluency is completely engaging and appropriately challenging.

20- Math Monsters

Math Monsters Bingo is a new, fun way to master math on your iPhone, iPad and iPod touch. The game lets you practice math anytime and anywhere using a fun Bingo styled game play.

21- Grid Lines

Grid Lines is a Battleship-style math game used to teach students the coordinate plane by plotting points in all four quadrants.

22- Marble Math

7 Important Traits of a Great Mobile Learning App

http://blogs.kqed.org/mindshift/2010/12/7-important-traits-of-a-great-mobile-learning-app/

The best learning apps have the following attributes:

1) They’re interactive. Touchscreens beg for touching.

2) They’re designed for shorter playtimes.

3) They’re focused narrowly by age and relevant to what they’re learning and their motor skills.

4) They’re fun, engaging children by making them laugh — but too too much.

5) They’re inter-generational, allowing a way to involve parents.

6) They’re modifiable, giving kids options to personalize.

7) They have built-in goals, to keep kids coming back with incentives.

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