Searching for "data"
Three lessons from rigorous research on education technology
Hope seen in “personalized” software for math
http://hechingerreport.org/three-lessons-rigorous-research-education-technology/
an August 2017 working paper, “Education Technology: An Evidence-Based Review,” published by the National Bureau of Economic Research with clear tables on which technology improves learning and which doesn’t.
1. Computers and internet access alone don’t boost learning
Handing out laptops, providing high-speed internet access or buying most other kinds of hardware doesn’t on its own boost academic outcomes. The research shows that student achievement doesn’t rise when kids are using computers more, and it sometimes decreases.
2. Some math software shows promise
math programs such as SimCalc and ASSISTments. One popular program, DreamBox, showed small gains for students, as well. Only one piece of software that taught reading, Intelligent Tutoring for the Structure Strategy (ITSS), showed promise, suggesting that it is possible to create good educational software outside of math, but it’s a lot harder.
One commonality of the software that seems to work is that it somehow “personalizes” instruction. Sometimes students start with a pre-test so the computer can determine what they don’t know and then sends each student the right lessons, or a series of worksheet problems, to help fill in the gaps. Other times, the computer ascertains a student’s gaps as he works through problems and makes mistakes, giving personalized feedback. Teachers also get data reports to help pinpoint where students are struggling.
3. Cheap can be effective
a study in San Francisco where texts reminded mothers to read to their preschoolers. That boosted children’s literacy scores.
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more on educational technology in this IMS blog
https://blog.stcloudstate.edu/ims?s=education+technology
My note: no, it is not
Tech’s push to teach coding isn’t about kids’ success – it’s about cutting wages
My note: it is NOT about creating masses of programmers and driving the salaries down, as the author claims; it is about fostering a generation, which is technology literate. A doctor, knowing how to code will be a better doctor in the era of IoT; a philosopher knowing how to code will be better in the era of digital humanities.
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more on coding in this IMS blog
https://blog.stcloudstate.edu/ims?s=coding
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more on RSS in this IMS blog
https://blog.stcloudstate.edu/ims?s=rss
‘School For Good And Evil’ Is A Kids’ Fantasy Series For The Fake News Era
There’s a YouTube channel, an interactive website with t-shirt giveaways and character contests, Instagrams, dramatic book trailers. Universal Pictures bought the rights to the series pretty much as soon as the first book was published.
The power of a lie that feels true and drives people’s behavior is at the heart of the book — a theme that feels very now.
Bibliographic Indexing Leader
Register for the September 28th webinar
https://www.brighttalk.com/webcast/13703/275301
metadata: counts of papers by yer, researcher, institution, province, region and country. scientific fields subfields
metadata in one-credit course as a topic:
publisher – suppliers =- Elsevier processes – Scopus Data
h-index: The h-index is an author-level metric that attempts to measure both the productivity and citation impact of the publications of a scientist or scholar. The index is based on the set of the scientist’s most cited papers and the number of citations that they have received in other publications.
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https://www.brighttalk.com/webcast/9995/275813
Librarians and APIs 101: overview and use cases
Christina Harlow, Library Data Specialist;Jonathan Hartmann, Georgetown Univ Medical Center; Robert Phillips, Univ of Florida
https://zenodo.org/
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The era of e-science demands new skill sets and competencies of researchers to ensure their work is accessible, discoverable and reusable. Librarians are naturally positioned to assist in this education as part of their liaison and information literacy services.
Research data literacy and the library
Christian Lauersen, University of Copenhagen; Sarah Wright, Cornell University; Anita de Waard, Elsevier
https://www.brighttalk.com/webcast/9995/226043
Data Literacy: access, assess, manipulate, summarize and present data
Statistical Literacy: think critically about basic stats in everyday media
Information Literacy: think critically about concepts; read, interpret, evaluate information
data information literacy: the ability to use, understand and manage data. the skills needed through the whole data life cycle.
Shield, Milo. “Information literacy, statistical literacy and data literacy.” I ASSIST Quarterly 28. 2/3 (2004): 6-11.
Carlson, J., Fosmire, M., Miller, C. C., & Nelson, M. S. (2011). Determining data information literacy needs: A study of students and research faculty. Portal: Libraries & the Academy, 11(2), 629-657.
embedded librarianship,
Courses developed: NTRESS 6600 research data management seminar. six sessions, one-credit mini course
http://guides.library.cornell.edu/ntres6600
BIOG 3020: Seminar in Research skills for biologists; one-credit semester long for undergrads. data management organization http://guides.library.cornell.edu/BIOG3020
lessons learned:
- lack of formal training for students working with data.
- faculty assumed that students have or should have acquired the competencies earlier
- students were considered lacking in these competencies
- the competencies were almost universally considered important by students and faculty interviewed
http://www.datainfolit.org/
http://www.thepress.purdue.edu/titles/format/9781612493527
ideas behind data information literacy, such as the twelve data competencies.
http://blogs.lib.purdue.edu/dil/the-twelve-dil-competencies/
http://blogs.lib.purdue.edu/dil/what-is-data-information-literacy/
Johnston, L., & Carlson, J. (2015). Data Information Literacy : Librarians, Data and the Education of a New Generation of Researchers. Ashland: Purdue University Press. http://login.libproxy.stcloudstate.edu/login?qurl=http%3a%2f%2fsearch.ebscohost.com%2flogin.aspx%3fdirect%3dtrue%26db%3dnlebk%26AN%3d987172%26site%3dehost-live%26scope%3dsite
NEW ROLESFOR LIbRARIANS: DATAMANAgEMENTAND CURATION
the capacity to manage and curate research data has not kept pace with the ability to produce them (Hey & Hey, 2006). In recognition of this gap, the NSF and other funding agencies are now mandating that every grant proposal must include a DMP (NSF, 2010). These mandates highlight the benefits of producing well-described data that can be shared, understood, and reused by oth-ers, but they generally offer little in the way of guidance or instruction on how to address the inherent issues and challenges researchers face in complying. Even with increasing expecta-tions from funding agencies and research com-munities, such as the announcement by the White House for all federal funding agencies to better share research data (Holdren, 2013), the lack of data curation services tailored for the “small sciences,” the single investigators or small labs that typically comprise science prac-tice at universities, has been identified as a bar-rier in making research data more widely avail-able (Cragin, Palmer, Carlson, & Witt, 2010).Academic libraries, which support the re-search and teaching activities of their home institutions, are recognizing the need to de-velop services and resources in support of the evolving demands of the information age. The curation of research data is an area that librar-ians are well suited to address, and a num-ber of academic libraries are taking action to build capacity in this area (Soehner, Steeves, & Ward, 2010)
REIMAgININg AN ExISTINg ROLEOF LIbRARIANS: TEAChINg INFORMATION LITERACY SkILLS
By combining the use-based standards of information literacy with skill development across the whole data life cycle, we sought to support the practices of science by develop-ing a DIL curriculum and providing training for higher education students and research-ers. We increased ca-pacity and enabled comparative work by involving several insti-tutions in developing instruction in DIL. Finally, we grounded the instruction in the real-world needs as articu-lated by active researchers and their students from a variety of fields
Chapter 1 The development of the 12 DIL competencies is explained, and a brief compari-son is performed between DIL and information literacy, as defined by the 2000 ACRL standards.
chapter 2 thinking and approaches toward engaging researchers and students with the 12 competencies, a re-view of the literature on a variety of educational approaches to teaching data management and curation to students, and an articulation of our key assumptions in forming the DIL project.
Chapter 3 Journal of Digital Curation. http://www.ijdc.net/
http://www.dcc.ac.uk/digital-curation
https://blog.stcloudstate.edu/ims/2017/10/19/digital-curation-2/
https://blog.stcloudstate.edu/ims/2016/12/06/digital-curation/
chapter 4 because these lon-gitudinal data cannot be reproduced, acquiring the skills necessary to work with databases and to handle data entry was described as essential. Interventions took place in a classroom set-ting through a spring 2013 semester one-credit course entitled Managing Data to Facilitate Your Research taught by this DIL team.
chapter 5 embedded librar-ian approach of working with the teaching as-sistants (TAs) to develop tools and resources to teach undergraduate students data management skills as a part of their EPICS experience.
Lack of organization and documentation presents a bar-rier to (a) successfully transferring code to new students who will continue its development, (b) delivering code and other project outputs to the community client, and (c) the center ad-ministration’s ability to understand and evalu-ate the impact on student learning.
skill sessions to deliver instruction to team lead-ers, crafted a rubric for measuring the quality of documenting code and other data, served as critics in student design reviews, and attended student lab sessions to observe and consult on student work
chapter 6 Although the faculty researcher had created formal policies on data management practices for his lab, this case study demonstrated that students’ adherence to these guidelines was limited at best. Similar patterns arose in discus-sions concerning the quality of metadata. This case study addressed a situation in which stu-dents are at least somewhat aware of the need to manage their data;
chapter 7 University of Minnesota team to design and implement a hybrid course to teach DIL com-petencies to graduate students in civil engi-neering.
stu-dents’ abilities to understand and track issues affecting the quality of the data, the transfer of data from their custody to the custody of the lab upon graduation, and the steps neces-sary to maintain the value and utility of the data over time.
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more on Scopus in this IMS blog
https://blog.stcloudstate.edu/ims?s=scopus
How algorithms impact our browsing behavior? browsing history?
What is the connection between social media algorithms and fake news?
Are there topic-detection algorithms as they are community-detection ones?
How can I change the content of a [Google] search return? Can I?
Larson, S. (2016, July 8). What is an Algorithm and How Does it Affect You?
The Daily Dot. Retrieved from
https://www.dailydot.com/debug/what-is-an-algorithm/
Johnson, C. (2017, March 10). How algorithms affect our way of life.
Desert News. Retrieved from
https://www.deseretnews.com/article/865675141/How-algorithms-affect-our-way-of-life.html
Understanding algorithms and their impact on human life goes far beyond basic digital literacy, some experts said.
An example could be the recent outcry over Facebook’s news algorithm, which enhances the so-called
“filter bubble”of information.
gender
Massanari, A. (2017). #Gamergate and The Fappening: How Reddit’s algorithm, governance, and culture support toxic technocultures. New Media & Society, 19(3), 329-346. doi:10.1177/1461444815608807
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community detection algorithms:
Bedi, P., & Sharma, C. (2016). Community detection in social networks. Wires: Data Mining & Knowledge Discovery, 6(3), 115-135.
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CRUZ, J. D., BOTHOREL, C., & POULET, F. (2014). Community Detection and Visualization in Social Networks: Integrating Structural and Semantic Information. ACM Transactions On Intelligent Systems & Technology, 5(1), 1-26. doi:10.1145/2542182.2542193
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Bai, X., Yang, P., & Shi, X. (2017). An overlapping community detection algorithm based on density peaks. Neurocomputing, 2267-15. doi:10.1016/j.neucom.2016.11.019
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topic-detection algorithms:
Zeng, J., & Zhang, S. (2009). Incorporating topic transition in topic detection and tracking algorithms. Expert Systems With Applications, 36(1), 227-232. doi:10.1016/j.eswa.2007.09.013
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topic detection and tracking (TDT) algorithms based on topic models, such as LDA, pLSI (https://en.wikipedia.org/wiki/Probabilistic_latent_semantic_analysis), etc.
Zhou, E., Zhong, N., & Li, Y. (2014). Extracting news blog hot topics based on the W2T Methodology. World Wide Web, 17(3), 377-404. doi:10.1007/s11280-013-0207-7
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The W2T (Wisdom Web of Things) methodology considers the information organization and management from the perspective of Web services, which contributes to a deep understanding of online phenomena such as users’ behaviors and comments in e-commerce platforms and online social networks. (https://link.springer.com/chapter/10.1007/978-3-319-44198-6_10)
ethics of algorithm
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate.
Big Data & Society,
3(2), 2053951716679679.
https://doi.org/10.1177/2053951716679679
journalism
Malyarov, N. (2016, October 18). Journalism in the age of algorithms, platforms and newsfeeds | News | FIPP.com. Retrieved September 19, 2017, from
http://www.fipp.com/news/features/journalism-in-the-age-of-algorithms-platforms-newsfeeds
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https://blog.stcloudstate.edu/ims?s=algorithm
more on algorithms in this IMS blog
see also
THE VALUE OF ACADEMIC LIBRARIES
A Comprehensive Research Review and Report. Megan Oakleaf
http://www.ala.org/acrl/sites/ala.org.acrl/files/content/issues/value/val_report.pdf
Librarians in universities, colleges, and community colleges can establish, assess, and link
academic library outcomes to institutional outcomes related to the following areas:
student enrollment, student retention and graduation rates, student success, student
achievement, student learning, student engagement, faculty research productivity,
faculty teaching, service, and overarching institutional quality.
Assessment management systems help higher education educators, including librarians, manage their outcomes, record and maintain data on each outcome, facilitate connections to
similar outcomes throughout an institution, and generate reports.
Assessment management systems are helpful for documenting progress toward
strategic/organizational goals, but their real strength lies in managing learning
outcomes assessments.
to determine the impact of library interactions on users, libraries can collect data on how individual users engage with library resources and services.
increase library impact on student enrollment.
p. 13-14improved student retention and graduation rates. High -impact practices include: first -year seminars and experiences, common intellectual experiences, learning communities, writing – intensive courses, collaborative assignments and projects, undergraduate research, Value of Academic Libraries diversity/global learning, service learning/community -based learning, internships, capstone courses and projects
p. 14
Libraries support students’ ability to do well in internships, secure job placements, earn salaries, gain acceptance to graduate/professional schools, and obtain marketable skills.
librarians can investigate correlations between student library interactions and their GPA well as conduct test item audits of major professional/educational tests to determine correlations between library services or resources and specific test items.
p. 15 Review course content, readings, reserves, and assignments.
Track and increase library contributions to faculty research productivity.
Continue to investigate library impact on faculty grant proposals and funding, a means of generating institutional income. Librarians contribute to faculty grant proposals in a number of ways.
Demonstrate and improve library support of faculty teaching.
p. 20 Internal Focus: ROI – lib value = perceived benefits / perceived costs
production of a commodity – value=quantity of commodity produced × price per unit of commodity
p. 21 External focus
a fourth definition of value focuses on library impact on users. It asks, “What is the library trying to achieve? How can librarians tell if they have made a difference?” In universities, colleges, and community colleges, libraries impact learning, teaching, research, and service. A main method for measuring impact is to “observe what the [users] are actually doing and what they are producing as a result”
A fifth definition of value is based on user perceptions of the library in relation to competing alternatives. A related definition is “desired value” or “what a [user] wants to have happen when interacting with a [library] and/or using a [library’s] product or service” (Flint, Woodruff and Fisher Gardial 2002) . Both “impact” and “competing alternatives” approaches to value require libraries to gain new understanding of their users’ goals as well as the results of their interactions with academic libraries.
p. 23 Increasingly, academic library value is linked to service, rather than products. Because information products are generally produced outside of libraries, library value is increasingly invested in service aspects and librarian expertise.
service delivery supported by librarian expertise is an important library value.
p. 25 methodology based only on literature? weak!
p. 26 review and analysis of the literature: language and literature are old (e.g. educational administrators vs ed leaders).
G government often sees higher education as unresponsive to these economic demands. Other stakeholder groups —students, pa rents, communities, employers, and graduate/professional schools —expect higher education to make impacts in ways that are not primarily financial.
p. 29
Because institutional missions vary (Keeling, et al. 2008, 86; Fraser, McClure and
Leahy 2002, 512), the methods by which academic libraries contribute value vary as
well. Consequently, each academic library must determine the unique ways in which they contribute to the mission of their institution and use that information to guide planning and decision making (Hernon and Altman, Assessing Service Quality 1998, 31) . For example, the University of Minnesota Libraries has rewritten their mission and vision to increase alignment with their overarching institution’s goals and emphasis on strategic engagement (Lougee 2009, allow institutional missions to guide library assessment
Assessment vs. Research
In community colleges, colleges, and universities, assessment is about defining the
purpose of higher education and determining the nature of quality (Astin 1987)
.
Academic libraries serve a number of purposes, often to the point of being
overextended.
Assessment “strives to know…what is” and then uses that information to change the
status quo (Keeling, et al. 2008, 28); in contrast, research is designed to test
hypotheses. Assessment focuses on observations of change; research is concerned with the degree of correlation or causation among variables (Keeling, et al. 2008, 35) . Assessment “virtually always occurs in a political context ,” while research attempts to be apolitical” (Upcraft and Schuh 2002, 19) .
p. 31 Assessment seeks to document observations, but research seeks to prove or disprove ideas. Assessors have to complete assessment projects, even when there are significant design flaws (e.g., resource limitations, time limitations, organizational contexts, design limitations, or political contexts); whereas researchers can start over (Upcraft and Schuh 2002, 19) . Assessors cannot always attain “perfect” studies, but must make do with “good enough” (Upcraft and Schuh 2002, 18) . Of course, assessments should be well planned, be based on clear outcomes (Gorman 2009, 9- 10) , and use appropriate methods (Keeling, et al. 2008, 39) ; but they “must be comfortable with saying ‘after’ as well as ‘as a result of’…experiences” (Ke eling, et al. 2008, 35) .
Two multiple measure approaches are most significant for library assessment: 1) triangulation “where multiple methods are used to find areas of convergence of data from different methods with an aim of overcoming the biases or limitations of data gathered from any one particular method” (Keeling, et al. 2008, 53) and 2) complementary mixed methods , which “seek to use data from multiple methods to build upon each other by clarifying, enhancing, or illuminating findings between or among methods” (Keeling, et al. 2008, 53) .
p. 34 Academic libraries can help higher education institutions retain and graduate students, a keystone part of institutional missions (Mezick 2007, 561) , but the challenge lies in determining how libraries can contribute and then document their contribution
p. 35. Student Engagement: In recent years, academic libraries have been transformed to provide “technology and content ubiquity” as well as individualized support
My Note: I read the “technology and content ubiquity” as digital literacy / metaliteracies, where basic technology instructional sessions (everything that IMS offers for years) is included, but this library still clenches to information literacy only.
p. 37 Student Learning
In the past, academic libraries functioned primarily as information repositories; now they are becoming learning enterprises (Bennett 2009, 194) . This shift requires academic librarians to embed library services and resources in the teaching and learning activities of their institutions (Lewis 2007) . In the new paradigm, librarians focus on information skills, not information access (Bundy 2004, 3); they think like educators, not service providers (Bennett 2009, 194) .
p. 38. For librarians, the main content area of student learning is information literacy; however, they are not alone in their interest in student inform ation literacy skills (Oakleaf, Are They Learning? 2011).
My note: Yep. it was. 20 years ago. Metaliteracies is now.
p. 41 surrogates for student learning in Table 3.
p. 42 strategic planning for learning:
According to Kantor, the university library “exists to benefit the students of the educational institution as individuals ” (Library as an Information Utility 1976 , 101) . In contrast, academic libraries tend to assess learning outcomes using groups of students
p. 45 Assessment Management Systems
Tk20
Each assessment management system has a slightly different set of capabilities. Some guide outcomes creation, some develop rubrics, some score student work, or support student portfolios. All manage, maintain, and report assessment data
p. 46 faculty teaching
However, as online collections grow and discovery tools evolve, that role has become less critical (Schonfeld and Housewright 2010; Housewright and Schonfeld, Ithaka’s 2006 Studies of Key Stakeholders 2008, 256) . Now, libraries serve as research consultants, project managers, technical support professionals, purchasers , and archivists (Housewright, Themes of Change 2009, 256; Case 2008) .
Librarians can count citations of faculty publications (Dominguez 2005)
.
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Tenopir, C. (2012). Beyond usage: measuring library outcomes and value. Library Management, 33(1/2), 5-13.
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methods that can be used to measure the value of library products and services. (Oakleaf, 2010; Tenopir and King, 2007): three main categories
- Implicit value. Measuring usage through downloads or usage logs provide an implicit measure of value. It is assumed that because libraries are used, they are of value to the users. Usage of e-resources is relatively easy to measure on an ongoing basis and is especially useful in collection development decisions and comparison of specific journal titles or use across subject disciplines.
do not show purpose, satisfaction, or outcomes of use (or whether what is downloaded is actually read).
- Explicit methods of measuring value include qualitative interview techniques that ask faculty members, students, or others specifically about the value or outcomes attributed to their use of the library collections or services and surveys or interviews that focus on a specific (critical) incident of use.
- Derived values, such as Return on Investment (ROI), use multiple types of data collected on both the returns (benefits) and the library and user costs (investment) to explain value in monetary terms.
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more on ROI in this IMS blog
https://blog.stcloudstate.edu/ims/2014/11/02/roi-of-social-media/
Qualey, E. e. (2014). What Can Infographics Do for You?. AALL Spectrum, 18(4), 7-8.
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Infographics for Advocacy
Infographics for Marketing
Infographics for Data
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more on inforgraphics in this IMS blog
https://blog.stcloudstate.edu/ims?s=infographics
Feagin, J. R., Orum, A. M., & Sjoberg, G. (1991). A Case for the case study. Chapel Hill: University of North Carolina Press.
https://books.google.com/books/about/A_Case_for_the_Case_Study.html?id=7A39B6ZLyJQC
or ILL MSU,M Memorial Library –General Collection HM48 .C37 1991
p. 2 case study is defined as an in-depth
Multi-faceted investigation, using qualitative research methods, of a single social phenomenon.
use of several data sources.
Some case studies have made use of both qualitative and quantitative methods.
Comparative framework.
The social phenomenon can vary: it can be an organization, it can be a role, or role-occupants.
p. 3Quantitative methods: standardized set of q/s
Demonstrating academic library impact to faculty: a case study
peer-review for the digital library perspective
notes available upon request
library data should focus on “impact”, not “size” to engage faculty.
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more on attrition and retention in academic in this IMS blog
https://blog.stcloudstate.edu/ims?s=retention
