Digital Literacy: An Interview with Doug Belshaw

Twenty-first century life is fueled by information technology facilitating our actions and communication. Recognizing technology's usefulness as well as its limitations, technical skills related to varied forms of information technology use have become necessary competencies for citizenry, success in reaching educational goals and participation in the workforce. We all need to be digital literate – but are we clear what this means?
Doug Belshaw: ‘Literacy is a condition, a way of being, not a threshold or a bar to cross’ (Image by Travis Miller).

Doug Belshaw: ‘Literacy is a condition, a way of being, not a threshold or a bar to cross’ (Image by Travis Miller, flickr creative commons).

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Designing Assessment

Assessment plays a vital role in delivering, evaluating, monitoring, improving and shaping learning experiences on the Web, at the desk and in the classroom. In the process of orchestrating educational technologies instructional designers are often confronted with the challenge of designing or deploying creative and authentic assessment techniques. assessment Read more ›
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My Personal Top 5 Tools for Teaching and Learning

Jane Hart has recently published her 2015 list of the 100 most popular tools for teaching and learning. For the past nine years, Hart generates this list annually by surveying professionals in instructional design and educational technology. As in previous years, social media and Web 2.0 tools dominate the collection, with Twitter being the number one choice. For instructional designers and educational technology researchers alike, Hart’s list is a useful resource to see what people in the field are using and to discover new tools and gadgets. It is also an opportunity to reflect upon one's own personal learning environment. Everyone has individual approaches, needs and preferences when it comes to teaching and learning tools. In my role as an instructional analyst at the University of North Carolina at Chapel Hill, I use and encounter a broad variety of products, tools and services. The infrastructures listed below either influence my everyday work or have the potential to be significant game changer in my work environment. Read more ›
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Help, I Need Somebody – Okay, Google? Help Seeking Offline and Online

online1Online help seeking is ingrained in our daily information behaviour. For the generation ‘Okay Google’ the answer to any question seems to be just one Web search away. However, help seeking is not effortless, but a skill that requires cognitive, metacognitive and social capacities. In the past three decades, researchers have scrutinized the process of face-to-face help seeking in classroom settings from many different angles. The research, to a great extent, investigated two main questions: (a) How do students seek help in classroom contexts, and (b) What factors influence face-to-face help seeking. The influential descriptive model of Nelson-LeGall (1981) comprises five steps:
  1. Become aware of need for help.
  2. Decide to seek help.
  3. Identify potential helpers.
  4. Elicit help.
  5. Evaluate received help.
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Patterns Everywhere? An Interview with Christian Kohls

Design patterns have become popular in the domains of architecture, software design, human computer interaction, Web 2.0, organizational structures, and pedagogy as a way to communicate successful practical knowledge. Patterns capture proven solutions for recurrent problems with respect to fitting contexts. Practitioners and researchers alike have been adopting the pattern approach to document their work, communicate results, facilitate discourses between experts and nonspecialists, formulate new questions and standardize approaches. Christian Kohls has authored several books about patterns, co-organized international conferences (PLOP, EuroPLOP), and published numerous articles on the practical use and epistemological origin of patterns. In the interview we talk about patterns in e-learning, teaching, instructional design and EdTech research. Read more ›
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You Can’t Teach An Old Dog New Tricks? Instructional Support For Adult Learning

The saying, ”You can’t teach an old dog new tricks,” depicts a common view many people implicitly share: Learning is best done young. For instance, the younger you learn a language, the better your chances of success. But is that actually true?

Can old dogs learn new tricks? Only if they want to!

Can old dogs learn new tricks? Only if they want to! (Image by Mark Robinson)

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Book Review: Teaching Crowds

crowd pic small

Crowds. They are everywhere, and generally crowds cause challenges for those that have to manage them. Yet, crowds can be exciting, energetic, and full of creativity. Trying to manage the complexities of a crowd, while harnessing the positive potential of a crowd can be especially tricky in an instructional context.

One of the most exciting (yet daunting) recent pushes in education is the call for using social media and social learning to connect with crowds. Whether this be a MOOC with tens of thousands of people, or teaching a distance course to a smaller group, teaching crowds can be a wonderful challenge. Teaching Crowds: Learning and Social Media teaching crowdsby Jon Dron and Terry Anderson is a recent book aimed to address this current teaching challenge. This book is part of the series, Issues in Distance Education, edited by Terry Anderson and David Wiley. Read more ›

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Stewarding Open Educational Practices: An Interview with Francesca Allegri and Bradley Hemminger

The term 'open educational resources (OER)' was coined in 2002 during a forum held by UNESCO as the open provision of educational resources, enabled by information and communication technologies, for consultation, use and adaptation by a community of users for non-commercial purposes. Since then, the idea of educational material, freely and openly accessible on the Web, has attracted substantial attention.
Open for Education, Image by John Martinez Pavliga

Open for Education, Image by John Martinez Pavliga

In the past five years, the OER movement shifted its focus from creation to reuse and the adoption of sustainable open educational practices. Between 2010 and 2011, the Open Educational Quality Initiative collected 60 case studies of successful OER projects in Europe. In 2014, the “Open Resources: Influence on Learners and Educators” (ORIOLE) project concluded with the book publication ‘Reusing Open Resources’, from which selected chapters are available as a special issue of the Journal of Interactive Media in Education. The organization ‘Lumen Learning’ recently released an interactive dashboard to communicate and share information about the effect of open educational resource (re)use.

The 2015 Horizon report identifies the proliferation of Open Educational Resources (OER) as one of six trends that will accelerate technology adoption in higher education. As OER is gaining traction across campuses, the report predicts an increased acceptance and usage over the next 2-3 years. However, the broader proliferation of OER hinges on effective leadership: “While data shows that some faculty are integrating OER on their own, institutional leadership can reinforce the use of open content”. As Tony Bates observed: “There is a lot of evidence to suggest that the take-up of OERs by instructors is still minimal, other than by those who created the original version”. 

How can institutional leadership foster the use of OER? Which strategies do stewards of open education deploy to disseminate best practices and high-quality material? It was my pleasure to talk to Francesca Allegri and Bradley Hemminger, who are currently implementing an OER initiative at the University of North Carolina at Chapel Hill.

What is your role at UNC Chapel Hill?

Brad: I’m a faculty member in the School of Information and Library Science. One of my major research areas is “Shared Open Scholarship”, and as part of this I’m interested in the role OERs can play in making education more accessible, and I am committed to promoting the reuse of high quality teaching materials. I chair our UNC OER committee, which several of us started in 2012. We are interested in having better support for OERs on the UNC campus. Related to this work, I’ve previously chaired the Electronic Theses and Dissertations committee on campus (which shifted us from print to free electronic dissemination of these materials), and chair of the UNC Scholarly Communications Committee. Fran: I am an Assistant Director (Interim) and Head of User Services at the Health Sciences Library at the University of North Carolina. I became involved in the OER initiative on campus at the invitation of Brad to help plan how the university could be successful in engaging faculty and other instructors in creating and using OER. Our library has been an early and strong proponent of open access to scholarly output and of public access to the published products of federally funded research. The OER initiative seemed to be a very logical extension of those initiatives as well as being tied to our global initiatives to improve access to health information.

What is the scope and goal of your OER initiative?

Brad: We plan to provide a well-developed program of support on campus for faculty who choose to make course improvements, including the use or development of Open Educational Resources as course materials. This program will use expertise in the Libraries, the Center for Faculty Excellence (CFE) and other units on campus.The program has four primary goals:
  1. Improve courses and learning outcomes at UNC
  2. Significantly reduce the cost of educational materials for students taking courses at UNC
  3. Produce open shared course materials that can be utilized by other institutions
  4. Become a visible leader in developing open educational resources, both at the state and national levels

What have you achieved so far, and what are next steps?

Brad: The first step was identifying important participants on campus who were interested in or might want to be involved with OERs, or would be affected by the adoption of OERs on campus, and engaging them in our discussions. Some of the groups we identified are the Center for Faculty Excellence, the University Libraries, UNC Press, the textbook division of Student Stores, ITS/Sakai (course software), Innovate@Carolina, General Administration, and Faculty Council. As a group, we drafted an initial planning document to guide our work.   The next step was surveying similar efforts at other institutions, and identifying what made them successful or not. A library science masters student conducted web site reviews and compiled a comparative spreadsheet and librarians created an online survey which was sent to faculty development, scholarly communications, and health sciences library directors’ listservs. From these conversations and data we evaluated whether there should be a program at UNC supporting OERs, what form it should take, and what challenges we should expect to address.

Fran: One thing we identified from our survey was that successful programs included the library and the faculty development center as critical partners. Our committee felt that, for a number of reasons, the best approach on our campus was a slow growth one, where we could build support on campus from campus units and faculty, have guidelines available (implemented here as a library resource guide http://guides.lib.unc.edu/OER), be sure the infrastructure was in place (for instance having an OER collection in the Carolina Digital Repository with an easy submission mechanism), and develop metrics for measuring success before we begin to promote OERs on campus.

We will begin to officially promote OER support on campus later this year (Fall 2015), including an award program that will annually help a small number of instructors re-examine their courses to incorporate more OERs, or to develop publicly sharable OER content for their courses.   The award program will provide stipends to help offset the costs involved with re-envisioning courses and developing open course content materials.  The UNC Press is connected to this effort by looking at ways to support authors of larger content pieces (like full textbooks).

Do you have a vision of how open educational practices will impact the UNC campus over the next 2-3 years?

Brad: In our discussions, one thing we emphasize is that this is a win/win proposition. With OERs you do not need to convert everyone to using OERs, nor should you (it is not necessarily appropriate for all course materials).   So, it is easy to grow at whatever pace best suits your environment. We believe the uptake will be small in the first few years (a few dozen courses). Early adopters are already doing this; so we are focused on educating instructors who may not be familiar with the OER concept, and what materials may already be available to them. We think, though, at some point in the future, this will snowball into much larger numbers; however this will most likely happen 5-10 years out.

When you look at your own personal learning environment, what part do open educational resources play?

Brad: Because of my research interests in open, shared scholarly discourse, I already follow OER practices. I produce most all of my course materials, and in some cases reuse freely available materials (slides from instructors of similar materials at other institutions, videos that do a good job of conveying important course topics).   I make all of my materials available online, and free to other instructors to use (licensed through Creative Commons).   The one exception that I haven’t managed to avoid (yet!) is the Database course I teach where our curriculum uses the same textbook for several courses in sequence.   Excluding that, students (or anyone) can freely access, save, and share my course materials at no cost.

Fran: Librarians are implanted with a sharing chip! All of the instructional materials we create here at the Library are freely available. When we receive requests to use or adapt content we have developed, we only ask for attribution. Unless there is some requirement from an external collaborator to do otherwise, that is how we approach our teaching materials. For me personally, I love to find OER content that I or my colleagues can use or adapt. Much better than recreating the wheel.

One role librarians will play in the UNC-CH OER initiative will be helping faculty find relevant, quality OER’s they can consider using in their teaching . This is a key way that the subject specialist librarians across the libraries can help faculty adopt use of this content. This may also inspire faculty to create or share curriculum materials they develop if librarians identify there is a lack of suitable content in their area of teaching. The librarians can also support faculty sharing efforts, for example, alerting them to the Carolina Digital Repository and submission process, assisting with Creative Commons licensing, and similar help that can preserve faculty’s desired author’s rights and make their contributions discoverable by their peers and students. Contacting a librarian early in the process could save the faculty member’s time, also.

Can you name some of the barriers and enablers for open educational practices that you have encountered in your work at UNC?

Brad:There are a number of barriers. Some of the main ones we have identified include
  • Educating instructors about what OERs are
  • Finding and developing quality materials
  • Intellectual property and copyright concerns
  • Financial income concerns
  • Technological and sustainability questions
To be successful, an initiative of this type needs to anticipate and respond to concerns and challenges such as these. Based on our committee’s research, however, we believe an OER program at UNC has the potential for a huge upside, in terms of impact and publicity. There is little downside, as appropriate infrastructure exists on campus to support OERs. Even if only a small fraction of courses at UNC adopt OERs, this still results in a significant benefit. This program has the potential to greatly impact every North Carolina student’s cost of education and this is a critical time to help students with education costs.

Fran: We also identified enabling factors. These include

  • Availability of a large and rapidly growing pool of OERs to use in creating course materials
  • High prices of traditional textbooks causing demand for more affordable educational materials
  • Instructors’ desire to provide high quality low cost course materials to students
  • Regular discussions of open access issues at faculty meetings and annual program by a campus scholarly communications committee.

From your experience, are students generally aware of or rather oblivious to the open learning opportunities that surround them?

Brad:Up until recently, I think students were less aware of Open Learning as a concept, and the practicality of OERs. During the last few years, and even more so in the near future, I think four factors are causing this to change:
  • Increasingly high prices of college textbooks
  • Familiarity with open concepts (open source software, freely available music/videos, Creative Commons)
  • Environments (YouTube, Facebook, Snapchat, Pinterest) encouraging sharing and reuse
  • Tools (cellphones, cameras, video editing software, presentation software) that facilitate easily producing and sharing freely available content

If you could give one single piece of advice to every faculty member and instructor, what would it be?

Fran:Please contact your subject librarian to learn more about OER and what OER materials and support are available to you!

If you want to learn more about our initiative, consult the UNC-CH campus page on OERs for more information. hemminger Brad Hemminger is an associate professor at the School of Information and Library Science (SILS) at the University of North Carolina. He has a joint appointment in Carolina Center for Genome Sciences. He has a number of areas of research interests including digital scholarship, information seeking, information visualization, user interface design, digital libraries and biomedical health informatics.   He has published over 85 papers, served on several international standards committees, and consulted for a number of companies in the areas of visualization and user interfaces. He serves as a reviewer for over a fifteen journals and conferences.   He currently teaches scholarly communications, databases, biomedical health informatics, information visualization, and data science. He is director the Informatics and Visualization Lab at UNC, part of the Interactive Information Systems Lab, and directs the Center for Research and Development of Digital Libraries.   His current research interests are focused on developing new paradigms for scholarship, publishing, information seeking and use by academics in this digital age. For more information see his website http://ils.unc.edu/bmh/.

allegriFrancesca Allegri, MSLS, is Assistant Director (Interim) of the Health Sciences Library, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina. As Assistant Director, she is determining and implementing user focused strategic initiatives, allocating resources, and advising the Director in these areas. She also is Head of User Services, Health Sciences Library. She manages a strong liaison librarian program and single service point (20 FTEs) and is part of the library’s senior management team. She is also a graduate of the National Library of Medicine/Association of Academic Health Sciences Libraries Leadership Fellows Program. Prior to that, she held two positions in the Health Sciences Library’s administrative unit managing professional librarian recruitment, staff development, planning, and institutional data collection and reporting. She also served four years as Department Head of the education department at the Health Sciences Library and has had leadership experience in campus organizations, such as the University Managers Association and the UNC Network for Clinical Research Professionals. Earlier, Ms. Allegri served as Assistant Head at the University of Illinois Library of the Health Sciences in Urbana, Illinois. She holds an MSLS from the University of Illinois at Urbana-Champaign, Urbana, Illinois.

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A Peek into Text Mining (II): Data Visualization

Many educational technology researchers leverage social media data to answer questions about trends, collaboration or learning networks. If you are not a programmer, you will most likely use existing apps and tools to conduct quantitative data analysis and generate visualizations such as word clouds and clusters. As more and more educators are acknowledging coding as an important digital literacy, this post we will explore some common techniques of statistical data visualization.

In my last posting on text mining, I described how to collect data from Twitter. In this post, I will describe how we can summarize a large set of tweets on a certain topic - for example the latest SITE conference.

Background: Giving structure to your data

Text data, such as tweets, comments or posts usually comes with limited structure, as compared to scores on likert scales. To visualize and quantify the data we have to give it structure in the first place. Suppose we have a character vector as the following:

> texts [1] "I am a member of the XYZ association"
[2] "Please apply for our open position"
[3] "The XYZ memorial lecture takes place on wednesday"
[4] "Vote for the most popular lecturer!"

What is a character vector? You can think of a character vector as a container of all text pieces. Each piece represents the text from an individual, and is assigned a number. You can access any piece by using its given number. This type of data is easy for humans to read, but not for machines. Machine prefers the same information structured in the following way:

matrix

A text file structured in this way is called document-term matrix. Each row in the matrix represents a word, while each column represents a document, which refers to all the texts from an individual. Each element in the matrix represents the number of times a particular word appears in a particular document. You may have noticed that all texts have been converted to lowercase in this matrix, while some words, like “a” or “the” are not shown up in the matrix.

To convert the tweet texts you collect into a document-term matrix, the following steps are usually necessary:

  1. Remove nonsense characters
  2. Convert all words to lowercase.
  3. Remove stop words, such as “a”, “an”, “that” and “the”.
As you can see, by delineating the text into single words, its meaning may change significantly. This is why it oftentimes makes sense to combine qualitative and quantitative approaches when analyzing data sets - simply looking at a word cloud is not a replacement for meaningful analysis of qualitative text data.

Sample Data - Tweets on #siteconf

Did you miss your favorite AACE conference? Would you like to find out what predominant topics people discussed? We collected 709 tweets using the hashtags "#siteconf".

Step 1: Word Clouds

To take a quick look at our data, an initial visual representation with world clouds is helpful.

wordcloudAs you can see, the word clouds present us some key information as well as a lot of noise. We can spot some popular topics at a glance, but it is impossible to see how concepts are related.

Step 2: Cluster Tree

A more structured way to explore the data in an associational sense is to look at the collection of terms that frequently co-occur. This method is called cluster analysis.

Cluster analysis is a way of finding association between items and bind nearby items into groups. A typical visualization technique is a tree diagram called dendrogram. The most common cluster analysis include K-means clustering and hierarchical clustering. K-means clustering require you to specify how many groups you prefer to have in the result before the analysis, while hierarchical clustering doesn’t have this requirement.

dendrogram

The density and shape of the dendrogram may vary depending on the sparsity. The above one is the dendrogram on sparsity .95. It is interesting that when people tweeted using the hashtag “#msueped”, they also tended to use “#site2015”. “#msueped” stands for Educational Psychology and Educational Technology from Michigan State University. You can tell that many people from this program went to SITE 2015 conference.

Conclusion

Did you gain a sense what the SITE community is talking about? Data visualization is certainly helpful to make sense of large datasets as it allows you to gain an overview from an elevated perspective. However, don’t mistake a set of images for the real thing. If you attended SITE 2015 in Las Vegas, your first hand experience is likely to be totally different and certainly more in-depth. Also keep in mind that while social media is becoming ever more popular, Twitter users are still only a sub-group of the whole audience.

No approach is neutral in its analysis: Understanding the tools that we use helps us to interpret seemingly obvious connections more carefully. If you want to explore how we produced these visualizations use our sample data set with instructions.

   

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Data Visualization with R – Step-by-Step Instructions

This post covers the details of how to use R to generate data visualizations. We will use a sample data set that includes about 800 tweets using hashtag “#edutech” for the purpose of explanation. To learn how this data set was collected read my post A Peek Into Text Mining: How To Collect Data From Twitter. Text data has to be converted into a document-term matrix for analysis. To convert our sample data set into a document-term matrix, you need to do the following things:
  1. Copy and paste all the codes in the file termDocumentMatrixConverter.R to your console of R, and run the codes.
  2. Run the following code in R console. Please remember to replace the filename with the name of your file (without csv suffix).

data <- vectorConvertor("edutech", TRUE) data <- plainTextDocumentConverter(data) data.tm <- TermDocumentMatrix(data)

Now your document-term matrix is saved in a variable called data.tm for future use.

Word Cloud

A word cloud is very helpful if you want to take a quick look at your data. To generate a word cloud, please run the following code in your R console:

# word cloud install.packages("wordcloud") library(wordcloud) # word cloud function can only be run on PlainTextDocument wordcloud(data)

This code will generate a word cloud. The generation of the word cloud may take some time. wordcloud2

Cluster Tree

Cluster analysis is a way of finding association between items and bind nearby items into groups. A typical visualization technique is a tree diagram called dendrogram. Before applying hierarchical clustering to the data, we will need to remove the the terms that only appear once. When we get the clusters, we will need to plot it to see the dendrogram. All in all, run the codes in hclusterofwords.R file first, and then run the following code in your R console.

hclusterofwords(data.tm)

Your dendrogram may look like this: cluster To learn more about clustering analysis visit the open access book and website The Elements of statistical learning.
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