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.
First, the learner has to realize that she or he needs assistance to overcome difficulties. Second, the learner has to decide whether to seek help or exhaust all available information. Third, once  he or she has decided to seek help, the learner has to find potential helpers. Fourth, the learner needs to approach potential helpers and request their help. Fifth, the learner needs to assess whether the help was useful in problem solving and determine whether or not more help is needed. online2Research on factors influencing help seeking revealed possible challenges along the way. Being or becoming aware that one needs help takes major metacognitive efforts, including evaluation and self-assessment. The decision to seek help is influenced by concerns of being labelled as incompetent. Eliciting help requires learners to be strategic about inquiries and have basic communication skills. Online environments are much more ubiquitous and open than classroom contexts, which give learners opportunities to take advantage of abundant resources on the Internet and to seek help from experts around the globe. Most importantly, online help seeking requires intensive cognitive efforts in raising questions or forming queries. Human helpers are highly adaptive to the needs of learners in face-to-face contexts: They can figure out what is going on even if the learners are not clear about their problems, or cannot organize their statements to give an unambiguous question. Therefore, traditionally, few researchers looked into how learners raise questions. Search engines on the other hand have very limited adaptivity, so ambiguous questions can hardly be answered. Similarly, people in online communities often lack context information and situational cues to figure out what the help seeker actually means. Therefore, learners usually need to segment big problems into subproblems with specific goals, decontextualize the subproblems for people who have little background knowledge, and convert the problems to either specific questions or queries. In other words, effective online help seeking requires logical thinking, discourse skills, knowledge of search engines and strong problem-solving skills. Unfortunately, learners may not be aware that they need to organize their thinking, or activate their discourse skills to form questions for inquiry, because the solution is apparently only one mouse click away. The challenges students face in online help seeking are new and need deliberate training. Investigating students’ online help seeking behavior and design effective trainings to foster this skill is an important prerequisite of both formal and informal learning online.
Posted in AACE Tagged with: ,

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.


"What’s the secret of a good chef? He knows basic recipes and ingredients to prepare a million different meals by combining these. That's how patterns work". (Image Source: Nicole Abalde, Flickr Commons)

In a nutshell, what are patterns and how can instructional designers use them?

Patterns are a specific way to capture best practices, such as e-learning methods, assessment types, media formats, forms of collaboration etc. What makes them special is that they are on a mid-level of abstraction offering both practical guidance and theoretical justification. A pattern is a specific solution which instructional designer can reuse and adopt to specific needs. The pattern description explains why, when, and how the solution can be applied.

What are the most relevant patterns in the field of e-learning?

The most relevant e-learning patterns are about educational videos and social learning. A lot of video material is produced at the moment but it’s not always appropriate. Everyone can produce videos today but not all of them are effective and efficient. This is a typical example where the elaborate description format of patterns can help instructional designers: choose the right format (when to use a lecture recording, a webinar, a screencast, or a commons craft style animation), adopt the content accordingly, and make a professional production with limited resources.

Social learning is very often student-initiated. However, instructional designers have to think about when and how to integrate these learning activities into the course design: how can we stimulate online collaboration and learning communities? How can learning analytics be used to improve the course design? How can we support and protect students and offer them an open space for experimentation and new ideas? These patterns are just emerging. While there are many opportunities there are also many drawbacks (such as high drop-out rates or a digital divide). That’s another important thing about patterns: they do not only highlight the beneficial aspects but the negative consequences as well.

As a professor, you are teaching software programming and computational science classes. Do you use patterns in the classroom?

Yes, of course! I do that in several ways. Patterns are a very well established approach in software design. So I am teaching these technical patterns to my students.

I am also using educational patterns for planning my courses. That includes patterns for assessment driven course design, the use of audience response systems and digital whiteboards, and the production of screencasts for my entire lecture on object oriented programming. Patterns help me to reflect about my own instructional design. Instead of just recoding my live lecture I produced and edited screencasts with similar content. This was quite a time investment but allowed me to have more student interaction in the lecture hall and use many different media types. Having pattern-oriented mind lets you weigh the pros and cons of each solution in a systematic way.

Most exciting for me, however, are my courses on e-learning patterns where I ask students to write their own patterns based on their experiences.

Do you have some general advice for integrating patterns in teaching?

Teachers can use patterns as inspiration and to detect problems they were not even aware of having. Both the problem and the solution part of a pattern description are very important. The solution part is obvious: it provides guidance to good designs and it can help instructional designers without prescribing scripted steps. Yet the problem statement is just as important because it can serve as some sort of a wake-up call. It is one thing to address problems you are aware of: you can find your own solution or use well-known patterns. But if you are not even aware of the problem you will never solve it.

How do your students respond to patterns?

Oh, they like them as solutions. That’s especially true for the software patterns since they provide good design tricks and release some of the burden of finding a robust and flexible architecture when programming. When it comes to students writing their own patterns, this is a different matter. The pattern format is very strict and it requires that the student reflect about his or her own practices. Sometimes we do certain activities naturally, such as forming learning groups online. One can easily identify this as a best practice. However, it is much harder to explain why and when this is more effective than learning alone. They need to find evidence that this is not just a subjective feeling, they need to find examples and counter-examples, etc. Pattern writing is quite difficult for students, but it offers many learning moments.

What is the best way to get involved with patterns?

Finding patterns in the world is the most natural thing every person does. Without pattern recognition we wouldn’t be able to identify other persons, social behavior, or even scientific laws. We have patterns in our heads! What the pattern community does is to search for patterns in successful designs. There are several pattern conferences around the world (PLoP conferenes) and the community is very open to newcomers. If you have some best practices in mind: just start writing a pattern today. You can find several starter kits for writing patterns on the websites of the pattern community ( Writing your own patterns is already an exciting experience. Once you have your first draft ready, don’t hesitate to submit it to one of the pattern conferences. Each submission will go through a mentoring process (“shepherding”) and you will get constructive feedback in Writers’ Workshops at the conferences.


Prof. Dr. CKohlshristian Kohls is an expert on patterns, e-learning, creativity, software design and software engineering. He is a professor of computational science at Cologne University of Applied Sciences, Germany. Prior to his current position, he worked as an international consultant at SMART technologies and as researcher and developer at the Knowledge Media Research Center. Christian Kohls holds a PhD from the University of Eichstätt-Ingolstadt, with a thesis about mental and conceptual representations of patterns. He holds a master’s degree of media and computer science from the University of Applied Sciences Wedel/Hamburg. He worked as consultant at pharus53 software solutions and implemented multilingual wbt solutions and software tutorials. He is inventor and development coordinator of moowinx, an end user tool to create interactive graphics.  
Posted in AACE

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)

Read more ›

Posted in AACE

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 ›

Posted in AACE Tagged with: , , ,

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, 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

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.

Posted in AACE

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:


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.


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.


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.


Posted in AACE, SITE Tagged with: , , ,

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) <- TermDocumentMatrix(data)

Now your document-term matrix is saved in a variable called 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.


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.
Posted in AACE Tagged with: , ,

A Peek into Text Mining: How to Collect Text Data from Twitter

In the last 25 years, the Internet has fundamentally changed the way we interact with each other. In 1993 there were only 50 static pages on the World Wide Web. Today, social networking tools alone have billions of active users.


Communication through social networking tools is both bidirectional and many-to-many at the same time. We can keep contact with our friends, friends of friends, and any number of people with shared interests. In these networks, a piece of information can easily travel along many different paths and have unforeseen impact.

Text Mining

These changing communication patterns coincide with new frontiers for academic research. 30 years ago, text mining did not exist as an independent academic field. Text data sets were expensive, and machines were not powerful enough to store or sort large amounts of text information. Today, researchers in the broad area of natural language processing list text analysis as one of the most important research areas. Text analysis is not only a challenging problem, but also a powerful tool that has been employed in diverse fields such as business, humanities and health sciences. Education is not an exception. Online activities are increasingly integrated into classroom learning, more and more people are using open educational resources and students worldwide connect through online learning communities. The resulting communication streams offer a vast amount of material for analysis.

Anyone Can Explore Big Data

Despite the potential, many educational researchers are unaware about how relatively easy it is to collect big data from social networking sites and how to process it. This post offers a basic introduction to educational researchers interested in text analysis on social networking tools and focuses on data collection from Twitter. Though data from Twitter is not fundamentally different from data from other social media networks, Twitter has unique characteristics that make it particularly interesting for text mining. On the one hand, weak-tie connection among people on Twitter is stronger than other networks, which greatly increases information exposure. On the other hand, Twitter has word limits on each tweet. Users tend to use precise rather than artful language when faced with this limit, which makes connections more obvious. To collect my data set, I am using R, an open source language and environment for statistical analysis. The following step-by-step instructions enable you to collect your own data set.

Download and Start Using R

If you are using Mac, please go to, click “download R” and follow the rest of the steps until you finish the installation. If you are using Windows, you may want to download R-studio when you finish installing R. R-studio provides a more user-friendly interface for Windows users.

The only knowledge that you need about R for now is the concept of working directory. R is capable of reading from and writing to a specific folder of your system, and the specific folder is the working directory to R. You can use the following command to check the current working directory of R:


To specify which directory you would like R to use as the working directory, you can use setwd() command. The following example tells R to use C:/ as the working directory.


BlogPost3-6 BlogPost3-5

Data Collection Approach 1: Popular hashtags

Some Twitter hashtags are very popular, and different people around the world keep tweeting using these hashtags constantly, like #elearning or #edutech. Some other hashtags, in contrast, may not be as popular, but more relevant and meaningful to a specific community, like the hashtag for SITE conference #siteconf. For tweets with the two different types of hashtags, Twitter weighs and indexes them differently, which requires different approaches for data collection. This section discusses how to collect information for a popular hashtag, using the example #edutech.

Create a Developer Account and Application on Twitter

To collect data from Twitter, you will need a developer account on Twitter first. You can register one at Once you have a developer account, return to the page and scroll down to the bottom of the page, click “Manage Your Apps” under “Tools”.


Now, simply click on “Create New Application” button on the following new page:


On the application creation page, the only thing you need to remember is to fill the Callback URL as


When you finish the creation step, you can check the details of your application:


The generated consumer keys and secrets would be under the tab “Keys and Access Token”. This piece of information will be important for you to successfully connect to Twitter later on.

Connection and Data Collection

If you have finished the installation of R and figured out what working directory is, then you can march ahead towards data collection by connecting to Twitter using R. One reason I love R is that it has a very active community. No matter what statistical calculation you need to do, or what common function you need to run, there’s always a package out there online. A package is a collection of R functions that make your life easier. Instead of writing your own functions for a purpose, you can instead just use the function coded by other people, in this case, a package called “twitteR” that implements Twitter’s APIs and can greatly simplify the code for connecting to Twitter. If you want to know more about the package, please check its manual here.

I am curating a collection of functions and detailed explanations on Github. If you have a Github account, please feel free to watch the progress of the functions. I am always trying to update them when there is any change in the package.

To connect to Twitter using R, simply copy and paste the codes in the file Authentication.R to your R (or R-studio) console. Please remember to replace the “xxxxx” with your own consumer keys and secrets before running the codes. When R returns the following strings, you will know that you have successfully connected to Twitter.

"Using browser based authentication"

Now you can move on to data collection using the hashtag you are interested in. Please copy and paste the codes in the file hashtagSearch.R, then run it. Type the following sample codes in the console after you run the codes in hashtagSearch.R:

tweetCollect("#statistics", 100, "statistics_from_twitter")

Now you can access your working directory and find a file named statistics_from_twitter.csv. This file contains your data. The above code simply tells R to collect 100 tweets using the following hashtag: #statistics. You can replace the hashtag with whatever you like to explore, and you can also increase or decrease the number of tweets to collect.

Data Collection Approach 2: Specific hashtags

If the tweets you would like to collect are not using constantly popular hashtag, the first thing you need to do is to search the hashtag using Twitter’s search function. If we would like to, for example, collect most tweets about SITE conference in recent two years using the hashtag “#siteconf”, we can just search the hashtag: Capture

Only most recent data is shown on this page, because Twitter is implementing infinite scrolling. What you need to do is to keep scrolling the page until all the tweets in recent two years show up on one page, and then you can save the HTML page to the working directory of R.

Data Processing

Technically speaking, the data collection is already finished. However, you still have to process the data before it can be used for future analysis. The goal is to format the tweets in two columns. One column represents the original tweets, while the other represents the processed tweets without the hashtag and hyperlinks. Each row represents tweets from an individual.

To process the data, copy and paste the codes in the file parse_Tweets_simplified.R, then run it in R. After that, type the following sample codes in the console:

getData("#siteconf", "#siteconf - Twitter Search.html", "siteconf_from_twitter.csv")

Now go to your current working directory and find a file named siteconf_from_twitter.csv, and that’s your data. The above code simply tells R to parse the tweets in the HTML file into your specified csv file. You can replace the hashtag with whatever you like to explore, and repeat all the above steps in this section.

What next? This is a series of two postings on text mining. Watch for my next post on how to conduct data visualization and further analysis. If you are interested in learning more about R, this is a list of recommended books.

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Learning from Video Games: An Interview with Best Paper Award Winner Eddie Gose

GoseThe use of video games for education is not new. There are many that have issued the call to use serious games for learning and research. Yet many instructional designers are not ready to give their full blessing on the use of video games for learning.

Perhaps this is where the work of researchers such as Eddie Gose comes in. Eddie Gose and Michael Menchaca, of the Educational Technology department at the University of Hawaii presented research on video game research at the E-Learn 2014 Conference.

In their award winning paper, Video Game Genres and What is Learned From Them, the authors describe their research around the possible benefits of defining genres of video games, and the associated learning constructs of video games. The following is an audio only interview with Dr. Gose about his current research, and the experience of winning a paper award at E-Learn. Click below to listen to this audio only interview with Dr. Gose.

Eddie Gose is an Instructional Designer at the Distance Course Design & Consulting group at the University of Hawaii at Manoa. He has a Ph.D. in Education with an emphasis on Educational Technology. His dissertation was on video games and learning. Other interests include learning through new media technologies.

Gose, E., & Menchaca, M. (2014, October). Video Game Genres and What is Learned From Them. In World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (Vol. 2014, No. 1, pp. 673-679).
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Let’s Talk About Flipping: An Interview With Matt Osment

Lately, flipping the classroom has become an educational imperative on many campuses.
Flipped learning reverses the traditional classroom approach to teaching and learning. It moves direct instruction into the learner’s own space. At home, or in individual study time, students watch video lectures that offer them opportunities to work at their own pace, pausing to make notes where necessary. This allows time in class to be spent on activities that exercise critical thinking, with the teacher guiding students in creative exploration of the topics they are studying. Flipped learning is sometimes seen simply as a different approach to delivering content. It also offers opportunities for the classroom to become a more flexible environment, where the physical layout can be shifted to enable group work, where students can make use of their own devices, and where new approaches to learning and assessment are put into practice. 2014 Innovating Pedagogy Report
The flipped classroom becomes a space for dynamic, interactive learning. (Image source:  Ffion Atkinson, flickr commons)

The flipped classroom becomes a space for dynamic, interactive learning. (Image source:
Ffion Atkinson, flickr commons)

Flipped Classroom Workshop at E-Learn 2014 - Meet Presenter Matt Osment

To free-up class time for active learning and group work, students need to process content outside of class. Effective instructional videos thus become a central ingredient of flipping. At E-Learn 2014, Matt Osment from the UNC Center for Faculty Excellence addressed the needs of instructional designers and faculty with a workshop on video production for the flipped classroom. Approximately 25 participants spent an afternoon learning the ins and outs of conceptualizing, planning, recording, producing, distributing, sharing and reusing videos for educational purposes.

When you go to conferences such as E-Learn or SITE, do you take advantage of the many workshops that are offered or do you simply attend presentations of papers? Hopefully you already take advantage of the many parts of an academic conference, but if you aren't familiar with how workshops run and are organized, this interview will give you insight in what some of the many benefits are to attending a workshop.

Osment Pic Matt Osment is an instructional designer at the University of North Carolina, Center for Faculty Excellence. His background includes technology curriculum development and instruction for the Adult Technology Education Center and Teen Computer Clubhouse at Boston’s Harriet Tubman House; e-Media project management for Harcourt publishing; and instructional design for St. Edward’s University in Austin, Texas, Master of Advanced Oncology online for Universität Ulm, Germany, and Master of Public Administration online for UNC’s School of Government.

Thinking about offering a workshop? Aloha Kona!

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