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.

   

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 data data.tm

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.

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.

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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 http://www.r-project.org/, 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:

getwd()

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.

setwd("C:/")

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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 https://dev.twitter.com/. 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”.

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

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On the application creation page, the only thing you need to remember is to fill the Callback URL as http://127.0.0.1:1410.

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

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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.

Posted in AACE Tagged with: , ,

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.

References
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).
Posted in AACE

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!

elc 03.jpg
Posted in AACE Tagged with: , , ,

E-Learn 2014 Outstanding Paper Award Winners: Priscilla Norton and Dawn Hathaway

What does it mean to win a best paper award at an AACE conference? How do researchers decide what conference to attend and where to submit their work? How do researchers implement their research in their own teaching?

These are just some of the questions that will get answered during the following interview with Drs. Priscilla Norton and Dawn Hathaway. Norton and Hathaway who were awarded with Outstanding Paper Awards at the E-Learn 2014 Conference for their paper Using a Design Pattern Framework to Structure Online Course Content: Two Design Cases.  Both are from the Division of Learning Technologies, College of Education and Human Development at George Mason University. Their research of late has focused on the topic of using design patterns to help organize their own courses, particularly in online instruction.

 About Dr. Norton and Dr. Hathaway

NortonPriscilla Norton is a Professor in the College of Education and Human Development at George Mason University. She has been involved with educational technology since the mid 1980’s, working with teachers to understand the role of the newer electronic technologies to support teaching and learning. Dr. Norton is Academic Program Coordinator for the Designing Digital Learning for Schools Certificate, Master’s, and Doctoral Programs as well as the Integration of Online Learning in Schools Certificate, Master’s, and Doctoral Programs. She is the author of numerous articles and two books – Teaching with Technology (2003) and Technology for Teaching (2001). More recently, Dr. Norton has been designing and developing e-learning environments for teachers and high school students resulting in part in The Online Academy – a virtual high school. This program was awarded the 2006 Governor’s Technology Award (COVITS) for Innovative Use of Technology in K-12 Education. In 2007, Dr. Norton was selected as a recipient of the Virginia Outstanding Faculty award sponsored by the State Commission on Higher Education in Virginia (SCHEV) and Dominion Power. Her research interests include design strategies and processes as they influence technology teacher education, online learning environments for both teachers and high school students, and the design of K–12 classroom learning. You can contact Dr. Norton by email at pnorton @ gmu.edu.

Hathaway 2Dawn Hathaway is an assistant professor in the College of Education and Human Development, Graduate School of Education, Division of Learning Technologies at George Mason University. Dr. Hathaway works with K-12 practicing teachers in a Master’s program in Curriculum and Instruction with an emphasis on the Integration of Technology in Schools. As a former School-Based Technology Resource Teacher, she has extensive experience collaborating with classroom teachers to design curriculum that integrates technology to enhance students’ learning experiences. Dr. Hathaway earned her MEd in Curriculum and Instruction and her PhD in Education with an Instructional Technology specialization at George Mason University. She has a robust record of scholarship that includes both qualitative and quantitative methodologies. You can contact Dr. Hathaway by email at dhathawa @ gmu.edu.

 References

Norton, P., & Hathaway, D. (2014, October). Using a Design Pattern Framework to Structure Online Course Content: Two Design Cases. In World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (Vol. 2014, No. 1, pp. 1440-1449).

Posted in AACE, Conferences Tagged with: , , ,

Adventure Learning – Wearable and Mobile Devices: An Interview with Mary Beth Klinger

During SITE 2015, Mike Searson offered conference participants a unique experience: Exploring the Red Rock Canyon Park and delving into the possibilities of mobile and wearable technologies at the same time.

As immediate past president of SITE—Society for Information Technology and Teacher Education, executive director of the School for Global Education & Innovation at Kean University and member of the Education Advisory Board for the US National Parks Service, Mike Searson was perfectly positioned to guide this workshop activity.

Prior to the workshop, Mike explained what he hoped that participants would take away from the experience.

“If nothing more happens than enjoying the beauty and surroundings of Red Rock Canyon, we would have had a rich experience. However, if we use the mobile and wearable devices that we carry around with us on a daily basis to collect data and document our experiences, then our trip to the park will become more engaging. Finally, […] can data and documentation collected in the Red Rock Canyon with our mobile and wearable devices then be integrated into lesson plans and curriculum standards?” Mike Searson, AACE Blog
The workshop tackled difficult questions that concern all educators who are interested in mobile and wearable learning: How can we best incorporate informal learning experiences into formal classroom activities? How can we address concerns about who might be accessing the data that students produce, and for what reasons? It was my pleasure to follow up with one of the workshop participants, Mary Beth Klinger, Professor of Business at the College of Southern Maryland.
SITE 2015 Workshop: Taking IT Outdoors at the Red Rock Canyon Park

SITE 2015 Workshop: Taking IT Outdoors at the Red Rock Canyon Park

What drove your decision to sign up for this unusual workshop - the encounter with nature, the immersion into new technologies or an interest in place-based education?

I have a strong interest in place-based instruction and wearable technologies. I have had the opportunity to explore Google Glass and incorporate this technology into my instruction.

I have found through the integration of QR codes or even integrating Auras into content that students have the potential to dig deeper into the topics that we are studying. For example, in one of the business courses I teach, a negotiation simulation is incorporated that revolves around a current international issue. In the resources provided to students, Auras is incorporated to have students explore more deeply the countries and customs. I have found that this adds to the overall experience for students.

I was looking for new ways that I could enhance these mobile technologies and ideas into my current teaching practice.

Give us a look behind the scenes: Can you describe your day at Red Rock Canyon Park?

After I took in the overall beauty of Red Rock Canyon Park, I began to think about experiential learning and the idea of providing students with meaningful experiences.

One way to do this is to explore locations and even interview people with an attempt to create an experience that students can participate in to learn complex topics more deeply.

I have not yet determined what this will look like, but the day at Red Rock with colleagues from around the world discussing these types of ideas was motivational.

Any anecdotes of mishaps, adventurous encounters or unexpected discoveries you would like to share?

Cell phone service was not very reliable, so in remote areas this would need to be accounted for. But GPS was working and that was interesting. I enjoyed being at the Canyon with everyone and exploring and ultimately thinking about these ideas.

I believe students would also benefit from this type of experience. The ability to find ways to provide these rich experiences to them is always a goal. This experience brought that to my attention again.

After the workshop, has your attitude towards mobile and wearable technologies changed? Are you rather cautious or eager to try out these devices and apps?

I utilize and attempt to find real world application to incorporate these types of devices into my teaching. So, this experience provided me an opportunity to think even more deeply about these tools and how I can integrate them into my classroom and instruction.

Which other personal lessons did you take home from the workshop?

I am still trying to discover ways to incorporate these tools into my teaching to enhance student learning. Students carry cell phones and many bring laptops to class. If I can discover ways to incorporate these tools into my teaching to get students more involved and excited about what they are learning, I would be thrilled.

This workshop provided a lot to think about and work towards. Primarily due to the connections that I made with the other participants, I found this experience very meaningful.

Does the workshop influence your teaching? Will you take advantage of the opportunities to take learning outdoors via technology with your own students?

I will look for ways to take learning outdoors. As I mentioned earlier, I do believe my students would benefit from this. However, at this time, I am not sure how I would accomplish this task.

Any advice you would like to share with conference workshop organizers?

This experience was meaningful. It is always helpful to have an immersive experience that you can participate in with colleagues from around the world. It provided an excellent opportunity to share information and resources around a learning model incorporating wearable and mobile technologies.

About:

Prof. Mary Beth Klinger

Prof. Mary Beth Klinger

Mary Beth Klinger is a professor of business and management at the College of Southern Maryland in La Plata, MD where she teaches undergraduate courses in business, management, leadership, organizational behavior, small business and entrepreneurship, and marketing. Her research interests are in the areas of knowledge management, leadership, innovation and technology, and global education. She holds a Ph.D. in Organization and Management, a Master’s in Business Administration, and a Master’s in International Management. Her professional background includes educational consulting, employment in private industry in logistics and supply chain management, as well as several federal government agencies, to include the Office of Personnel Management, the U.S. Department of Labor, and the Federal Trade Commission.
Posted in Conferences, SITE Tagged with: , , ,

What Can Educational Researchers Do to Make Their Studies Replicable?

replication

Replication - a rarity in educational research. Can open and social software increase transparency?

Replication is important to science. It helps make science a self-correcting system. Any time a result is surprising, researchers will try to replicate it, to see if the phenomenon is dependable or just random occurrence. Among different fields in social science, education is the one that is most deeply troubled by the rarity of replication. Only around 0.13 percent of education studies published in top 100 educational journals are replications, according to a research conducted by Matthew Makel and Jonathan Plucker at Duke University in 2012. Some educational researchers may argue that education is an exception in which replication doesn’t play an important role. However, it would be impossible to tell which educational theory is built upon foundation of stone, or which upon the foundation of sand. The lack of replication does not only make it difficult to tell dependable phenomenon from random occurrence, but also makes it impossible to identify fraud. Jonathan Plucker said education research had not a single fraud accusation in many years, simply because educational researchers failed to replicate each others’ research. What can we do The rarity of replication in educational research is partially due to “publish or perish” culture in academics. Many editors of high-end journals stress novelty over replicability, though some of them realize this is a problem. Fortunately, some new journals have been set up to encourage the submission of replication studies very recently. The other reason for such rarity is complexity of educational research. Educational studies often involve in some unspoken assumption and unlisted important factors. Difference between participants, learning and teaching settings, and other uncontrollable factors all contribute to the difficulty of replication. For a researcher, to make the study as transparent as possible will definitely help with replication. Detailed description of the study context and participants helps a lot, but the efforts can always go a little bit further. The increasing popularity of social media use and online version control tools can help increase the transparency of many research. Text data with unified hashtag on social media, like Twitter or Facebook can be easily accessed and collected by any interested researchers. GitHub (one popular online version control tool) makes it easier to share the analysis script and refined data online. GitHub page can even easily generate a professional website for the promotion of your research project. github Many educational studies involves activities that require participants to read and write, like studies on cognition, reflection, self-regulated learning or problem-based learning. Many of these activities can be easily migrated from classroom context to online context. For instance, this study is an ongoing research project investigating the effect of a proposed intervention on college students’ goal setting. Participant’s tweets with unified hashtag “edit4020” provide researchers an easy way to access all the text data. Each participant’s public profile can give rich information about the participant him or her self. Different tools that help data collection and text analysis have been developed and applied in other fields, like political science and psychology. For example, this set of scripts focus on data collection from Twitter. To migrate studies from classroom environment to online environment does not only makes the data collection easier, but also makes the studies more transparent. The transparency increases the credibility of your finding, and lowers the difficulty of replication.
Posted in AACE

Who Do You Connect With? Cultivating Your Personal Learning Environment

As the future connects us, how are we handling it? Who do we connect with? As we have all this information out there, what is our role? With all the answers available in networks, what are the most important questions in our field?’ Ann Hill-Duin, E-Learn 2014 Conference Talk

Personal Learning Environments and Networks

Personal learning environments (PLE) are ‘an idea of how individuals approach the task of learning’ (Educause 2009) and describe ‘the activities and milieu of a modern online learner’ (Martindale & Dowdy, 2010). PLEs comprise tools, communities, and services learners use to direct their own learning and pursue educational goals. They migrate the management of learning from the institution to the learner’ (Downes, 2007). Though technology plays an important role in facilitating one’s PLE, the specific tools and environments may shift over time: As smart phones and tablets are more and more widespread, the concept has moved away from centralized, server-based solutions to distributed and portable mobile apps Horizon Report Wiki 2015.

I was first introduced to the concept of PLEs through the Massive Open Online Course ‘Personal Learning Environments, Networks and Knowledge (PLENK 2010)’ – and the discussions in this MOOC still shape my conceptual understanding of personal learning environments – most importantly: PLEs should be considered as an approach, rather than just a specific technology.

Everyone has and has always had a personal learning environment. Looking at just the technology-based components of a PLE will ignore the influences of personal networks, communities, and physical resources on personal learning’. (Larry Phillips, September 2010, PLENK Discussion Board)
Cultivating the personal learning environment is an ongoing task that requires choices not only about learning resources and infrastructures, but also about your learning network. People who are part of an informal social learning network provide resources or further contacts, and reciprocal advantages emerge among the networkers. Examples include simplifying workflows (“cutting through the red tape”), passing on strategic information and mentoring network members in their professional development.

Sometimes this network grows organically, through colleagues, friends, and miscellaneous conference contacts, other times, we deliberately include people we see as experts in our field.

birdsofafeather

Birds of a Feather: Weak Social Ties in Knowledge Networks Can Fuel Problem Solving

Who to Connect with?

With an increased engagement in social media channels this question faces us within the AACE community. Just as it becomes more and more difficult to keep up with the numerous publications and trends in Educational Technology, finding the right mix and balance for meaningful engagement with social media is a lifelong learning challenge.

Review our list of  Top 20 in Educational Technology on Social Media to find suggestions for Twitter channels and Edublogs.

Over to You

Although social media tools make it easy and convenient to keep up with a large number of resources, it takes deliberate effort, informed choices and, last but not least, individual preferences to shape a meaningful personal learning environment.

We want to know more about your personal learning environment. Do you have a favorite Edublogger? Which Twitter feeds do you pick up as your daily information grain? Which tools, communities and gatherings to you frequent online and offline? Leave comments and share recommendations through Twitter (@AACE) and on our Facebook page.

Posted in AACE Tagged with: , , , , ,

Top 20 in Educational Technology to Connect with through Social Media

Interested in growing your personal learning network? We informally queried the AACE community and beyond resulting in these top 20 suggestions for Edublogs and Twitter Handles. The list includes past AACE conference keynote speakers, conference committee members and EdITLib contributors.

EdTech Scholars' Twitter Profiles

Who is Who in Twitter?

Terry Anderson
@terguy
Professor in Distance Education at Athabasca University (CA)
Topics: Digital Scholarship, Open Education, Educational Technology, Learning Networks, MOOCs
Blog: http://terrya.edublogs.org/


Tony Bates
@drtonybates
Consultant in E-Learning and Distance Education (CA)
Topics: Instructional Design, Open Access, Open Education, Educational Technology, Strategy and Innovation, E-Books, Open Textbooks
Blog: http://www.tonybates.ca/


Curtis Bonk
@travelinedman Professor of Instructional Systems Technology in the School of Education at Indiana University (US)
Topics: Open Education, Self-Directed Learning, Motivation, MOOCs, OER, Instructional Design, Global Learning
Blog: http://travelinedman.blogspot.com/


danah boyd
@zephoria
Scholar at Microsoft Research, Founder of Data & Society Research Center, Fellow at Harvard's Berkman Center (US)
Topics: Social Media, Youth Culture, Internet Culture, Big Data, Social Networ Blog: http://www.zephoria.org/thoughts/


Saul Carliner
@saulcarliner
Professor of Educational Technology at Concordia University (US)
Topics: Workplace Learning, Educational Technology, Instructional Design, Organizational Development


Grainne Conole
@gconole
Professor of Learning Innovation at School of Education, Bath Spa University (UK)
Topics: Online Learning, Higher Education, Learning Design, OER, Learner Experience, Learning Theories, Methodologies
Blog: http://e4innovation.com/


Alec Couros
@courosa
Professor of Educational Technology & Media, University of Regina (CA)
Topics: Personal Learning Networks, Personal Learning Environments
Blog: http://educationaltechnology.ca/couros/


Mark Curcher
@macurcher
Director of 21st Century Educators Program, Tampere University of Applied Sciences (FI)
Topics: Teacher Development, Entrepreneurship, EduPunk, Innovation, Educational Technology


Laura Czerniewicz
@Czernie
Director of Center for Educational Technology at University of Cape Town (SA)
Topics: Higher Education, Digital Scholarship, Open Education, Digital Divide, Mobile Learning, OER
Blog: http://lauraczerniewicz.uct.ac.za/


Nellie Deutsch
@nelliemuller
EFL teacher, faculty at Atlantic University (US), founder of Integrating Technology for Active Lifelong Learning (IT4ALL) and Moodle for Teaches (M4T)
Topics: Online Collaborative Learning, Moodle, Teacher Education, EFL, MOOCs, K-12


Aaron Doering
@chasingseals
Associate Professor of Learning Technologies / Learning Technologies Media Lab Director at University of Minnesota (US)
Topics: Adventure Learning, Experiental Learning, Design Based Research, Innovative Learning Design, Photography, Multimedia


Stephen Downes
@oldaily
Senior Researcher at National Research Council of Canada (CA)
Topics: Personal Learning Environments, cMOOCs, Connectivism, Sensemaking, Networked Learning, Educational Technology, Higher Education, K-12
Blog: http://www.downes.ca/news/OLDaily.htm


Jon Dron
@jondron
Professor at School of Computing and Information Systems, Athabasca University (CA)
Topics: Digital Scholarship, Open Education, Educational Technology, Learning Networks, MOOCs
Blog: https://landing.athabascau.ca/blog/owner/jond


Ann Hill Diun
@AHDuin
Professor at Department of Writing Studies, University of Minnesota (US)
Topics: Organizational Development, Higher Education, Personal Learning Environments, Portfolios, Social Networks


Alan Levine
@cogdog
Educational Media Consultant (US)
Topics: Digital Storytelling, Educational Technology, cMOOCs, Podcasting, Photography, Multimedia
Blog: http://cogdogblog.com/


Charles Miller
@design2research
Associate professor of Learning Technologies at University of Minnesota (US)
Topics: Adventure Learning, Experiental Learning, Design Based Research, Innovative Learning Design, Photography, Multimedia


Howard Rheingold
@hrheingold
Researcher, Author, Visiting lecturer in Stanford University's Department of Communication (US)
Topics: Learning Communities, Virtual Communities


George Siemens
@gsiemens
Director of Learning Innovation and Networked Knowledge Research Lab (LINK) at University of Texas at Arlington (US)
Topics: Collective Intelligence, Connectivism, Learning Analytics, Learning Networks, Big Data, MOOCs
Blog: www.elearnspace.org/blog/


Martin Weller
@mweller
Professor of Educational Technology in the Institute of Educational Technology at Open University (UK)
Topics: Digital Scholarship, Open Education, OER, Open Access
Blog: http://blog.edtechie.net/


Steve Wheeler
@timbuckteeth
Professor ofLearning Technology in the Plymouth Institute of Education at Plymouth University (UK).
Topics: E-Learning, Mobile Learning, Web 2.0, Blogs, Wikis, Podcasting, Distance Education, Social Networks
Blog: http://steve-wheeler.blogspot.com/


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