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 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:


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.


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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”.


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).
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
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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.


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).

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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.


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.
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What Can Educational Researchers Do to Make Their Studies Replicable?


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.


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.

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

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

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

Laura Czerniewicz
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
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
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
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
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
Professor at Department of Writing Studies, University of Minnesota (US)
Topics: Organizational Development, Higher Education, Personal Learning Environments, Portfolios, Social Networks

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

Charles Miller
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
Researcher, Author, Visiting lecturer in Stanford University's Department of Communication (US)
Topics: Learning Communities, Virtual Communities

George Siemens
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
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
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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Is It Possible to Learn Anything Online? A Student’s Perspective

Open learning resources along with web based training and online degree programs on almost every subject have been accumulating at an amazing speed, and become vastly abundant for each individual learner. According to a study conducted by Babson Survey Research Group, at least 30 new courses are released on major MOOC platforms (e.g., Coursera, EdX) every week in 2014, compared with 10 in 2012. Students’ enrollment of online courses is growing even faster. Only 2% of students used to take at least one online course in 2002, in the fall of 2010 this number had increased to 30%. More than 7.1 million students took at least one online course during only fall 2012 . A recent survey by Ambient Insight Research predicts that the online learning market will rise to $49.9 billion by 2015.

Online learning is on the rise - both in formal and informal settings (Image by Alec Couros)

Online learning is on the rise - both in formal and informal settings (Image by Alec Couros)

Is it possible to learn anything online? An effective learner can teach him or herself almost anything with the copious online resources. However, my personal experience in a Massive Open Online Course (MOOC) makes me doubtful whether college students are prepared for this type of learning.

I took a course named “R Programming” on Coursera last year. This course was very popular, and has been shared on Facebook for more than 6.000 times. The course was presented as an introductory course, recommended to people who have “some familiarity with programming concepts and basic knowledge of statistical reasoning” before taking the course. The less-than-4-hour video lectures of the course covered some very basic programming knowledge, like control structure and loop. However, when it came to assignments and projects, the requirement for programming knowledge suddenly increased to a level far beyond the video lectures and recommended prerequisite knowledge. Many students felt frustrated when working on the assignments/projects and dropped out off the course. I finished the course and got a certificate with distinction, simply because I had been coding in different programming languages for several years, not because I learned very much from the course material. Honestly, I didn’t even watch all the lecture videos.

From my experience, especially in the area of programming, this is not an exception. Many online learning resources are not structured in a way that reaches learners with no or little pre-knowledge.  Though they may contain valuable material and information, it is doubtful that you will learn how to program if you are not a programmer yet. Plus, it is as easy to drop out as it is to sign in. To take advantage of resources like MOOCs effectively, a learner has to be able to think critically, understand clearly the knowledge structure of a subject and his/her own abilities, constantly diagnose learning problems, search online for additional learning material, and seek support through a personal learning network. Is the typical college student ready for this type of learning?

Much of the discussion around MOOCs creates the impression that today’s students are digital natives, held back in our informal learning journeys by outdated brick-and-mortar institutions. My ongoing research and personal experiences tell a different story. In 2014, I conducted  a survey among college students majoring in computer science at the University of Georgia. It included three questions on students’ attitude towards self-directed online learning.

Interestingly enough, the low score of first/second year college students indicate that they did not believe that they can learn sophisticated knowledge through online learning. They didn’t like independent learning very much, and also reported less frequent online search in their learning. A possible interpretation of the difference between first/second year college students to fourth year students is that the college experience actually helps us to develop independent online learning behavior. This is a question worth further exploration.

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Que sera, sera? Predicting Future Trends in Educational Technology – Horizon Report 2015

Since the New Media Consortium (NMC) released the ‘Horizon Report 2015 Higher Education’ at the beginning of February, the 50-page document has been broadly circulated and commented upon in the blogosphere and on twitter. Reactions vary from appreciative “As always it makes interesting reading” (Grainne Conole) to critical “NMC should be obligated to re-examine its methodology” (Stephen Downes).

About the Horizon Project

Horizon2015 Since 2004, the New Media Consortium annually releases the Horizon trend report to identify key issues that are likely to have an impact on education over the next five years. The selection process for the NMC Horizon Report is a modified Delphi process. The Delphi method involves experts in a two-step moderated group discussion to identify possible future developments. This strategy is used to predict the impact of new technological trends or innovations.

From 2004-2009, the New Media Consortium released one single annual edition of the Horizon report. In 2009, the NMC added a K-12 edition to the series, followed by the Museum edition in 2010 and the Library edition in 2014.

Until recently, each report followed the same structure, highlighting six emerging technologies or practices based on time to adoption (one year or less, two to three years, four to five years). In 2013, the report introduced a new section on ‘significant challenges’; and the 2014 edition brought with it a complete structural overhaul, which tripled the number of trends and developments discussed in the report.

2015 Higher Education Edition in a Nutshell

In its current form, the Horizon report identifies 18 topics likely to impact planning and decision-making in the educational technology sector: Six key trends accelerating technology adoption, six significant challenges for technology adoption, and six important technological developments.

2015 Horizon Report for Higher Education - Overview

Six Trends Accelerating Technology Adoption

The Horizon report identifies six key trends that are likely to drive technology planning and decision-making: Long-term trends will influence the educational technology sector over the next five years and beyond, mid-term trends will be influential for the next 3-5 years, and short-term trends are likely to become commonplace or fade away in 1-2 years.
  1. Increasing Use of Blended Learning: In terms of trends in the short-term, the report foresees a rising amount of online and blended learning offerings that complement traditional classroom activities on campus. While blended learning is not exactly a new trend, the report notes changes in its implementation: “Instructors are thinking more deeply about mimicking the types of interactions learners are accustomed to in brick and mortar settings”.
  2. Redesigning Learning Spaces: As another short-term trend, the report identifies the effort of reconfiguring learning spaces to better support new forms of teaching and learning: “Instead of the traditional rows of chairs with writing surfaces facing a podium, universities are creating more dynamic classroom layouts, often with seating arrangements that foster collaborative work.”
  3. Proliferation of Open Educational Resources (OER): As OER is gaining traction across campuses, the report predicts an increased acceptance and usage as a mid-term trend. 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”.
  4. Growing Focus on Measuring Learning: Measuring learning through data-driven practice and assessment is seen as a mid-term trend. As institutions are facing pressure from accreditation bodies and governing agencies to document student achievement and learning outcomes, this process may be facilitated by learning analytics: “The emerging science of learning analytics is providing the statistical and data mining tools to recognize challenges early, improve student outcomes, and personalize the learning experience”.
  5. Advancing Cultures of Change and Innovation: As a long-term trend, the report predicts a cultural shift in institutional leadership and curricular structures towards agile start-up models that foster flexibility, creativity and entrepreneurial thinking: “It will require visionary leadership to build higher education environments that are equipped to quickly change processes and strategies as start-ups do. If these organizational models are designed well, universities can experience more efficient implementation of new practices and pedagogies”.
  6. Cross-Institutional Collaboration: The report predicts increased cross-institutional collaboration as another long-term trend, reflecting the notion that innovation can scale better when ideas are shared between institutions: “The prevalence of consortia underscores a vision of institutions as belonging to part of a larger ecosystem in which long-term survival and relevance in higher education relies on the mutually beneficial partnerships”.

Six Significant Challenges for Technology Adoption

The report lists six challenges that are not charted on a timeline, but categorized as solvable, difficult and wicked, depending on how well we understand the scope of the problem and its potential solutions.
  1. Blending Formal and Informal Learning: As one can learn something about almost anything at the palm of one’s hand, self-directed learning, led by curiosity or serendipitous discovery, has the potential to enrich formal learning in higher education. However, institutions struggle to acknowledge and validate informal learning experiences.
  2. Improving Digital Literacy Skills: As the traditional view of literacy as the ability to read and write has expanded to encompass fluency in using digital tools and online information with aptitude and creativity. In order to improve digital literacy, both students and faculty need support and training.
  3. Personalizing Learning: Universities struggle to design and offer educational experiences that address the individual student’s specific learning needs, interests, aspirations and cultural background. Data-driven approaches to effectively facilitate individual learning pathways have only recently begun to emerge.
  4. Teaching Complex Thinking: Complex thinking describes the ability to understand systems in order to solve problems by deciphering how individual components work together as part of a dynamic unit that creates patterns over time. While data visualization and infographics can make complex ideas digestible for students, the skillful presentation of data has become yet another expectation scientists and researchers need to meet.
  5. Competing Models of Education: As more and more free and low-cost content becomes accessible via the Internet and, at the same time, students face rising costs of tuition, new models of education (i.e., MOOCs, competency-based degree programs) are bringing unprecedented competition to the traditional four-year campus experience: “There is a growing need to frankly evaluate the models and determine how to best support collaboration, interaction and assessment at scale”.
  6. Rewarding Teaching: Since both organizational rankings and individual career trajectories are largely determined by research output, universities struggle to acknowledge talent and skill as an instructor as a valuable asset, which impedes the implementation of innovative pedagogies: “Overemphasis on research has caused a number of negative ramifications, including an excessive dependence on part-time faculty”.

Six Important Developments in Educational Technology

In its final section, the report discusses emerging educational technologies that have the potential to foster changes in education within the next five years – for example through the development of progressive pedagogies and learning strategies, the organization of teachers’ work or the delivery of content. Educational technologies are broadly defined as tools and resources used to improve teaching, learning, and creative inquiry. Currently, the NMC monitors seven different types: 1) Consumer technologies, (2) digital strategies, (3) technologies enabling transformative innovation, (4) Internet technologies, (5) learning technologies, (6) social media technologies, (7) visualization technologies.
  1. Bring Your Own Device: The report states that a growing number of best practice approaches are paving the way for Bring Your Own Device (BYOD) to enter mainstream with an adoption timeframe of one year or less. BYOD is a digital strategy that refers to people bringing their own laptops, tablets, smartphones, etc. to their learning or work environment, thus enabling students and educators to leverage the tools that they find most efficient: “The link between the use of personal devices and increases in productivity gets stronger each passing year as more organizations adopt BYOD policies”.
  2. Flipped Classroom: As another digital strategy on the short-term horizon, the report predicts the broad adoption of flipped classrooms in higher education. The flipped model shifts the time spent in class from content transfer to group discussions, project-based learning and other learner-centered activities. The lecture-based information delivery takes place before and after class in form of video recordings, podcasts or reading assignments.
  3. Makerspaces: Makerspaces, community-oriented workshops that engage learners in problem-solving through hands-on design and construction, are forecasted to reach mainstream within 2-3 years: “Widespread enthusiasm behind makerspaces in steadily growing”. A growing number of universities are creating informal learning spaces that support the maker movement, offering 3D printers, laser cutters, Legos, sewing machines and other tools.
  4. Wearable Technologies: As another mid-term trend, wearable technologies are poised to see significant growth in the coming years. This consumer technology is expected to spur experimentation in higher education.
  5. Adaptive Learning Technologies: With an adoption timeframe of 4-5 years, the horizon report describes the advancement of adaptive learning. The term refers to smart learning applications that adjusts over time to user data, thus customizing learning experiences for individual needs on a large scale. This can happen by adapting instructional material according to individual user data, or by aggregating data across a large sample of users to optimize curricula.
  6. Internet of Things: Another trend on the long-term horizon is the Internet of Things (IoT). IoT signifies a network of objects that connect the physical realm and the information technology sphere by embedding chips, sensors or tiny processors into objects so that they can transmit information such as age, cost, color, pressure or humidity. Application options in higher education include streamlining processes, automation and data-driven sustainability efforts

Is it Useful?

From Web 2.0 and social media to open education and personal learning environments to Massive Open Online Courses - educational technology research is a trend-driven discipline. Visions of the future in form of technology forecasts and trend reports are common ways for practitioners and researchers alike to stay ahead of the technology curve. At the turn out the millennium future studies in education have seen a definite boom. Various reports, projects, surveys and workshops aim to depict future needs and emerging themes in education, for example the CORE Education’s Ten Trends Annual Report (New Zealand), the Innovating Pedagogy Report (UK), or the European TEL-MAP project.

Among these publications and initiatives, the Horizon report forms an influential resource for educators that are interested in not only learning what the emerging trends are, but also how they might be able to participate in and shape the transformation process.

Que sera, sera

Given the rapidly changing environments of modern societies there is a growing need to know about the development of future technologies and their impact upon societal changes. Reducing risks and identifying opportunities are common motives for studying the future. However, educational technology and technological change are both drivers and results of complex interactions in the context of social, economic, and political forces. Future Studies in the educational technology sector are methodologically tricky and may be compared to forecasting today's weather:
“Very long-range climate trends, alternative scenarios, or panels of experts are less effective than getting a rich contextual picture of the weather (perhaps from the weather channel) and looking at very recent trends such as direction and speed of weather fronts.” (Coates et al., 2001).

At first glance, one would expect that trend forecasts like the Horizon Report thrive to achieve correct prognosis about the future and that thus their quality is simply measured by the number of correct predictions in a given time frame. However, at a closer look, it is not that simple. The report is conducted to influence and inform strategic planning. Thereby it impacts future developments and may foster or prevent certain developments. Hence, its strength is to inspire discourse within the community by depicting alternative futures for educational technology adoption.

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