This is my contribution to a collaborative writing project, which had a loose theme of “The Highs and Lows of Social Media”….

“Twitter is hard, and scary, and hard, but people keep saying it’s currently the most important social networking tool, and absolutely vital to any kind of worthwhile online presence. So no pressure then. But as a newbie, where are you supposed to start? Well, with an example of a really good tweet I suppose. Let’s see…

‘Facebook is down. Please refrain from genuine human contact until the problem is resolved.’

Now that’s good! It’s satire, because it implies that people who use social networking sites have no actual interpersonal skills, but it also, you know, is a piece of social networking itself. So it’s a kind of double strength satire. It turns out this tweet was written by no less a person, I mean Person, than God. Despite shouldering the responsibility of creating and overseeing the universe, apparently He can still find time to tweet on topics as diverse as Wimbledon, Donald Trump’s presidential aspirations and gun ownership. It turns out that this deity, as is the fashion, has a human incarnation on Earth. David Javerbaum is an Emmy award-winning American comedy writer whose writing credits include The Daily Show with Jon Stewart and The Late Show with David Letterman. He has been running @TheTweetOfGod since October 2010, and has to date gathered an impressive flock of over 2.15 million followers.

Jauverbaum’s background as a professional writer shines through on his Twitter feed. Frequently hilarious, often topical and regularly poignant, his tweets are the best lesson I’ve seen in constructing messages of 140 characters or less. Assuming the identity of one of history’s most feared / worshipped creators gives him a unique perspective from which to comment on humanity’s peculiarities. You might assume that being omnipotent and omniscient would leave God with nobody to look up to. He does however, follow just one person on Twitter – Justin Bieber. Justin has almost 70 million followers, and if there’s one quality you’d think the inspiration for so many major world religions would appreciate it’s popularity. Except now Katy Perry has even more followers. Sort it out God.

Hardly the most avid of social networkers myself, I have only recently set up a Twitter account. I’ve barely used it yet, and with an almost saintly restraint have resisted the urge to indulge in that most basic of human instincts and tweet endlessly about cats and how cute they are. Just as many people of a religious persuasion turn to their God(s) in times of need for solace and guidance, I turn to mine in the hope that some of His literary technique, satirical turn of phrase and 2.15 million followers might come my way. I’ve compensated for my own lack of original content by retweeting Him (I may not know much about microblogging, but I know what I like.) Though my start on Twitter has been a little shaky, I know I can do better. With God’s help.”

An attempt to create a visual representation to trace the various strands of themes from myself and three fellow MA students on a collaborative writing exercise. The theme was, loosely, the highs and lows of social media. We seem to have come down more on the side of the lows though!

Why are scientific collaborations so difficult to sustain? It has been natural to think of scientists as being potentially really good at collaboration, but attempts to set up computer-based collaborative projects (collaboratories) within the scientific community haven’t been too successful. It seems while they are good collaborators, they function best in localised, face to face groups.

Three areas of difficulty are identified;

1. Knowledge (as opposed to mere information) is hard to transmit across distances. It’s much easier for a scientist to explain his ideas, which may be on the cutting edge of understanding, directly to a colleague than to someone over a computer network.

2. Scientists work independently most of the time. They are inclined to work to their own research and travel schedules.

3. They typically work for institutions, and there are traditionally difficulties with cross-institutional barriers. Legal issues may need to be resolved, and there is often a lot of protectiveness over intellectual property.

To help resolve these difficulties, The Science of Collaboratories (SOC) was a five-year project funded by the National Science Foundation (NSF) to study large-scale academic research collaborations across many disciplines. The goals were to compare different collaborative projects, develop theory about this emerging research form, and develop strategies for facilitating more successful projects in the future. They ended up coming up with a seven category taxonomy of collaboratories. Taxonomy is the science or technique of classification. (Why not just say “categories”? You see this is why we’ve got point 1. above.)

There follows a very long-winded and utterly riveting account of what defines a collaboratory, the kind of sampling techniques used, and a bit with “prototypicality” in it.

Of the 7 categories explained, I a few notable ones were;.

Shared Instrument: This category is set up to allow researchers to get access to expensive or normally inaccessible equipment. An example is given of twin telescopes in Hawaii, which due to their remote location can be accessed remotely from several subscribed universities. This kind of observation produces a very large amount of data which needs to be dealt with.

A Community Data System: An information resource that is created, maintained, or improved by a geographically-distributed community. The example is given of the Protein Data Bank (PDB) which processes and distributes 3d structure data of proteins and molecules. Interestingly, this project and ones like it often lead to great advances in 3d modelling and data visualisation techniques to deal with the large datasets produced.

Open Community Contribution System: This is a group of often geographically separated people who unite to work on a specific research problem. The interesting thing is that it often involves members of the general public, and encourages them to help deal with projects in the form of work, not necessarily contributing data. Wikipedia is given as an example, but it also reminds me of the usefulness of amateur astronomers, who by sheer strength of numbers can monitor large portions of the sky that professionals can’t, and have made many important discoveries.

This is an interesting review on the practice of Knowledge Cartography, or mind mapping, and how it has evolved over a very long time to become a useful tool for the construction and sharing of knowledge and how mind maps, once made, can then be built on and discussed.

The main point is that when you externalise ideas in some sort of visual form, it makes you far better able to get a grasp on them. You can immediately begin to see connections between topics in a visual way, and there’s a sense of a bird’s eye view of your own thought processes. Also I think when you begin to create a mind map either on paper or with software such as Xmind, there’s a sense of needing to be organised and efficient with 2d, actual space, which seems to encourage the same discipline with thought processes.

It’s interesting that the point is made that the making of maps actually predates number systems and written language in our species. I’m sure it would be fascinating to trace the stages of how writing systems derived from the making of maps. I also found that the use of colour in some of the examples given really enhanced the meaning. The Argument and Evidence mapping used to study cases in courts used green for positive arguments, red for negative ones and orange for solutions that were found to counteract the negatives.

A look at the Interaction analysis model (IAM) which is a tool developed by Gunawardena, Lowe and Anderson in 1997. This tool is used to analyse the activity of students on asynchronous online learning tools such as blogs and discussion boards. The article assesses the IAM and its validity as a way to measure students’ learning experiences, and considers how it might be improved.

The issue of assessing quality of interaction as opposed to just quantity is brought up again as in the Community of Enquiry Framework. To tackle this, Peters and Slotta developed a method of analysing the changes students made to a wiki, but with a deeper consideration of the type of changes made, the particular types of files added, and whether the changes made were to a student’s own work or another’s. By differentiating between “peer” and “self” a picture emerges as to the levels of collaboration and contribution to the group, and some light was shed on aspects which may inhibit an individual’s activity, such as being overwhelmed by the amount of content on a board. (“Coding” the data seems to keep coming up in these studies, and seems to refer to how particular bits of information are logged under certain categories and how they are dealt with.)

To analyse knowledge construction, the IAM sets out 5 distinct phases of knowledge construction, each with distinct learning processes;

1. Sharing and comparing of information – statements or observations, comparisons of knowledge etc.
2. The discovery and exploration of dissonance or inconsistency among ideas, concepts or statements. Where students identify areas of disagreement and are forced to properly back up and reassert their positions.
3. Negotiation of meaning/co-construction of knowledge. This is where common ground is sought, differences of opinion are negotiated and compromises to accommodate other’s views are found.
4. Testing and modification of proposed synthesis or co-construction.  Where ideas proposed are tested against existing knowledge, or personal experience, to see what holds up and what doesn’t.
5. Agreement statement(s)/applications of newly constructed meaning. What has been agreed upon and adapted or what has been rejected? What has the group learned from the process that they didn’t know before?

When using the IAM to assess a given discussion or blog, the information is broken down and is assigned to one of the above 5 groups. Ideally, you want to see a lot of students’ contributions getting to the highest numbers on the scale. This didn’t happen though – in several studies of groups of teachers and students, the vast majority of posts were in Ph1 and a few in 2 and 3, with little or none at all in 4 and 5. This was explained by participants essentially feeling inhibited by the need to be polite to people they perhaps didn’t know and not seem controversial, or by being very self conscious if they were being assessed and monitored. In the case of students who met regularly in other classes face to face, it was thought they simply didn’t need to interact online. The big exception was a group of women enrolled in an educational technology course in Korea (Preaching to the converted..?) Also a postgrad course on Multimedia in Education in Portugal scored much higher in the later phases.

The lack of engagement in the higher phases was discussed, a lack of experience on behalf of the moderators / instructors was highlighted, for example in the setting of tasks and questions the students would be likely to willingly contribute to. Also, cultural factors were at play – studies in Taiwan and Singapore suggested that students were not comfortable with the dissonance aspect of the interaction, but were able to reach forms of resolution without it. This suggests that the (western) notion of needing robust debate and challenges to ones deeply held beliefs to learn may not be so valid.

This is a report on the findings of the Community of Enquiry, set up in the nineties in the University of Alberta by the three authors, to investigate the then recent idea of graduate programs being delivered online, with an emphasis on discussion and debate taking place in discussion forums. They were delivering a graduate program in Communications and Technology, so it was a pretty apt study given the context.

Social presence, cognitive presence and teaching presence were the three main elements identified by the group as being vital to the process of successful online learning. The online communication was text based and asynchronous. This was in the sense that a face to face classroom or video conferencing situation is synchronous, where people present are able to directly respond to each other. They were very interested in the pros and cons of both approaches, against the perceived background that too much of the learning experience was lost in the asynchronous approach.

There was an extensive study of the transcripts of students’ discussions in order to tease out how these different presences were working together to give students a well rounded and rewarding educational experience, in order to see how this experience could be improved. There is a long description of how exacting they were in analysing the data they got, most of which is scarily complex. I was very struck by their use of the phrase “unit of learning” and their attempts to define what that might even be. It seem it’s easy to quantify how many words or posts a student has written or responded to, but not so easy to quantify exactly how much “stuff” was learned. Quantitative analysis is a lot easier to do than qualitative analysis it seems. This issue prompted them to examine those difficulties specifically in another study. The study seems to have been very influential, having been cited over 600 times in scholarly publications at the time of writing.

Allows you to capture and save references.

Evernote is good for keeping your notes, and sync them between your devices. You can attach tags or documents to notes.

Zotero is a better solution.  Installs as a plugin for browsers. It allows you, when on Google Scholar, to bring up a list of items on the page, select what you want and it’ll add the info to a folder, with all the bibliographical details it can find. You can then get it to create a bibliography in a format you choose.  You are also able to add notes or tags. It will also sync the information to all of your other devices .

In Word or Open Office you can also get an add-on to add footnotes to your documents.

Google is a good starting point for searches for info, especially if you filter out unwanted terms in an advanced search and are discerning about what you’re looking for. DuckDuckGo is a very good alternative, it doesn’t track your searches like Google, so will often suggest different and better results. Google Scholar is an academic search engine. It lists how often the article has been found to be cited, and tells you where it was cited, giving you an idea of the relative importance. You can search in a similar way in the college library, and use the Google search bar there, follow the “Where can I get this” link and you’ll often get the full articles to download. Checking the author’s bibliographies can get you some more good results. Use “create alert” link at the bottom of Google scholar and you’ll get notified of related searches through email.