Some thoughts on use of metrics in university research assessment

The UK’s Research Excellence Framework (REF) is like a walrus: it is huge, cumbersome and has a very long gestation period. Most universities started preparing in earnest for the REF early in 2011, with submissions being made late in 2013. Results will be announced in late December, just in time to cheer up our seasonal festivities.
 
Like many others, I have moaned about the costs of the REF: not just in money, but also the time spent by university staff, who could be more cheerfully and productively engaged in academic activities. The walrus needs feeding copious amounts of data: research outputs must be carefully selected and then graded in terms of research quality. Over the summer, those dedicated souls who sit on REF panels were required to read and evaluate several hundred papers. Come December, the walrus digestive system will have condensed the concerted ponderings of some of the best academic minds in the UK into a handful of rankings.

But is there a viable alternative? Last week I attended a fascinating workshop on the use of metrics in research. I had earlier submitted comments to an independent review of the role of metrics in research assessment from the Higher Education Funding Council for England (HEFCE), arguing that we need to consider cost-effectiveness when developing assessment methods. The current systems of evaluation have grown ever more complex and expensive, without anyone considering whether the associated improvements justified the increasing costs. My view is that an evaluation system need not be perfect – it just needs to be ‘good enough’ to provide a basis for disbursement of funds that can be seen to be both transparent and fair, and which does not lend itself readily to gaming.

Is there an alternative?
When I started preparing my presentation, I had intended to talk just about the use of measures of citations to rank departments, using analysis done for an earlier blogpost, as well as results from this paper by Mryglod et al. Both sources indicated that, at least in sciences, the ultimate quality-related research (QR) funding allocation for a department was highly correlated with a department-based measure of citations. So I planned to make the case that if we used a citation-based metric (which can be computed by a single person in a few hours) we could achieve much the same result as the full REF process for evaluating outputs, which takes many months and involves hundreds of people.
However, in pondering the data, I then realised that there was an even better predictor of QR funding per department: simply the number of staff entered into the REF process.

Before presenting the analysis, I need to backtrack to just explain the measures I am using, as this can get quite confusing. HEFCE deserves an accolade for its website, where all the relevant data can be found. My analyses were based on the 2008 Research Assessment Exercise (RAE).  In what follows I used a file called QR funding and research volume broken down by institution and subject, which is downloadable here. This contains details of funding for each institution and subject for 2009-2010. I am sure the calculations I present here have been done much better by others and I hope they will not by shy to inform me if there are mistakes in my working.

The variables of interest are:

• The percentages of research falling in each star band in the RAE. From this, one can compute an average quality rating, by multiplying 4* by 7, 3* by 3, and 2* by 1 and adding these, and dividing the total by 100. Note that this figure is independent of department size and can be treated as an estimate of the average quality of a researcher in that department and subject.
• The number of full-time equivalent research-active staff entered for the RAE. This is labelled as the ‘model volume number’, but I will call it Nstaff. (In fact, the numbers given in the 2009-2010 spreadsheet are slightly different from those used in the computation, for reasons I am not clear about, but I have used the correct numbers, i.e. those in HEFCE tables from RAE2008).
• The departmental quality rating: this is average quality rating x Nstaff. (Labelled as “model quality-weighted volume” in the file). This is summed across all departments in a discipline to give a total subject quality rating (labelled as “total quality-weighted volume for whole unit of assessment”).
• The overall funds available for the subject are listed as “Model total QR quanta for whole unit of assessment (£)”. I have not been able to establish how this number is derived, but I assume it has to do with the size and cost of the subject, and the amount of funding available from government.
• QR (quality-related) funding is then derived by dividing the departmental quality rating by the total subject quality rating and multiplying by overall funds. This gives the sum of QR money allocated by HEFCE to that department for that year, which in 2009 ranged from just over £2K (Coventry University, Psychology) to over £12 million (UCL, Hospital-based clinical subjects). The total QR allocation in 2009-2010 for all disciplines was just over £1 billion.
• The departmental H-index is taken from my previous blogpost. It is derived by doing a Web of Knowledge search for articles from the departmental address, and then computing the H-index in the usual way. Note that this does not involve identifying individual scientists.

Readers who are still with me may have noticed that we'd expect QR funding for a subject to be correlated with Nstaff, because Nstaff features in the formula for computing QR funding. And this makes sense, because departments with more research staff require greater levels of funding. A key question is just how much difference does it make to the QR allocation if one includes the quality ratings from the RAE in the formula.

Size-related funding
To check this out, I computed an alternative metric, size-related funding, which multiplies the overall funds by the proportion of Nstaff in the department relative to total staff in that subject across all departments. So if across all departments in the subject there are 100 staff, a department with 10 staff would get .1 of the overall funds for the subject.

Table 1 shows: the correlation between Nstaff and QR funding (r QR/Nstaff) and how much a department would typically gain or lose if size-related funding were adopted, expressing the absolute difference as a percentage of QR funding (± % diff).

Table 1: Mean number of staff and QR funding by subject, with correlation between QR and N staff, and mean difference between QR funding and size-related funding

Mean Mean r QR/ ± % Subject Nstaff QR £K Nstaff diff Cardiovascular Medicine 26.3 794 0.906 23 Cancer Studies 38.1 1,330 0.939 13 Infection and Immunology 43.7 1,506 0.971 22 Other Hospital Based Clinical Subjects 58.2 1,945 0.986 23 Other Laboratory Based Clinical Subjects 21.8 685 0.952 41 Epidemiology and Public Health 26.6 949 0.986 25 Health Services Research 21.9 659 0.900 24 Primary Care & Community Based Clinical  10.4 370 0.790 29 Psychiatry, Neuroscience & Clinical Psychology 46.7 1,402 0.987 15 Dentistry 31.1 1,146 0.977 13 Nursing and Midwifery 18.0 487 0.930 32 Allied Health Professions and Studies 20.4 424 0.884 36 Pharmacy 27.5 899 0.936 24 Biological Sciences 45.1 1,649 0.978 19 Pre-clinical and Human Biological Sciences 49.4 1,944 0.887 18 Agriculture, Veterinary and Food Science 33.2 999 0.976 21 Earth Systems and Environmental Sciences 28.6 1,128 0.971 14 Chemistry 37.9 1,461 0.969 18 Physics 44.0 1,596 0.994 8 Pure Mathematics 18.4 489 0.957 24 Applied Mathematics 20.0 614 0.988 19 Statistics and Operational Research 12.6 406 0.953 19 Computer Science and Informatics 22.9 769 0.954 26 Electrical and Electronic Engineering 23.8 892 0.982 17 General Engineering; Mineral/Mining Engineering 28.9 1,073 0.958 30 Chemical Engineering 26.6 1,162 0.968 15 Civil Engineering 23.2 1,005 0.960 19 Mech., Aeronautical, Manufacturing Engineering 35.7 1,370 0.987 14 Metallurgy and Materials 21.1 807 0.948 24 Architecture and the Built Environment 18.7 436 0.961 23 Town and Country Planning 15.1 306 0.911 27 Geography and Environmental Studies 22.8 505 0.969 21 Archaeology 20.7 518 0.990 12 Economics and Econometrics 25.7 581 0.968 20 Accounting and Finance 11.7 156 0.982 19 Business and Management Studies 38.7 630 0.964 27 Library and Information Management 16.3 244 0.935 26 Law 26.6 426 0.960 30 Politics and International Studies 22.4 333 0.955 31 Social Work and Social Policy & Administration 19.1 324 0.944 26 Sociology 24.1 404 0.933 24 Anthropology 18.6 363 0.946 12 Development Studies 21.7 368 0.936 25 Psychology 21.1 424 0.919 35 Education 21.0 346 0.983 34 Sports-Related Studies 13.5 231 0.952 37 American Studies and Anglophone Area Studies 10.9 191 0.988 11 Middle Eastern and African Studies 17.7 393 0.978 17 Asian Studies 15.9 258 0.938 26 European Studies 20.1 253 0.787 30 Russian, Slavonic and East European Languages 8.7 138 0.973 22 French 12.6 195 0.979 16 German, Dutch and Scandinavian Languages 8.4 129 0.966 17 Italian 6.3 111 0.865 20 Iberian and Latin American Languages 9.1 156 0.937 17 Celtic Studies 0.0 328 English Language and Literature 20.9 374 0.982 26 Linguistics 11.7 168 0.956 18 Classics, Ancient History, Byzantine and Modern Greek Studies 19.4 364 0.992 22 Philosophy 14.4 258 0.987 23 Theology, Divinity and Religious Studies 11.4 174 0.958 32 History 20.8 366 0.988 21 Art and Design 22.7 419 0.955 37 History of Art, Architecture and Design 10.7 213 0.960 18 Drama, Dance and Performing Arts 9.8 221 0.864 36 Communication, Cultural and Media Studies 11.9 195 0.860 29 Music 10.6 259 0.863 33 Correlations between Nstaff and QR funding are very high –above .9. Nevertheless, this analysis shows that, as is evident in Table 1, if we substituted size-related funding for QR funding, the amounts gained or lost by individual departments can be substantial.  In some subjects, though, mainly in the Humanities, where overall QR allocations are anyhow quite modest, the difference between size-related and QR funding is not large in absolute terms. In such cases, it might be rational to allocate funds solely by Nstaff and ignore quality ratings.  The advantage would be an enormous saving in time – one could bypass the RAE or REF entirely. This might be a reasonable option if the amount of expenditure on the RAE/REF by the department exceeds any potential gain from inclusion of quality ratings. Is the departmental H-index useful? If we assume that the goal is to have a system that approximates the outcomes of the RAE (and I’ll come back to that later) then for most subjects you need something more than Nstaff. The issue then is whether an easily computed department-based metric such as the H-index or total citations could add further predictive power. I looked at the figures for two subjects where I had computed the departmental H-index: Psychology and Physics.  As it happens, Physics is an extreme case: the correlation between Nstaff and QR funding was .994. Adding an H-index does not improve prediction because there is virtually no variance left to explain. As can be seen from Table 1, Physics is a case where use of size-related funding might be justified, given that the difference between size-related and QR funding averages out at only 8%. For Psychology, adding the H-index to the regression explains a small but significant 6.2% of additional variance, with the correlation increasing to .95. But how much difference would it make in practice if we were to use these readily available measures to award funding instead of the RAE formula? The answer is more than you might think, and this is because the range in award size is so very large that even a small departure from perfect prediction can translate into a lot of money. Table 2 shows the different levels of funding that departments would accrue depending on how the funding formula is computed. The full table is too large and complex to show here, so I'll just show every 8th institution. As well as comparing alternative size-related and H-index-based (QRH) metrics with the RAE funding formula (QR0137), I have looked at how things change if the funding formula is tweaked: either to give more linear weighting to the different star categories (QR1234), or to give more extreme reward for the highest 4* category (QR0039) – something which is rumoured to be a preferred method for REF2014. In addition, I have devised a metric that has some parallels with the RAE metric, based on the residual of the H-index after removing effect of departmental size. This could be used as an index of quality that is independent of size; it correlates with r = .87 with the RAE average quality rating. To get an alternative QR estimate, it was substituted for the average quality rating in the funding formula to give the Size.Hres measure. Table 2: Funding results in £K from different metrics for seven Psychology departments representing different levels of QR funding institution QR0137 Size-related QR1234 QR0039 QRH Size.Hres A 1891 1138 1424 2247 1416 1470 B 812 585 683 899 698 655 C 655 702 688 620 578 576 D 405 363 401 400 499 422 E 191 323 276 121 279 304 F 78 192 140 44 299 218 G 26 161 81 13 60 142 To avoid invidious comparisons, I have not labelled the departments, though anyone who is curious about their identity could discover them quite readily.  The two columns that use the H-index tend to give similar results, and are closer to a QR funding based that treats the four star ratings as equal points on a scale (QR1234). It is also apparent that a move to QR0039 (where most reward is given for 4* research and none for 1* or 2*) will increase the share of funds to those institutions who are already doing well, and decrease it for those who already have poorer income under the current system. One can also see that some of the Universities at the lower end of the table – all of them post 1992 universities – seem disadvantaged by the RAE metric, in that the funding they received seems low relative to both their size and the H-index. The quest for a fair solution So what is a fair solution? Here, of course, lies the problem. There is no gold standard. There has been a lot of discussion about whether we should use metrics, but much less discussion of what we are hoping to achieve with a funding allocation. How about the idea that we could allocate funds simply on the basis of the number of research-active staff? In a straw poll I’ve taken, two concerns are paramount. First, there is a widely held view that we should give maximum rewards to those with highest quality research, because this will help them maintain their high standing, and incentivise others to do well. This is coupled with a view that we should not be rewarding those who don’t perform. But how extreme do we want this concentration of funding to be? I’ve expressed concerns before that too much concentration in a few elite institutions is not good for UK academia, and that we should be thinking about helping middle-ranking institution become elite, rather than focusing all our attention on those who have already achieved that status. The calculations from RAE in Table 2 show how a tweaking of the funding formula to give higher weighting to 4* research will take money from the poorer institutions and give it to the richer ones: it would be good to see some discussion of the rationale for this approach. The second source of worry is the potential for gaming. What is to stop a department from entering all their staff, or boosting numbers by taking on extra staff? The first point could be dealt with by having objective criteria for inclusion, such as some minimal number of first- or last-authored publications in the reporting period.  The second strategy would be a risky one, since the institution would have to provide salaries and facilities for the additional staff, and this would only be cost-effective if the QR allocation would cover it. Of course, a really cynical gaming strategy would be to hire people briefly for the REF and then fire them once it is over. However, if funding were simply a function of number of research-active staff, it would be easy to do an assessment annually, to deter such short-term strategies. How about the departmental H-index? I have shown that it not only is a fairly good predictor of RAE QR funding outcomes on its own, incorporating as it does both aspects of departmental size and research quality, but it also correlates with the RAE measure of quality, once the effect of departmental size is adjusted for. This is all the more impressive when one notes that the departmental H-index is based on any articles listed as coming from the departmental address, whereas the quality rating is based just on those articles submitted to the RAE. There are well-rehearsed objections to the use of citation metrics such as the H-index: first any citation-based measure is useless for very recent articles. Second, citations vary from discipline to discipline, and in my own subject, Psychology, within sub-disciplines.. Furthermore, the H-index can be gamed to some extent by self-citation, or scientific cliques, and one way of boosting it is to insist on having your name on any publication you are remotely connected with - though the latter strategy is more likely to work for the H-index of the individual than for the H-index of the department. It is easy to find anecdotal instances of poor articles that are highly cited and good articles that are neglected.  Nevertheless, it may be a ‘good enough’ measure when used in aggregate: not to judge individuals but to gauge the scientific influence of work coming from a given department over a period of a few years. The quest for a perfect measure of quality I doubt that either of these ‘quick and dirty’ indices will be adopted for future funding allocations, because it’s clear that most academics hate the idea of anything so simple. One message frequently voiced at the Sussex meeting was that quality is far too complex to be reduced to a single number.  While I agree with that sentiment, I am concerned that in our attempts to get a perfect assessment method, we are developing systems that are ever more complex and time-consuming. The initial rationale for the RAE was that we needed a fair and transparent means of allocating funding after the 1992 shake-up of the system created many new universities. Over the years, there has been mission creep, and the purpose of the RAE has been taken over by the idea that we can and should measure quality, feeding an obsession with league tables and competition. My quest for something simpler is not because I think quality is simple, but rather because I think we should use the REF just as a means to allocate funds. If that is our goal, we should not reject simple metrics just because we find them oversimplistic: we should base our decisions on evidence and go for whatever achieves an acceptable outcome at reasonable cost. If a citation-based metric can do that job, then we should consider using it unless we can demonstrate that something else works better. I'd be very grateful for comments and corrections.

Reference  
Mryglod, O., Kenna, R., Holovatch, Y., & Berche, B. (2013). Comparison of a citation-based indicator and peer review for absolute and specific measures of research-group excellence Scientometrics, 97 (3), 767-777 DOI: 10.1007/s11192-013-1058-9

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E-Learning for Current Generations

In recent years I have been working on two major concepts:
first, the connectivist theory of online learning, which views learning as a
network process; and second, the massive open online course, or MOOC, which is
an instantiation of that process. These, however, represent only the most
recent of what can be seen as a series of 'generations' of e-learning. In this
talk I describe these generations and discuss how they led to, and are a part
of, the most recent work in online learning.

The theme I would like to explore today concerns the growth
and development of our idea of online learning, or as it is sometimes called,
e-learning. What I would like to do is to describe a series of 'generations' of
technologies and approaches that have characterized the development of online
learning over the years. These generations of have informed the shape of online
learning as it exists today, and will help us understand something of the
direction it will take in the future.

These generations span more than a 20-year period. Indeed,
there may even be described a 'generation zero' that predates even my own
involvement in online learning. This generation is characterized by systems such
as Plato, and represents the very idea of placing learning content online. This
includes not only text but also images, audio, video and animations. It also
represents, to a degree, the idea of programmed learning. This is the idea that
computers can present us with content and activities in a sequence determined
by our choices and by the results of online interactions, such as tests and
quizzes. We have never wandered far from this foundational idea, not even in
the 21st century. And it continues to be the point of departure for all
subsequent developments in the field of online learning.

For me, 'generation one' consists of the idea of the
network itself. My first work in the field of online learning was to set up a
bulletin board system, called Athabaska BBS, in order to allow students from
across the province to communicate with me online. It was also the time I first
began using email, the time I began using the Usenet bulletin Board system, and
the time I first began using online information systems such as Gopher. The
process of connecting was involved and complex, requiring the use of modems and
special software.

As generation one developed, generation zero matured. The
personal computer became a tool anyone could use to create and store their own
content. Commercial software came into existence, including both operating
systems and application programs such as spreadsheets, word processors, and
database tools. Content could be created in novel ways - the 'mail merge'
program, for example, would allow you to print the same letter multiple times,
but each with a different name and address drawn from a database.

The next generation takes place in the early 1990s and is
essentially the application of computer games to online learning. These games
were in the first instance text-based and very simple. But they brought with
them some radical changes to the idea of learning itself.

One key development was the idea that multiple people could
occupy the same online 'space' and communicate and interact with each other.
This development coincided with the creation of IRC - inter-relay chat - and
meant that you were in real time communication with multiple people around the
world. But more: the gaming environment meant you could do things with other
people - explore terrain, solve puzzles, even fight with them.

Another key idea was the design of the gaming space itself.
Early computer games (and many early arcade games) were designed like
programmed learning: they were like a flow chart, guiding you through a series
of choices to a predetermined conclusion. But the online games were much more
open-ended. Players interacted with the environment, but the outcome was not
predetermined. At first it was created by chance, as in the rolling of dice in
a Dungeons and Dragons game. But eventually every game state was unique, and it
was no longer possible to memorize the correct sequence of steps to a
successful outcome.

The third element was the technology developed to enable
that which we today call object oriented programming. This changed the nature
of a computer program from a single entity that processed data to a collection
of independent entities - objects - that interacted with each other: they could
send messages to each other to prompt responses, one could be 'contained' in
another, or one could be 'part' of another. So a game player would be an
object, a monster would be an object, they would be contained in a 'room' that
was also an object, and gameplay consisted of the interactions of these objects
with each other in an unplanned open-ended way.

During the development of this second generation we saw the
consolidation of computer-based software and content, and the commercialization
of the network itself. The many brands we saw in the 80s - Atari, Amiga, Tandy,
IBM, and many more - coalesced into the now familiar Mac-PC divide. A few major
software developers emerged, companies like Microsoft and Corel. Computers
became mainstream, and became important business (and learning) tools.

Meanwhile, the world of networks began to commercialize.
Commercial bulletin board services emerged, such as Prodigy, AOL, GEnie and
Compuserv. And the first local internet service providers came into being.
Networking became the way important people connected, and communities like the
WELL began to define a new generation of thought leaders.

You can begin to see a pattern developing here. Through the
first three generations, a familiar process of innovation occurs: first the
development and piloting of the technology (which is also when the open source
community springs up around it), then the commercialization of the technology,
then the consolidation of that commercial market as large players eliminate
weaker competitors.

The next generation sees the development of the content
management system, and in learning, the learning management system.

Both of these are applications developed in order to apply
the functionality developed in generation zero - content production and
management - to the platform developed in generation one - the world wide web.
The first content management systems were exactly like mail merge, except
instead of printing out the content, they delivered it to the remote user
(inside a computer program, the commands are exactly the same - 'print' is used
to print data to a page, print data to a file, or print data to the network).

Early learning management systems were very easy to define.
They consisted of a set of documents which could be merged with a list of
registered users for delivery. They also supported some of the major functions
of networks: bulletin boards, where these users could post messages to each
other, chat rooms, where they could occupy the same online space together, and
online quizzes and activities, where they could interact with the documents and
other resources.

It is interesting to me to reflect that the major debates
about online learning around this time centered on whether online learning
would be mostly about online content - that is, reflective of generation zero -
or mostly about online interaction - that is, reflective of generation one. I
remember some teachers in Manitoba swearing by the interaction model, and using
a bulletin-board style application called FirstClass - eschewing to more
content-based approach I was favouring at the time.

Learning management systems drew a great deal from distance
learning. Indeed, online was (and is still) seen as nothing more than a special
type of distance learning. As such, they favoured a content-based approached,
with interaction following secondarily. And a very standard model emerged:
present objectives, present content, discuss, test. More advanced systems
attempted to replicate the programmed learning paradigm. The Holy Grail of the
day was adaptive learning - a system which would test you (or pretest you) to
determine your skill level, then deliver content and activities appropriate to
that level.

Despite its now-apparent shortcomings, the learning
management system brought some important developments to the field.

First, they brought the idea that learning content could be
modularized, or 'chunked'. This enabled a more fine-grained presentation of
learning content than traditional sources such as textbooks and university
courses. Shorter-form learning content is almost ubiquitous today.

Second, it created the idea that these content modules or
chunks were sharable. The idea that books or courses could be broken down into
smaller chunks suggested to people that these chunks could be created in one
context and reused in another context.

And third, they brought together the idea of communication
and content in the same online environment. The learning management system
became a place where these smaller content objects could be presented, and then
discussed by groups of people either in a discussion board or in a live chat.

These were the core elements of learning management
technology, and a generation of online learning research and development
centered around how content should be created, managed and discussed in online
learning environments. People discussed whether this form of learning could be
equal to classroom learning, they discussed the methodology for producing these
chunks, and they discussed the nature, role and importance of inline
interaction.

Around this time as well an ambitious program began in an
effort to apply some of the generation two principles to learning management
systems (and to content management in general). We came to know this effort
under the heading of 'learning objects'. In Canada we had something called the
East-West project, which was an attempt to standardize learning resources. The
United States developed IMS, and eventually SCORM. Most of the work focused on
the development of metadata, to support discoverability and sharing, but the
core of the program was an attempt to introduce second generation technology -
interactive objects - to learning and content management.

But it didn't take hold. To this day, the learning
management system is designed essentially to present content and support
discussion and activities around that content. We can understand why when we
look at the development of the previous generations of online learning.

By the time learning management systems were developed,
operating systems and application programs, along with the content they
supported, were enterprise software. Corporations and institutions supported
massive centralized distributions. An entire college or university would
standardize on, say, Windows 3.1 (and very few on anything else). 'Content'
became synonymous with 'documents' and these documents - not something fuzzy
like 'objects' - were what would be created and published and shared.

The network was by this time well into the process of
becoming consolidated. Completely gone was the system of individual bulletin
board services; everything now belonged to one giant network. Telecoms and
large service providers such as AOL were coming to dominate access. The
internet standardized around a document presentation format - HTML - and was
defined in terms of websites and pages, constituting essentially a simplified
version of the content produced by enterprise software. The same vendors that
sold these tools - companies like Microsoft and Adobe - sold web production and
viewing tools.

Probably the most interesting developments of all at the
time were happening outside the LMS environment entirely. The tools used to
support online gaming were by this time becoming commercialized. It is worth
mentioning a few of these. New forms of games were being developed and entire
genres - strategy games, for example, sports games, and first-person shooters -
became widely popular.

Though gaming remained a largely offline activity, online
environments were also beginning to develop. One of the first 3D multi-user
environments, for example, was Alpha Worlds. This was followed by Second Life,
which for a while was widely popular. Online gaming communities also became
popular, such as the chess, backgammon and card playing sites set up by Yahoo.
And of course I would be remiss if I didn't mention online gambling sites.

As I mentioned, these developments took place outside the
LMS market. The best efforts of developers to incorporate aspects of gaming -
from object oriented learning design to simulations and gaming environments to
multi-user interactions - were of limited utility in learning management
systems. LMSs were firmly entrenched in the world of content production, and to
a lesser extent the world of networked communication.

This leads us next to the fourth generation, paradoxically
called web 2.0 - and in the field of online learning, e-learning 2.0.

The core ideas of web 2.0 almost defy description in
previous terminology. But two major phenomena describe web 2.0 - first, the
rise of social networks, and second, the creation of content and services that
can interact with those networks. Web 2.0 is sometimes described as the 'web as
a platform' but it is probably more accurate to see it as networking being
applied to data (or perhaps data being applied to networking).

The core technology of web 2.0 is social software. We are
most familiar with social software through brand names like Friendster,
MySpace, Twitter, Linked In, Facebook, and most recently, Google+. But if we
think for a moment about what social software is, it is essentially the
migration of some of your personal data - like your mailing list - to a content
management system on the web. These systems then leverage that data to create
networks. So you can now do things online - like send the same message to many
friends - that you could previously only do with specialized applications.

E-learning 2.0 is the same idea applied to e-learning
content. I am widely regarded as one of the developers of e-learning 2.0, but
this is only because I recognized that a major objective of such technologies
as learning objects and SCORM was to treat learning resources as data. The idea
was that each individual would have available online the same sort of content
authoring and distribution capabilities previously available only to major
publishers. And these would be provided online.

E-learning 2.0 brings several important developments to the
table.

First, it brings in the idea of the social graph, which is
essentially the list of people you send content to, and the list of people who
send you content, and everyone else's list, all in one big table. The social
graph defines a massive communications network in which people, rather than
computers, are the interconnected nodes.

Second, it brings in the idea of personal publishing. The
beginning of web 2.0 is arguably the development of blogging software, which
allowed people to easily create web content for the first time. But it's also
Twitter, which made creating microcontent even easier, and YouTube, which
allowed people to publish videos, and MySpace, which did the same for music,
and Facebook and Flickr, which did the same for photos.

Third, it brings in the idea of interoperability, first in
the form of syndication formats such as RSS, which allow us to share our
content easily with each other, but also later in the form or application
programming interfaces, which allow one computer program on one website to
communicate with another program on another website. These allow you to use one
application - your social network platform, for example - to use another
application - play a game, edit content, or talk to each other.

And fourth, it brings us the idea of platform-independence.
Web 2.0 is as much about mobile computing as it is about social software. It is
as much about using your telephone to post status updates or upload photos as
it is about putting your phonebook on a website. Maybe even more so.

What made web 2.0 possible? In a certain sense, it was the
maturation of generation 0, web content and applications. After being
developed, commercialized and consolidated, these became enterprise services.
But as enterprises became global, these two become global, and emerged out of
the enterprise to become cloud and mobile contents and applications.

Some of the major social networking sites are actually
cloud storage sites - YouTube and Flickr are the most obvious examples. Some
are less obvious, but become so when you think about it - Wikipedia, for
example. Other cloud storage sites operate behind the scenes, like Internet
Archive and Amazon Web Services. And there are cloud services, like Akamai,
that never reach the mainstream perception.

These cloud services developed as a result of enterprise
networking. On the research side, high-speed backbones such as Internet 2 in
the U.S. and CA*Net 3 in Canada virtually eliminated network lag even for large
data files, audio and video. Similar capacities were being developed for lease
by the commercial sector. And the now-consolidated consumer market now began to
support always-on broadband capacity through ASDL or cable internet services.

The consolidation of core gaming technologies took place
largely behind the scenes. This era sees the ascendance of object-oriented
coding languages such as Java and dot Net. The open-ended online environment
led to massive multiplayer online games such as Eve and World of Warcraft. In
learning we see the emergence of major simulation developers such as CAE and
conferencing systems such as Connect, Elluminate, and Cisco. These have become
dominant in the delivery of online seminars and classes.

Content management services, meanwhile, were increasingly
commercialized. We saw the emergence of Blackboard and WebCT, and on the
commercial side products like Saba and Docent. Google purchased Blogger, Yahoo
purchased Flickr, and even the world of open source systems came to be
dominated by quasi-commercial enterprises. Innovators moved on and began to try
radical new technologies like RSS and AJAX, Twitter and Technorati. Today we
think of social networking in terms of the giants, but when it started in the
mid-2000s the technology was uncertain and evolving. In education, probably the
major player from this era was Elgg, at that time and still to this day a novel
technology.

Today, of course, social networking is ubiquitous. The
major technologies have been commercialized and are moving rapidly toward
commodification and enterprise adoption. The ubiquity of social networking came
about as a result of the commercialization of content management services. A
new business model has emerged in which providers sell information about their
users to marketing agencies. The proliferation of social networking sites has
now been reduced to a few major competitors, notably YouTube, Facebook and
Twitter. The providers of search and document management services - Yahoo,
Microsoft, Apple and Google - have their own social networks, but these are
also-rans. Hence when people speak of 'social network learning' they often mean
'using Facebook to support learning' or some such thing.

This is the beginning of the sixth generation, a generation
characterized by commercialized web 2.0 services, a consolidation of the
CMS/LMS market, the development of enterprise conferencing and simulation
technology, cloud networking and - at last - open content and open operating
systems.

Now before the Linux advocates lynch me, let me say that,
yes, there have always been open operating systems. But - frankly - until
recently they have always been the domain of innovators, enthusiasts and hobbyists.
Not mainstream - not, say, running underlying major commercial brands, the way
Linux now underlies Apple's OSX, and not widely used, say, the way Android
powers a large percentage of mobile phones.

So that's the history of online learning through five
generations, but it is also a listing of the major technologies that form the
foundation for sixth-generation e-learning, which I would characterized by the
Massive Open Online Course.

Let me spend a few moments talking about the development of the MOOC model.

When George Siemens and I created the first MOOC in 2008 we
were not setting out to create a MOOC. So the form was not something we
designed and implemented, at least, not explicitly so. But we had very clear
ideas of where we wanted to go, and I would argue that it was those clear ideas
that led to the definition of the MOOC as it exists today.

There were two major influences. One was the beginning of
open online courses. We had both seen them in operation in the past, and had
most recently been influenced by Alec Couros's online graduate course and David
Wiley's wiki-based course. What made these courses important was that they
invoked the idea of including outsiders into university courses in some way.
The course was no longer bounded by the institution.

The other major influence was the emergence of massive
online conferences. George had run a major conference on Connectivism, in which
I was a participant. This was just the latest in a series of such conferences.
Again, what made the format work was that the conference was open. And it was
the success of the conference that made it worth considering a longer and more
involved enterprise.

We set up Connectivism and Connective Knowledge 2008
(CCK08) as a credit course in Manitoba's Certificate in Adult Education (CAE),
offered by the University of Manitoba. It was a bit of Old Home Week for me, as
Manitoba's first-ever online course was also offered through the CAE program,
Introduction to Instruction, designed by Conrad Albertson and myself, and
offered by Shirley Chapman.

What made CCK08 different was that we both decided at the
outset that it would be designed along explicitly connectivist lines, whatever
those were. Which was great in theory, but then we began almost immediately to
accommodate the demands of a formal course offered by a traditional
institution. The course would have a start date and an end date, and a series
of dates in between, which would constitute a course schedule. Students would
be able to sign up for credit, but if they did, they would have assignments
that would be marked (by George; I had no interest in marking).

But beyond that, the course was non-traditional. Because
when you make a claim like the central claim of connectivism, that the
knowledge is found in the connections between people with each other and that
learning is the development and traversal of those connections, then you can't
just offer a body of content in an LMS and call it a course. Had we simply
presented the 'theory of connectivism' as a body of content to be learned by
participants, we would have undercut the central thesis of connectivism.

This seems to entail offering a course without content -
how do you offer a course without content? The answer is that the course is not
without content, but rather, that the content does not define the course. That
there is no core of content that everyone must learn does not entail that there
is zero content. Quite the opposite. It entails that there is a surplus of
content. When you don't select a certain set of canonical contents, everything
becomes potential content, and as we saw in practice, we ended up with a lot of
content.

Running the course over fourteen weeks, with each week
devoted to a different topic, actually helped us out. Rather than constrain us,
it allowed us to mitigate to some degree the effects an undifferentiated
torrent of content would produce. It allowed us to say to ourselves that we'll
look at 'this' first and 'that' later. It was a minimal structure, but one that
seemed to be a minimal requirement for any sort of coherence at all.

Even so, as it was, participants complained that there was
too much information. This led to the articulation of exactly what connectivism
meant in a networked information environment, and resulted in the definition of
a key feature of MOOCs. Learning in a MOOC, we advised, is in the first
instance a matter of learning how to select content.

By navigating the content environment, and selecting content
that is relevant to your own personal preferences and context, you are creating
an individual view or perspective. So you are first creating connections
between contents with each other and with your own background and experience.
And working with content in a connectivist course does not involve learning or
remembering the content. Rather, it is to engage in a process of creation and
sharing. Each person in the course, speaking from his or her unique
perspective, participates in a conversation that brings these perspectives
together.

Why not learn content? Why not assemble a body of
information that people would know in common? The particular circumstances of
CCK08 make the answer clear, but we can also see how it generalizes. In the
case of CCK08, there is no core body of knowledge. Connectivism is a theory in
development (many argued that it isn't even a theory), and the development of
connective knowledge even more so. We were hesitant to teach people something
definitive when even we did not know what that would be.

Even more importantly, identifying and highlighting some
core principles of connectivism would undermine what it was we thought
connectivism was. It's not a simple set of principles or equations you apply
mechanically to obtain a result. Sure, there are primitive elements - the
component of a connection, for example - but you move very quickly into a realm
where any articulation of the theory, any abstraction of the principles,
distorts it. The fuzzy reality is what we want to teach, but you can't teach
that merely by assembling content and having people remember it.

So in order to teach connectivism, we found it necessary
for people to immerse themselves in a connectivist teaching environment. The
content itself could have been anything - we have since run courses in critical
literacies, learning analytics, and personal learning environments. The content
is the material that we work with, that forms the creative clay we use to
communicate with each other as we develop the actual learning, the finely
grained and nuanced understanding of learning in a network environment that
develops as a result of our working within a networked environment.

In order to support this aspect of the learning, we decided
to make the course as much of a network as possible, and therefore, as little
like an ordered, structured and centralized presentation as possible. Drawing
on work we'd done previously, we set up a system whereby people would use their
own environments, whatever they were, and make connections between each other
(and each other's content) in these environments.

To do this, we encouraged each person to create his or her
own online presence; these would be their nodes in the course networks. We
collected RSS feeds from these and aggregated them into a single thread, which
became the course newsletter. We emphasized further that this thread was only
one of any number of possible ways of looking at the course contents, and we
encouraged participants to connect in any other way they deemed appropriate.

This part of the course was a significant success. Of the
2200 people who signed up for CCK08, 170 of them created their own blogs, the
feeds of which were aggregated with a tool I created, called gRSShopper, and
the contents delivered by email to a total of 1870 subscribers (this number
remained constant for the duration of the course). Students also participated
in a Moodle discussion forum, in a Google Groups forum, in three separate
Second Life communities, and in other ways we didn't know about.

The idea was that in addition to gaining experience making
connections between people and ideas, participants were making connections
between different systems and places. What we wanted people to experience was
that connectivism functions not as a cognitive theory - not as a theory about
how ideas are created and transmitted - but as a theory describing how we live
and grow together. We learn, in connectivism, not by acquiring knowledge as
though it were so many bricks or puzzle pieces, but by becoming the sort of
person we want to be.

In this, in the offering of a course such as CCK08, and in
the offering of various courses after, and in the experience of other people
offering courses as varied as MobiMOOC and ds106 and eduMOOC, we see directly
the growth of individuals into the theory (which they take and mold in their
own way) as well as the growth of the community of connected technologies,
individuals and ideas. And it is in what we learn in this way that the
challenge to more traditional theories becomes evident.

Now I mentioned previously that the MOOC represents a new
generation of e-learning. To understand what that means we need to understand
what the MOOC is drawing from the previous generations, and what the MOOC
brings that is new.

Let me review:

Generation 0 brings us the idea of documents and other
learning content, created and managed using application programs. In this the
sixth generation of such technologies we have finally emerged into the world of
widespread free and open online documents and application programs. The ability
to read and write educational content, to record audio and make video, is now
open to everybody, and we leverage this in the MOOC. But this is not what makes
the MOOC new.

Additionally, a fundamental underlying feature of a
connectivist course is the network, which by now is in the process of becoming
a cloud service. WiFi is not quite ubiquitous, mobile telephony is not quite
broadband, but we are close enough to both that we are connected to each other
on an ongoing basis. The MOOC leverages the network, and increasingly depends
on ubiquitous access, but this is not what makes the MOOC new.

The MOOC as we have designed it also makes use of
enterprise 'game' technology, most specifically the conferencing system.
Elluminate has been a staple in our courses. We have also used - and may well
use again in the future - environments such as Second Life. Some other courses,
such as the Stanford AI course, have leveraged simulations and interactive
systems. Others, like ds106, emphasize multimedia. Using these and other
immersive technologies, the MOOC will become more and more like a personal
learning environment, but this is not what makes the MOOC unique.

The MOOC also makes explicit use of content management systems.
The early MOOCs used Moodle; today we encourage participants to use personal
content management systems such as WordPress and Blogger. The gRSShopper
environment itself is to a large degree a content management system, managing a
large store of user contributions and facilitator resources. But clearly, the
element of content management is not what makes the MOOC new.

And the MOOC makes a lot of use of commercial social
networking services. Twitter feeds and the Facebook group are major elements of
the course. Many students use microblogging services like Posterous and Tumblr.
Like membership in a social network, membership in the course constitutes
participation in a large graph; contents from this graph are aggregated and
redistributed using social networking channels and syndication technologies.
But many courses make use of social networks. So that is not what makes a MOOC
unique.

So what's new? I would like to suggest that the MOOC adds
two major elements to the mix, and that it is these elements that bear the most
investigation and exploration.

First, the MOOC brings the idea of distributed technology
to the mix. In its simplest expression, we could say that activities do not
take place in one central location, but rather, are distributed across a large
network of individual sites and services. The MOOC is not 'located' at
cck12.mooc.ca (or at least, it's not intended to me) - that is just one nexus
of connected sites.

In fact, it is the idea of distributed knowledge that is introduced by the MOOC again, and the means of learning is really involved with this
idea. When you learn as a network, you cannot teach one fact after another.
Each fact is implicated with the others. You cannot
see a single
fact, even if you extract a fact from the data, because it would be only one abstraction, an
idealization, and not more true that
the identification of regularities
in the data - and
learning becomes more like a process to create landforms, and
less like an exercise of memory. It
is the process
of pattern recognition that we want to develop, and not the remembering of facts.

Accordingly, the second element the MOOC brings to the mix
revolves around the theory of effective networks. More deeply, the MOOC
represents the instantiation of four major principles of effective distributed
systems. These principles are, briefly, autonomy, diversity, openness and
interactivity.

For example, it is based on these principles that we say
that it is better to obtain many points of view than one. It is based on these
principles that we say that the knowledge of a collection of people is greater
than just the sum of each person’s knowledge. It is based on these principles
that we argue for the free exchange of knowledge and ideas, for open education,
for self-determination and personal empowerment.

These four principles form the essence of the design of the
network - the reason, for example, we encourage participants to use their
preferred technology (it would be a lot easier if everybody used WordPress).

We are just now as a community beginning to understand what
it means to say this. Consider 'learning analytics', for example, which is an
attempt to learn about the learning process by examining a large body of data.

What is learned in the process of learning analytics is not
what is contained in individual bits of data - that would be ridiculous - but
overall trends or patterns. What is learned, in other words, emerges from the
data. The things we are learning today are very simple. In the future we expect
to learn things that are rather more subtle and enlightening.

Let me now say a few words in closing about Generation 6
and beyond.

From my perspective, the first three generations of
e-learning (and the web generally) represent a focus on documents, while the
second three represent a focus on data. Sometimes people speak of the second
set as a focus on the Semantic Web, and they would not be wrong. Data does not
stand alone, the way documents do; the representation of any object is
connected to the representation of any number of other objects, through shared
features or properties, or by being related by some action or third party
agency.

Indeed, if the first three generations are contents,
networks and objects respectively, the second three generations are those very
same things thought of as data: the CMS is content thought of as data, web 2.0
is the network thought of as data, and the MOOC is the environment thought of
as data. So what comes after data is pretty important, but I would say, it is
also to a certain degree knowable, because it will have something to do with
content, the network, and the environment.

Here's what I think it will be - indeed, here's what I've
always thought it would be. The next three generations of web and learning
technology will be based on the idea of flow.

Flow is what happens when your content and your data
becomes unmanageable. Flow is what happens when all you can do is watch it as
it goes by - it is too massive to store, it is too detailed to comprehend. Flow
is when we cease to think of things like contents and communications and even
people and environments as things and start thinking of them as (for lack of a
better word) media - like the water in a river, like the electricity in our
pipes, like the air in the sky.

The first of these things that flow will be the outputs of
learning (and other) analytics; they will be the distillation of the massive
amounts of data, presented to us from various viewpoints and perspectives,
always changing, always adapting, always fluid.

Inside the gRSShopper system I am working toward the
development of the first sort of engines that capture and display this flow.
gRSShopper creates a graph of all links, all interactions, all communications.
I don't know what to do with it yet, but I think that the idea of comprehending
the interactions between these distributed systems in a learning network is an
important first step to understanding what is learned, how it is learned, and
why it is learned. And with that, perhaps, we can take our understanding of
online learning a step further.

But that, perhaps, may take the efforts of another
generation.

Thank you.

Read More

How to Write Articles and Essays Quickly and Expertly

From time to time people express amazement at how I can get so much
done. I, of course, aware of the many hours I have idled away doing
nothing, demur. It feels like nothing special; I don't work harder,
really, than most people. Nonetheless, these people do have a point. I
am, in fact, a fairly prolific writer.

Part of it is tenacity. For example, I am writing this item as I wait
for the internet to start working again in the Joburg airport departures
area. But part of it is a simple strategy for writing you essays and
articles quickly and expertly, a strategy that allows you to plan your
entire essay as you write it, and thus to allow you to make your first
draft your final draft. This article describes that strategy.



Begin by writing - in your head, at least - your second paragraph (that
would be the one you just read, above). Your second paragraph will tell
people what your essay says. Some people write abstracts or executive
summaries in order to accomplish this task. But you don't need to do
this. You are stating your entire essay or article in one paragraph. If
you were writing a news article, you would call this paragraph the
'lede'. A person could read just the one paragraph and know what you had
to say.



But how do you write this paragraph? Reporters will tell you that
writing the lede is the hardest part of writing an article. Because if
you don't know what the story is, you cannot write it in a single
paragraph. A reporter will sift through the different ways of writing
the story - the different angles - and find a way to tell it. You,
because you are writing an article or essay, have more options.



You have more options because there are four types of discursive
writing. Each of these types has a distinct and easy structure, and once
you know what sort of writing you are doing, the rest of the article
almost writes itself. The four types of structure are: argument,
explanation, definition, and description. So, as you think about writing
your first paragraph, ask yourself, what sort of article are you
writing. In this article, for example, I am writing a descriptive
article.



These are your choices of types of article or essay:



Argument: convinces someone of something

Explanation: tells why something happened instead of something else

Definition: states what a word or concept means

Description: identifies properties or qualities of things



An argument
is a collection of sentences (known formally as 'propositions') intended
to convince the reader that something is he case. Perhaps you want to
convince people to take some action, to buy some product, to vote a
certain way, or to believe a certain thing. The thing that you want to
convince them to believe is the conclusion. In order to convince people,
you need to offer one or more reasons. Those are the premises. So one
type of article consists of premises leading to a conclusion, and that
is how you would structure your first paragraph.



An explanation
tells the reader why something is the case. It looks at some event or
phenomenon, and shows the reader what sort of things led up to that
event or phenomenon, what caused it to happen, why it came to be this
way instead of some other way. An explanation, therefore, consists of
three parts. First, you need to identify the thing being explained.
Then, you need to identify the things that could have happened instead.
And finally, you need to describe the conditions and principles that led
to the one thing, and not the other, being the case. And so, if you are
explaining something, this is how you would write your first paragraph.



A definition
identifies the meaning of some word, phrase or concept. There are
different ways to define something. You can define something using words
and concepts you already know. Or you can define something by giving a
name to something you can point to or describe. Or you can define
something indirectly, by giving examples of telling stories. A
definition always involves two parts: the word or concept being defined,
and the set of sentences (or 'propositions') that do the defining.
Whatever way you decide, this will be the structure of your article if
you intend to define something.



Finally, a description
provides information about some object, person, or state of affairs. It
will consist of a series of related sentences. The sentences will each
identify the object being defined, and then ascribe some property to
that object. "The ball is red," for example, were the ball is the object
and 'red' is the property. Descriptions may be of 'unary properties' -
like colour, shape, taste, and the like, or it may describe a relation
between the object and one or more other objects.



Organizing Your Writing



The set of sentences, meanwhile, will be organized on one of a few common ways. The sentences might be in chronological order. "This happened, and then this happened," and so on. Or they may enumerate a set of properties ('appearance', 'sound', 'taste', 'small', 'feeling about', and the like). Or they may be elements of a list ("nine rules for good technology," say, or "ten things you should learn"). Or, like the reporters, you may cover the five W's: who, what, where, when, why. Or the steps required to write an essay.



When you elect to write an essay or article, then, you are going to
write one of these types of writing. If you cannot decide which type,
then your purpose isn't clear. Think about it, and make the choice,
before continuing. Then you will know the major parts of the article -
the premises, say, or the parts of the definition. Again, if you don't
know these, your purpose isn't clear. Know what you want to say (in two
or three sentences) before you decide to write.



You may a this point be wondering what happened to the first paragraph.
You are, after all, beginning with the second paragraph. The first
paragraph is used to 'animate' your essay or article, to give it life
and meaning and context. In my own writing, my animation is often a
short story about myself showing how the topic is important to me.
Animating paragraphs may express feelings - joy, happiness, sadness, or
whatever. They may consist of short stories or examples of what you are
trying to describe (this is very common in news articles). Animation may
be placed into your essay at any point. But is generally most effective
when introducing a topic, or when concluding a topic.



For example, I have now concluded the first paragraph of my essay, and
then expanded on it, thus ending the first major part of my essay. So
now I could offer an example here, to illustrate my point in practice,
and to give the reader a chance to reflect, and a way to experience some
empathy, before proceeding. This is also a good place to offer a
picture, diagram, illustration or chart of what you are trying to say in
words.



Like this: the second paragraph sill consist of a set of statements. Here is what each of the four types look like:



Argument:



Premise 1

Premise 2 ... (and more, if needed)

Conclusion



Explanation:



Thing being explained

Alternative possibilities

Actual explanation



Definition:



Thing being defined

Actual definition



Description:



Thing being described

Descriptive sentence

Descriptive sentence (and more, connected to the rest, as needed)



So now the example should have made the concept clearer. You should
easily see that your second paragraph will consist of two or more
distinct sentences, depending on what you are trying to say. Now, all
you need to do is to write the sentences. But also, you need to tell
your reader which sentence is which. In an argument, for example, you
need to clearly indicate to the reader which sentence is your conclusion
and which sentences are your premises.



Indicator Words



All four types of writing have their own indicator words. Let's look at
each of the four types in more detail, and show (with examples, to
animate!) the indicator words.



As stated above, an argument will consist of a conclusion and some
premises. The conclusion is the most important sentence, and so will
typically be stated first. For example, "Blue is better than red." Then a
premise indicator will be used, to tell the reader that what follows is
a series of premises. Words like 'because' and 'since' are common
premise indicators (there are more; you may want to make a list). So
your first paragraph might look like this: "Blue is better than red, because blue is darker than red, and all colours that are darker are better."



Sometimes, when the premises need to be stressed before the conclusion
will be believed, the author will put the conclusion at the end of the
paragraph. To do this, the author uses a conclusion indicator. Words
like 'so' and 'therefore' and 'hence' are common conclusion indicators.
Thus, for example, the paragraph might read: "Blue is darker than red,
and all colours that are darker are better, so blue is better than red."



You should notice that indicator words like this help you understand
someone else's writing more easily as well. Being able to spot the
premises and the conclusion helps you spot the structure of their
article or essay. Seeing the conclusion indicator, for example, tells
you that you are looking at an argument, and helps you spot the
conclusion. It is good practice to try spotting arguments in other
writing, and to create arguments of your own, in our own writing.



Arguments
can also be identified by their form. There are different types of
argument, which follow standard patterns of reasoning. These patterns of
reasoning are indicated by the words being used. Here is a quick guide
to the types of arguments:



Inductive argument:
the premise consists of a 'sample', such as a series of experiences, or
experimental results, or polls. Watch for words describing these sorts
of observation. The conclusion will be inferred as a generalization from
these premises. Watch for words that indicate a statistical
generalization, such as 'most', 'generally, 'usually', 'seventy
percent', 'nine out of ten'. Also, watch for words that indicate a
universal generalization, such as 'always' and 'all'.



A special case of the inductive argument is the causal generalization.
If you want someone to believe that one thing causes another, then you
need to show that there are many cases where the one thing was followed
by the other, and also to show that when the one thing didn't happen,
then the other didn't either. This establishes a 'correlation'. The
argument becomes a causal argument when you appeal to some general
principle or law of nature to explain the correlation. Notice how, in
this case, an explanation forms one of the premises of the argument.



Deductive argument: the premises consist of propositions, and the conclusion consists of some logical manipulation of the premises. A categorical
argument, for example, consists of reasoning about sets of things, so
watch for words like 'all', 'some' and 'none'. Many times, these words
are implicit; they are not started, but they are implied. When I said
"Blue is better than red" above, for example, I meant that "blue is
always better than red," and that's how you would have understood it.



Another type of deductive argument is a propositional argument.
Propositional arguments are manipulations of sentences using the words
'or', 'if', and 'and'. For example, if I said "Either red is best or
blue is best, and red is not best, so blue is best," then I have
employed a propositional argument.



It is useful to learn the basic argument forms, so you can very clearly
indicate which type of argument you are providing. This will make your
writing clearer to the reader, and will help them evaluate your writing.
And in addition, this will make easier for you to write your article.



See how the previous paragraph is constructed, for example. I have
stated a conclusion, then a premise indicator, and then a series of
premises. It was very easy to writing the paragraph; I didn't even need
to think about it. I just wrote something I thought was true, then
provided a list of the reasons I thought it was true. How hard is that?



In a similar manner, an explanation
will also use indicator words. In fact, the indicator words used by
explanations are very similar to those that are used by arguments. For
example, I might explain by saying "The grass is green because it rained
yesterday." I am explaining why the grass is green. I am using the word
'because' as an indicator. And my explanation is offered following the
word 'because'.



People often confuse arguments and explanations, because they use
similar indicator words. So when you are writing, you can make your
point clearer by using words that will generally be unique to
explanations.



In general, explanations are answers to 'why' questions. They consider
why something happened 'instead of' something else. And usually, they
will say that something was 'caused' by something else. So when offering
an explanation, use these words as indicators. For example: "It rained
yesterday. That's why the grass is green, instead of brown."



Almost all explanations are causal explanations, but in some cases (especially when describing complex states and events) you will also appeal to a statistical explanation.
In essence, in a statistical explanation, you are saying, "it had to
happen sometime, so that's why it happened now, but there's no reason,
other than probability, why it happened this time instead o last time or
next time." When people see somebody who was killed by lightening, and
they say, "His number was just up," they are offering a statistical
explanation.



Definitions are
trickier, because there are various types of definition. I will
consider three types of definition: ostensive, lexical, and implicit.



An 'ostensive' definition is an
act of naming by pointing. You point to a dog and you say, "That's a
dog." Do this enough times, and you have defined the concept of a dog.
It's harder to point in text. But in text, a description amounts to the
same thing as pointing. "The legs are shorter than the tail. The colour
is brown, and the body is very long. That's what I mean by a 'wiener
dog'." As you may have noticed, the description is followed by the
indicator words "that's what I mean by". This makes it clear to the
reader that you are defining by ostension.



A 'lexical' definition is a
definition one word or concept in terms of some other word or concept.
Usually this is describes as providing the 'necessary and sufficient
conditions' for being something. Another way of saying the same thing is
to say that when you are defining a thing, you are saying that 'all and
only' these things are the thing being defined. Yet another way of
saying the same thing is to say that the thing belongs to such and such a
category (all dogs are animals, or, a dog is necessarily an animal) and
are distinguished from other members in such and such a way (only dogs
pant, or, saying a thing is panting is sufficient to show that it is a
dog).



That may seem complicated, but the result is that a lexical definition
has a very simply and easy to write form: A (thing being defined) is a
type of (category) which is (distinguishing feature). For example, "A
dog is an animal that pants."



This sentence may look just like a description, so it is useful to
indicate to the reader that you are defining the term 'dog', and not
describing a dog. For example, "A 'dog' is defined as 'an animal that
pants'." Notice how this is clearly a definition, and could not be
confused as a mere description.



The third type of definition is an implicit
definition. This occurs when you don't point to things, and don't place
the thing being defined into categories, but rather, list instances of
the thing being defined. For example, "Civilization is when people are
polite to each other. When people can trust the other person. When there
is order in the streets." And so on. Or: "You know what I mean. Japan
is civilized. Singapore is civilized. Canada is civilized." Here we
haven't listed necessary and sufficient conditions, but rather, offered
enough of a description as to allow people to recognize instances of
'civilization' by their resemblance to the things being described.



Finally, the description
employs the 'subject predicate object' form that you learned in school.
The 'subject' is the thing being described. The 'predicate' is
something that is true of the subject - some action it is undertaking,
or, if the predicate is 'is', some property that it possesses. And the
'object' may be some other entity that forms a part of the description.



As mentioned, the sentences that form a description are related to each
other. This relation is made explicit with a set of indicator words. For
example, if the relation is chronological, the words might be
'first'... 'and then'... 'and finally'...! Or, 'yesterday'... 'then
today'... 'and tomorrow'...



In this essay, the method employed was to identify a list of things -
argument, explanation, definition, and description - and then to use
each of these terms in the sequence. For example, "An argument will
consist of a ..." Notice that I actually went through this list twice,
first describing the parts of each of the four items, and then
describing the indicator words used for each of the four items. Also,
when I went through the list the second time, I offered for each type of
sentence a subdivision. For example, I identified inductive and
deductive arguments.



Summary



So, now, here is the full set of types of things I have described (with indicator words in brackets):



Argument (premise: 'since', 'because'; conclusion: 'therefore', 'so')

Deductive

Categorical ('all', 'only', 'no', 'none', 'some')

Propositional ('if', 'or', 'and')

Inductive

Generalization ('sample', 'poll', 'observation')

Statistical ('most', 'generally, 'usually', 'seventy percent', 'nine out of ten')

Universal ('always' and 'all')

Causal ('causes')



Explanation ('why', 'instead of')

Causal ('caused')

Statistical ('percent', 'probability')



Definition ('is a', 'is defined as')

Ostensive ( 'That's what I mean by...' )

Lexical ('All', 'Only', 'is a type of', 'is necessarily')

Implicit ('is a', 'for example')



Description

Chronology ('yesterday', 'today')

Sensations ('seems', 'feels', 'appears', etc.,)

List ('first', 'second', etc.)

5 W's ('who', 'what', 'where', 'when', 'why')



Complex Forms



As you have seen in this article, each successive iteration (which has
been followed by one of my tables) has been more and more detailed. You
might ask how this is so, if there are only four types of article or
essay.



The point is, each sentence in one type of thing might be a whole set of
sentence of another type of thing. This is most clearly illustrated by
looking at an argument.



An argument is a conclusion and some premises. Like this:



Statement 1, and

Statement 2,

Thus,

Statement 3



But each premise might in turn be the conclusion of another argument. Like this:



Statement 4, and

Statement 5,

Thus,

Statement 1



Which gives us a complex argument:



Statement 4, and

Statement 5,

Thus, Statement 1

Statement 2

Thus Statement 3



But this can be done with all four types of paragraph. For example, consider this:



Statement 1 (which is actually a definition, with several parts)

Statement 2 (which is actually a description)

Thus,

Statement 3



So, when you write your essay, you pick the main thing you want to say. For example:



Second paragraph:



Statement 1, and

Statement 2

Thus

Statement 3



Third paragraph:



Statement 4 (thing being defined)

Statement 5 (properties)

Statement 1 (actual definition)



Fourth Paragraph



Statement 5 (first statement of description)

Statement 6 (second statement of description)

Statement 2 (summary of description)



As you can see, each simple element of an essay - premise, for example -
can become a complex part of an essay - the premise could be the
conclusion of an argument, for example.



And so, when you write your essay, you just go deeper and deeper into the structure.



And you may ask: where does it stop?



For me, it stops with descriptions - something I've seen or experienced,
or a reference to a study or a paper. To someone else, it all reduces
to definitions and axioms. For someone else, it might never stop.



But you rarely get to the bottom. You simply go on until you've said
enough. In essence, you give up, and hope the reader can continue the
rest of the way on his or her own.



And just so with this paper. I would now look at each one of each type
of argument and explanation, for example, and identify more types, or
describe features that make some good and some bad, or add many more
examples and animations.



But my time is up, I need to board my flight, so I'll stop here.



Nothing fancy at the end. Just a reminder, that this is how you can
write great articles and essays, first draft, every time. Off the top of
your head.

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Research-based proof that students use cell phones for LEARNING

A new study conducted by TRU provides a body of research which supports the idea that students use cell phones to learn, and also that schools are not acknowledging or supporting them fully, yet. This research supports the work of  innovative educators who are guiding today’s generation text and will help in the effort of getting more schools to stop fighting and start embracing student use of mobile devices for learning in school. Rather than banning, the study highlights the fact that if we meet children where they are we can leverage their use of mobile devices for powerful lear

ning.

The research supports the fact that mobile technology can inspire and engage students by letting them lead their learning and supporting them in choosing and using the devices they know, love, and prefer. The study reveals that whether allowed to use their devices in school or not, students are moving forward and using them for learning even if their school is lagging behind in embracing student-owned devices.

Kids FINALLY have a case for why they really need mobile devices to learn. The survey is the first of its kind and examines how middle school students are using mobile devices, revealing that these tools are actually helping kids learn math and science better, and increasing their confidence and motivation, despite the fact that most schools (88%) strictly forbid their use for learning.

Despite the perception by some parents and teachers that cell phones are distracting to kids, this national study proves that children deserve more credit as 1 in 3 are using their devices to complete homework and learn better.

Here are some of the most exciting findings from the study:

• "An unexpected number of middle school students (from all ethnicities and incomes) say they are using mobile devices including smartphones and tablets to do their homework. Previous TRU research indicated that middle school students are using smartphones and tablets for communication and entertainment. However, this is the first TRU research that shows that middle school students are also using these mobile devices to complete homework assignments.
• More than one out of three middle school students report they are using smartphones (39%) and tablets (31%) to do homework.
• More than 1 in 4 students ( 26 %) are using smartphones for their homework, weekly or more.
• Hispanic and African American middle school students are using the smartphones for homework more than Caucasian students. Nearly one half of all Hispanic middle school students (49%) report using smartphones for homework. Smartphone use for homework also crosses income levels with nearly one in three (29%) of students from the lowest income households reporting smartphone usage to do their homework assignments.  (a quota was set to ensure a minimum of 200 respondents with a household income of $25,000 or less.)
• Despite the high numbers of middle school students using laptops, smartphones and tablets for homework, very few are using these mobile devices in the classroom, particularly tablets and smartphones. A large gap exists between mobile technology use at home and in school.
• Where 39% of middle school students use smartphones for homework, only 6% report that they can use the smartphone in classroom for school work. There is also a gap in tablet use. Although 31% of middle school students say they use a tablet for homework, only 18% report using it in the classroom.
• 66% of students are not allowed to use a tablet for learning purposes in the classroom, and 88% are not allowed to use a phone.
• Students say using mobile devices like tablets makes them want to learn more.

• A significant opportunity appears to exist for middle schools to more deeply engage students by increasing their use of mobile devices in the classroom.
• Access to mobile devices at home is high among this group, and students are already turning to these devices to complete homework assignments. Therefore, it is only natural and highly beneficial for students to extend this mobile device usage into the classroom.
• Teacher education and training on the effective integration of mobile technologies into instruction may provide significant benefits for all. Mobile device usage in class appears to have the potential to sustain, if not increase interest in STEM subjects as students progress into high school.

It’s time to spread the world and ensure educators know the wealth of ways to safely, ethically, and effectively utilize the power of mobile technology with students for homework and IN the classroom. For ideas and support in using cell phones for learning check out Teaching Generation Text: Using Cell Phones to Enhance Learning.

Survey Methodology

Verizon Foundation

commissioned TRU to conduct quantitative research on middle school students’ use of technology.  TRU conducted 1,000 online interviews among sixth- to eighth-grade students, ages 11-14, yielding a margin of error of + 3.0 percentage points. A quota was set to ensure a minimum of 200 respondents with a household income of $25,000 or less.  Unless otherwise noted, all reported data is based on a statistically reliable base size of n=100 or greater.
TRU is the global leader in youth research and insights, focusing on tweens, teens and twenty-somethings. For more than 25 years, TRU has provided the insights that have helped many of the world's most successful companies and organizations develop meaningful connections with young people. As an advocate for young people, TRU has provided critical direction for many of the nation’s most prominent and successful social-marketing campaigns, helping to keep young people safe and healthy. TRU’s work has made a difference – from being put to use at the grass-roots level to being presented at the very highest levels of government.

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