“Networked Learning Communities–The Benefits for Continuing Professional Developmentof Virtual Learning Environment Teachers”A Critical Literature Review by Chris O Tool

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Review covers literature for the period from 1996 to 2018: What are the benefits of networked learning communities for continuing professional development sharing and learning?
In the area of teacher professional development, research has shown that teacher networks add value for their development, the implementation of changes, leadership, and improved teaching practices. Four main themes emerged in response to the primary research question. These were:

  • Enhanced social learning processes for CPD: learning communities help participants in this study to develop their competencies by sharing information and collaboration / helps to minimize the isolation that learners may have due to cultural, social or geographical reasons
  • Greater use of formal and informal learning for CPD: Communication, collaboration and learning between individuals occurs both through formal and informal networks/ Yet, formal learning paths are rarely designed to meet the demands VLE teachers face in professional practice
  • Learning across barriers in time and space for CPD: Networked learning communities provide a means for supporting the development of professional development learning communities across states and countries
  • Increased levels of interaction for CPD: By cultivating interaction among CPD learners, networked learning communities support profound learning and greater levels of professional practice

Networked learning theory suggests that the real power of networked learning communities rests primarily in “collaborative inquiry that challenges thinking and practice” based on the richness of VLE teacher professional knowledge sharing and creation (Katz, Earl, & Jaffar, 2009, p.21) and that this type of collaborative inquiry rests on the strength of the relationships between the actors or nodes in the network (Church et al., 2002; Haythornwaite, & de Laat, 2010)

Full article available here

Student resistance to curriculum changes

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Sometimes, when we talk about learner independence, active learning or agency, we forget that this is not always for granted. Student consensus can not be considered a given. Trying out new things in a course (changing formats, layouts or mediums) produces changes that can be met with resistance and suspicion and it usually takes time until the cohort is convinced that what you are doing is actually working for them.

Student-Centered Learning and Student Buy-In article in Inside Higher Ed shows the results of curriculum change in a Biology course over a period of four years in relation to student satisfaction and acceptance. Pre- and post- course surveys show that student resistance decreased over the years and while grades did not change, the students’ perception of their gains has.

I remember that when we first introduced networked practices in an undergraduate design studio, students were terrified of the idea that their preliminary research and drawings would be published online for everyone to see. When talking about this, some expressed the fear that their ideas would loose their originality or that by the end of the semester everyone would converge to a single design idea/concept. Of course, none of this happened: in fact, it was quite revealing to see how diverse the research approaches and their respective representations actually were from a very early stage in the design process.

But there is also another interesting aspect in this article: the very fact that there was no single teacher but 13 of them. Now, I think this severely enhances the idea of a learning community. It’s not just about changing the format, it is about how you do it. By opening up the curriculum to more researchers and more teachers and by presenting the students with a course that is founded on a collaborative effort you ultimately denounce the idea of the expert and what comes along with that. And it is not by chance that grades have nothing to do with this. The very act of learning and being part of a learning community luckily can never fall into the hands of assessment.

Where do trees come from? Graphs!

TREES & GRAPHS

Trees start from a root node and might connect to other nodes, which means that could contain subtrees within them. Trees are defined by a certain set of rules: one root node may or may not connect to others, but ultimately, it all stems from one specific place. The tree follows one direction and cannot have loops or cyclical links.

Graphs are non linear structures: their data doesn’t follow an order. Trees will always be graphs, but not all graphs will be trees. Graphs do not have a concept of a root node. They can have a direction or not or they could have some links that have direction and others that don’t. Every graph must have at least one single node. (a graph with one node is called singleton).

Edges (sometimes referred to as links) can connect nodes in any way possible. Edges are what differentiates graphs. There are two types of edges: a edge that has a direction or flow, and an edge that has no direction or flow. We refer to these as directed and undirected edges, respectfully. In a directed edge, we can only travel from the origin to the destination, and never the other way around (digraph). However, it’s an entirely different story with undirected edges. In an undirected edge, the path that we can travel goes both ways. That is to say, the path between the two nodes is bidirectional, meaning that the origin and destination nodes are not fixed.

In mathematics, graphs are a way to formally represent a network, which is basically just a collection of objects that are all interconnected. For example, in mathematical terms, we describe graphs as ordered pairs. Remember high school algebra, when we learned about (x,y) ordered pair coordinates? Similar deal here, with one difference: instead of x and y, the parts of a graph instead are: v, for vertices, and e, for its edges. If our graph has more than one node and more than one edge that ordered pair — (V, E) — is actually made up of two objects: a set of vertices, and a set of edges. The “unordered” part is really important here, because remember, unlike trees, there is no hierarchy of nodes.

Facebook, a massive social network, is a type of graph. Twitter, on the other hand, works very differently from Facebook. I can follow you, but you might not follow me back.

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Vaidehi Joshi, A Gentle Introduction To Graph Theory. In Medium, Retrieved from here

Networks as predictive tools

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Networks play a key role when there is no objective way to determine performance, claims Barabasi in his new book called: “The Formula: The Universal Laws of Success.” Barabasi examined the career paths of scientists and artists both successful and less successful ones by tracing their networks.  While performance is about each individual, their success is about the people they connect to, therefore for Barabasi, success is a collective measure.

However appealing this research may be I resist the predictive character the author implies. I’d love to read the book eventually, but still, this bothers me. Networks are the very representation of complexity and it is inconsistent to consider them as normative tools where quantitative/statistical data can lead to predetermined results. Networks are all about emergence; thus the inability to predict how and when they will evolve. Sure, sometimes it could be that some patterns reappear, but just like the author says, networks are bigger than us or our ability to control them.

I also fail to see the relevance of the term success in this context. It looks so arbitrary and shallow. As much as I would love to see some professionals’ networks and the way they penetrate society, I’d rather the research focused on their ability to change the world for the better. If success is a collective measure, then it should be evaluated in regard to α collective benefit.

 

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Bruno Latour

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  • His early work had done more than that of any other living thinker to unsettle the traditional understanding of how we acquire knowledge of what’s real
  • In a series of controversial books in the 1970s and 1980s, he argued that scientific facts should instead be seen as a product of scientific inquiry. Facts, Latour said, were “networked”;  they stood or fell not on the strength of their inherent veracity but on the strength of the institutions and practices that produced them and made them intelligible. If this network broke down, the facts would go with them.
  • Founder of the new academic discipline of science and technology studies
  • The mid-1990s were the years of the so-called science wars, a series of heated public debates between “realists,” who held that facts were objective and free-standing, and “social constructionists,” like Latour. If scientific knowledge was socially produced — and thus partial, fallible, contingent — how could that not weaken its claims on reality?  Lately, however, these debates have begun to look more like a prelude to the post-truth era in which society as a whole is presently condemned to live.
  • By showing that scientific facts are the product of all-too-human procedures, these critics charge, Latour — whether he intended to or not — gave license to a pernicious anything-goes relativism that cynical conservatives were only too happy to appropriate for their own ends (…) But Latour believes that if the climate skeptics and other junk scientists have made anything clear, it’s that the traditional image of facts was never sustainable to begin with.
  • With the rise of alternative facts, it has become clear that whether or not a statement is believed depends far less on its veracity than on the conditions of its “construction” — that is, who is making it, to whom it’s being addressed and from which institutions it emerges and is made visible. 
  • In Abidjan, Latour began to wonder what it would look like to study scientific knowledge not as a cognitive process but as an embodied cultural practice enabled by instruments, machinery and specific historical conditions.
  • Day-to-day research — what he termed science in the making — appeared not so much as a stepwise progression toward rational truth as a disorderly mass of stray observations, inconclusive results and fledgling explanations (…) During the process of arguing over uncertain data, scientists foregrounded the reality that they were, in some essential sense, always speaking for the facts; and yet, as soon as their propositions were turned into indisputable statements and peer-reviewed papers — what Latour called ready-made science — they claimed that such facts had always spoken for themselves.
  • In the 1980s, Latour helped to develop and advocate for a new approach to sociological research called Actor-Network Theory (…) Latour had seen how an apparently weak and isolated item — a scientific instrument, a scrap of paper, a photograph, a bacterial culture — could acquire enormous power because of the complicated network of other items, known as actors, that were mobilized around it. The more socially “networked” a fact was (the more people and things involved in its production), the more effectively it could refute its less-plausible alternatives.
  • Latour believes that if scientists were transparent about how science really functions — as a process in which people, politics, institutions, peer review and so forth all play their parts — they would be in a stronger position to convince people of their claims
  • Whether they are conscious of this epistemological shift, it is becoming increasingly common to hear scientists characterize their discipline as a “social enterprise” and to point to the strength of their scientific track record, their labors of consensus building and the credible reputations of their researchers.

Excerpts from: Bruno Latour, the Post-Truth Philosopher, Mounts a Defense of Science, By Ava Kofman published in New York Times, full article available here

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