CCK08. We live in a complex and changing world. A world in which knowledge is rapidly growing with multiple tools augments our ability to interact with each other. The learning process is changing and we need new models and metaphors to explain how knowledge is acquired. Nowadays digital technology is an important part of everyday life for almost all students and some teachers. As Prensky says, “our students are all «native speakers» of the digital language of computers, videogames and the Internet” (Prensky, 2001). Does this affect the way in what students learn?

According to Prensky a typical high school student in USA have watched 20,000 hours of television; have spent 10,000 hours playing videogames and have been reading only 5,000 hours. The new generation is digital native and it means there are a new attitude to technology and some new process that affects learning. Some of them are:

• Friends distributed across multiple networks as instant messengers, social networks (MySpace, Facebook, etc.), email addresses and cell phone contacts.
• Online reading and writing. The screen is a natural place to access and show information that could be text, pictures, videos, chat session, forum discussions or a Wikipedia article. As opposed to previous generation with strong text-based learning, digital natives make an intensive media mix.
• Emphasis on practice. Digital natives usually don’t read manuals or instructions and trust that devices can teach how to manipulate them itself. Trial and error is often a quickly way to get what you want quickly.

Connectivism is a learning theory that fits perfectly to this context and explains how learning happens in a rapid changing core of global knowledge. Connectivism can be defined as “the application of network principles to define both knowledge and the process of learning. Knowledge is defined as a particular pattern of relationships and learning is defined as the creation of new connections and patterns as well as the ability to maneuver around existing networks/patterns” (Siemens, 2008).

To understand the network metaphor we need to know a bit about networks theory. For Wellman a network consist of one or more nodes connected by on or more ties. Nodes are a unit that possibility is connected (for example, individuals, households, workgroups, organizations, states, etc) and ties are one or more specific type of connection. Networks form distinct and analyzable patterns with emergent properties. Following this concepts it is possibly to represent a college student learning network in a graphic like this:

We can explain how learning occurs following nodes and ties and understanding that the key of learn is the process of make connections. The more nodes an ties, the more strong is the knowledge. This is a good way to represent how we learn across diverse sources recognizing and interpreting patterns within a network that is changing continuously. However, there are some questions to connectivism that I have no answer for the moment.

• Connectivism is clearly related to network theory that is relevant to see connections and relationship between components of a process. Nevertheless, is knowledge only the sum of nodes and ties? If the answer must yes… are these entities relevant to explain by itself the whole learning process.
• Think in this example: All computer programs are at least a vast group of zeros and ones. It’s a fact. But can not understand and explain how it works analyzing only these numbers. The binary code represents process, relationship and interaction. In other words, software is more than the binary code.
• What is the role of external factors? Culture, environment and context influence the learning process and favor some relationship instead of others. Networks don’t exist in an empty space and there are some other elements that determine and model knowledge acquisition.

Nonetheless, these questions to come from an initial impressions after introducing in connectivism theory. Perhaps, I loosing some vital node or ties to understand it in a more deeply way.

References

• Prensky (2001) Digital Natives, Digital Inmigrants. Retrieved September 28, 2008 from Prensky Web Site: http://www.marcprensky.com/writing/Prensky%20-%20Digital%20Natives,%20Digital%20Immigrants%20-%20Part1.pdf
• Wellman, B. (n/d) Networks for Newbies. A Non-Technical Introduction to Social Network Analysis. Retrieved September 28, 2008 from Wellman’s Page at University of Toronto Web Site: http://www.chass.utoronto.ca/~wellman/publications/networksfornewbies/networks4newbies.ppt
• Siemens, G. (2008) What is the unique idea in Connectivism? Retrieved September 28, 2008 from Connectivism Blog Web Site: http://connectivism.ca/blog/2008/08/what_is_the_unique_idea_in_con.html

This third week the course of Conectivismo -with George Siemens and Stephen Downes as facilitators- is oriented to reflect on the networks. The theory of networks is of which they are begun mentioning a pair of things that seem obvious but that quickly go more and more comlex. Like the mathematical one.

In a sentence, the theory of networks consists…

“To discover how A, who is touch with B and C, is affected by the relationship between B and C”

(John Barnes)

Barry Wellman’s slides [ppt] begins saying simple things like these. This theory assumes that the reality can be represented like a set of connected units. A network consists of one or more nodes and these nodes can be practically any thing: people, organizations, groups, nations, etc.

The connections between nodes are “ties” and the theory of networks studies the patterns of relationship, the emergent properties that indicate microsociological studies of Homans or Simmel. Here the things goes more complex and we have to follow one specific methodology with multiple levels of analysis. Differences among actors are traced to the constraints and opportunities that arise from how they are embedded in networks.

At Wednesday’s session Valdis Krebs gave us some interesting examples of things that can be analyzed through Social Networks Analysis (SNA) methodology. SNA is a mathematical and visual analysis of relationships, flows and influence between people, groups, organizations, computers or other information/knowledge processing entities.

Key Opinion Leaders

In this example Krebs SNA to make he classical study of Lazersfed [doc] on key opinion leaders (in red). As it is seen, the structure is much more complex that the scheme of two-step flow usually used to explain it.

Porn Star HIV Network

This other example shows to the propagation of HIV at porn industry of United States in 2004. At the centre is Darren James who was the first infected by HIV. The red points represent stars infected, yellow points those who had pending results and green points are the actors without HIV.

Can you find the terrorists?

Here Krebs shows the connections how terrorists (on the right) has much smaller density bond between its members, although it centers strongly is connected in two cases.

The social networks theory is a mathematical model to analyze the interrelations and is specially useful to diagnose communication processes at organizations. In the educative field, connectivism is clearly the extrapolation of network metaphor to the scope of knowledge to show how people construct it.

At first sight, the main limitation of the model seems to be that bonds are defined in binary terms: there is relationship (1) or there isn’t (0). Nevertheless, the complexity of human interaction (and knowledge acquisition) has nuances and levels which cannot be discriminated as yes/no. Perhaps in these cases we must return to analyze the details to establish a new network that’s with new and more detailed nodes and bonds.

Esta tercera semana el curso de Conectivismo a cargo de George Siemens y Stephen Downes está orientado a reflexionar sobre las redes. La teoría de redes es de esas que se comienzan mencionando un par de cosas que parecen obvias pero que rápidamente se van complejizando. Como la matemática.

“En síntesis, la teoría de redes consiste en como A, que es está conectado con B y C, es afectado por la relación entre B y C”

(John Barnes)

La presentación de Barry Wellman [ppt] comienza diciendo cosas sencillas como estas. Esta teoría asume asume que la realidad puede ser vista como un conjunto de unidades conectadas entre sí. Una red consta de uno o más nodos y estos nodos pueden ser prácticamente cualquier cosa: personas, organizaciones, grupos, naciones, etc.

A las conexiones entre nodos se les llaman vínculos y la teoría de redes se encarga de estudiar los patrones de relaciones o vínculos, las propiedades emergentes que señalan los estudios microsociológicos de Homans o Simmel. Aquí es donde la cuestión comienza a ser más compleja ya que hay seguir una metodología específica con múltiples niveles de análisis donde se analizar las diferencias entre actores a partir de las constricciones y oportunidades que pueden surgir por cómo están embebidos en sus redes.

En la sesión del miércoles Valdis Krebs dio algunos ejemplos interesantes de cosas que se pueden analizar a través de la metodología del Análisis de Redes Sociales (SNA, Social Network Analysis), que es un análisis matemático de las relaciones, los flujos y la influencia entre personas, grupos, organizaciones, computadoras y cualquier entidad capaz de procesar información o conocimiento.

Key Opinion Lider

En este ejemplo utiliza el SNA para reeditar el clásico estudio de Lazersfed [doc] sobre los líderes de opinión (marcados en rojo). Como se ve, la estructura es mucho más compleja que el esquema de los dos escalones (two-step flow) que suele utilizarse explicarla.

Porn Star HIV Networks

Este otro ejemplo -menos académico- muestra la propagación del HIV en la industria del cine porno de Estados Unidos en el año 2004. En el centro está  Darren James, que fue el primero en contraer la enfermedad y los demás puntos rojos representan a los otros infectados, los amarillos a quienes tenían por ese momento la prueba pendiente y los verdes los actores con análisis de HIV negativo.

Can you find the terrorists?

En este tercer ejemplo, Krebs muestra las conexiones como la red de una organización terrorista (a la derecha) tiene una densidad de vínculo mucho menor entre sus miembros, aunque el centra está fuertemente conectado.

La teoría social de redes es un modelo matemático que ayuda al análisis de las interrelaciones y es especialmente útil para diagnosticar los procesos de comunicación en las organizaciones. En el campo educativo, el conectivismo es claramente la extrapolación de la metáfora de las redes al ámbito del conocimiento para mostrar cómo las personas construyen su conocimiento.

A primera vista, la principal limitación del modelo parece ser que solo concibe los vínculos en términos binarios: hay o no hay conexión entre nodos. Sin embargo, en la complejidad de las relaciones humanas (y en la adquisición del conocimiento) hay matices y niveles de que no pueden discriminarse en un sencillo sí/no. Quizás, en estos casos tengamos que volver a analizar el detalle para establecer una nueva red cuyos nodos sean los diferentes componentes de un vínculo.