Akram M. Awad ph.D.
Complex networks have been playing an increasingly important role in educational technology. The Internet, the World Wide Web, Facebook, and eBay are examples of some of the myriad types of networks that are a part of everyday life for many people. Three important types of networks are technological, social, and education. First, an example of a technological network can be seen in a model of the World Wide Web where a node could represent a web page and edges could represent hyperlinks. Similarly, in a model of the Internet nodes could represent routers and edges could represent the exchange of traffic. Second, social networks encompass a broad range of networks that occur in the real world where the nodes represent some type of naturally occurring entity and links between pairs of nodes represent some type of social relationship. For example, there are social networks where nodes can represent people and links can represent an interaction, a past communication, a coauthor ship, or a citation. Third, education networks are similar except they use links to model some type of education interaction between nodes. Examples of education networks include models where each node is a person, a multimedia, and links represent learning environments.
Since these networks are often inherently technological and involve social and/or educational interactions between the nodes, it is necessary to use theories and models from three fields —sociology, educations, and technology — to study them. Theories from sociology are needed to understand the relationships that people form and maintain over these networks technology. Models from education are needed to understand the interactions of agents who act in their own self-interest. Technology, in particular, could have a broad impact on studies of social networks as well as education networks. For example, Situated Cognition is a learning theory which supports the idea that learning occurs only when situated within a specific context. It believes that learning takes place in a learning community or community of practice, where the learners take an active role in the learning community. It involves a process of interaction between the learners within the community, the tools available within the specific situation and the physical world. It is within this active participation, this interaction (whether with tools, artifacts or other people), where knowledge is located. Therefore knowing evolves as the learners participate and interact within the new situation. Cognition is linked to the action the learners in the community take, whether it is physical in nature or a reflective process within the learners themselves. Situated Cognition also takes into account the culture of the community at large and “treats culture as a powerful mediator of learning and practices, both for students and teachers.
So what is the role of technology within this emerging theory of learning? As stated above action needs to take place in order for cognition to occur. This action must take place within a community of practice or learning community. This action often involves interaction between tools and or artifacts that are situated in the community. These tools and or artifacts are invaluable parts to the learning system. Without these parts the interactions that they produce, assist or motivate, may not occur. Therefore technology in this learning theory is a piece of the learning environment that helps to bring about cognition. It is quite clear that the learners who are placed into this type of learning environment would be using their “knowledge and skills—by thinking critical, applying knowledge to new situations, analyzing information, comprehending new ideas, communicating, collaborating, solving problems, making decisions. A more specific algorithmic question in this general area is to study the influence people exert on each other to adopt a new behavior and how the spread of the new behavior is affected by the topology of the network. A second broad class of computational questions is motivated by networks where the interactions are well described by a distributed cognition theoretic model where students are afforded more power by participate in a systematically designed learning environment that supports interaction amongst its participants. Distributed cognition describes a construction of knowledge that takes place in a natural environment which is synergistically connected to the cognitive actions taken by the participants in the learning environment. It is through this interaction among people or artifacts such as devices, technologies or media where cognition occurs Artifacts can help to scaffold new capabilities as well as off-load a certain amount of cognitive work thus reducing the cognitive load of the learners and helping to augment their capabilities. At times, by using these artifacts, a little bit of the information might stick with the user, this is known as cognitive residue. It is through interaction with other members and artifacts that progresses learning. Therefore communication among all participants is paramount in importance.
The role of technology within this theory is an invaluable part of the system in which the learners are interacting. This interaction can either help to distribute their knowledge, off-load certain amounts of cognitive work making the cognitive load less and or help to scaffold new capabilities. In this theory technology (artifacts and or tools) can be used to help extend human capabilities. An example of this might be the use of manipulatives in the early development of basic addition skills.The problem might be too complex for the child to solve, but with the use if the manipulative, they can visually represent their thinking and use the tool to help them solve the problem. Another example of this is taken from a case study that was conducted using robotics to produce solving problem skills. In this case study, students were placed into small collaborative groups and were asked to construct a robot, using Lego Mind storm for schools kits, which would perform various tasks. The groups were introduced to a tool known as a flowchart. They used these flowcharts to map the programming instructions they would give the robot to complete the given task. This allowed them to off-load some of the cognitive work to the flowchart and then through its use, they were able to solve harder. The above example shows that cognition takes place because of the cognitive abilities of the learner plus the augmentation of these capabilities by the use of the external technology natural technological question is to analyze the quality of learners' knowledge and skills when placed in to a learning environment. Another example in this general category of questions is quantifying how the topology of the network affects the learning. Understanding the learning environments and the various types of interactions and dynamics over networks is key to increasing educational technologist understanding of how to build and maintain more efficient and effective systems. In Socially-Shared Cognition learners are participants in a community where the cognition is shared between the participants, the artifacts and tools they are using and the social institutions in which the learning occurs . The learners of this community are required to be active participants in order for cognition to occur. My research contributes to this endeavor by focusing on the Community of learning and computational issues involved in the study of social networks and education networks. The word community implies that the people within it are taking an active part in the process of learning. They all support communication amongst the learners and interaction with others, artifacts, and tools in order to assist cognition. In these theories technology plays an integral part, either by helping to assist the learning of new skill by providing scaffolding or by off-loading some cognitive work to make the learning process easier. These technologies may also help to maintain the vital interaction amongst the learners within the community. It is this interactive environment where the students are learning by doing, communicating and receiving feedback which helps to bring about the skills desired by the 21st century.
Past Research My thesis work combined structural models of social networks with models of interaction from education and analyzed this combination from a computational perspective. The field of social network theory1 primarily focuses on measuring the topology of naturally occurring networks then inventing generative models that output networks of similar structure
current Research As a postdoctoral researcher at Yarmouk university, I have continued this line of study using large scale data analysis. My coauthors and I analyzed how learners in e-learning environments influence their behavior. More specifically, we compared the probability that a person will adopt a behavior given that of his/her class mate have already adopted that behavior to the probability that a person will adopt a behavior given that of the people most similar to that person have already adopted the behavior. This work focuses
Future Research My future work will primarily focus on classroom technology, and understanding how the behavior of individuals affects the overall topology of the network. In other words, I will study how the global learning environment emerges from the local dynamics between the nodes. I intend to take a bottom up approach to understanding how these properties arise by arriving at accurate models of how nodes attach to other nodes and how nodes interact with other nodes. These interactions may depend on the context of the networks in question, thus different models may be necessary for different classes of networks. As discussed above, previous work in the social network theory literature has derived stochastic models that output networks that share statistical properties with real-world networks. But almost all of these models fail to consider the interactions occurring between nodes. My approach to filling in this gap in the literature will utilize a diversity of techniques including:
analysis of large data sets, behavioral experiments, theoretical analysis, and simulation. I also intend to use more controlled behavioral experiments to improve our understanding of the specific dynamics between learners arranged in a network. Typically, laboratory controlled to complement these approaches, I intend to use both theoretical analysis and simulations in my study of social and economic models of interaction over networks. A theoretical study of these types of models would allow us to characterize the different types of interactions that occur in networks of different contexts. This would allow for the study of general classes of social networks as opposed to inventing new theories for each individual social network. Furthermore, formally analyzing the local dynamics would lead to a better understanding of how changes to local incentives and local interactions effect the behavior of a population as a whole, and in turn, the general topology of the network. Simulations can be used to exhibit the dynamics of the learners in these networks. For example large scale simulations could be used to incorporate the behavioral models into a large network setting and then check that the macro-level phenomenon observed by the large scale data analyses emerges. This would provide a validation of the more focused behavioral models as well as exhibit how they combine on a large scale. In summation, the intersection of social network theory and economics, when studied from a computer science point of view, provides a timely new research area teeming with unexplored, well-motivated research directions.