Design Researchers are Social Science Hackers
Social Scientists seek to understand human behavior, the difficulty of our work is in attributing the observed behavior to nature or nurture. (Perhaps an aside: I believe that it is in our nature to nurture. Therefore it's a false dichotomy if taken too literally particularly in the social sciences.) To me the difference resides in our multiple brain systems, the autonomic (includes physiological) and the cognitive and the motor, of which govern human behavior sometimes at odds with each other.
I distinguish social scientists from hard scientists who seek to understand physical phenomena. It isn't that such physical phenomena doesn't also exist in the human body and also affects human behavior, it's just that social scientists are working at a large grain size, behavior is a larger unit of analysis than phenomena. For example, ergonomics and physics or psychology and neuroscience. Social scientists have to not only understand the relevant physical phenomena at work (if known and understood by physical scientists), but they must also understand the social and psychological factors as well. Social scientists must synthesize and analyze in order to understand human behavior. Too much analysis and practitioners get little useful actionable knowledge. Too much synthesize and we rely on evidence that is either speculative or anecdotal, a solution that falls apart quickly or is too fragile to change.
Social scientists are a broad class of thinkers that worry about autonomic and cognitive systems. I would put designers in this class. Designers, whether they fall on the artistic side or the industrial side, both try to hack the autonomic and cognitive systems. What distinguishes the artists (in my opinion) is that they are not interested in activating specific human behaviors (more on the side of emotions, thoughts, or perceptions) which is why they fall on the outside of social science (for me anyway). Designers that are artistic, take those hacks and generate human behavior. The best example is marketing and advertising for purchasing behavior (only exists in a society of abundance). Designers on the industrial side can be artistic (best example is Apple products like iPod and iPhone) but they can also be based on function. Designers that worry about function can hack into both the autonomic and the cognitive and the motor systems. iPhone incorporation of multi-touch gesture activates a pre-verbal and less cognitive load (we call this intuitive but I think this term obscures what's going on.). Designers called information architects and web designers worry more about the cognitive, motor, and autonomic (in that order of priority). The mental model of the web user that they bring to the browsing experience can be very different from the assumed model of the designer. This can lead to negative human behaviors (at least as seen from the web site owner), which is moving onto some other website.
Educational researchers also fall into the social scientist category, except we spend most of our deal of time thinking and observing and designing the nurture system (in my framework the cognitive system). We don't pay a lot of attention to the autonomic (which includes physiology in my framework) or motor systems (physical education is probably an exception). For example, we know that sleep, exercise and nutrition play roles in cognition and learning. However, we don't account for such factors in our analyses. Neuroscience particularly the examination of oxygenated blood flow in the brain, along with other methodologies like electrical impulses, may shift our methodologies in educational research to think about the full science of education and learning.
Design research in education works because we start with the designer's process of hacking A-C-M systems in order to generate the positive human behaviors we seek and also in the process revealing the many hacks that don't work. In synchronous mode we are actively conscious of the systems we are hacking and the behaviors we seek. In this flow-like hypothesis generating and testing mode, we are the types of social scientists like inventors or engineers maybe even artists. In asynchronous mode, we are examining our successes and failures retrospectively particularly to deconstruct cause and effect, building a theory.
Since we don't operate on the root physical phenomena like hard scientists, our theories are local, context sensitive and humble (or fragile). Our theories don't scale very well and don't operate throughout the universe. Our theories do account for nurture quite a bit and kind of holds nature as a constant. Our theories don't scale very well, partly because we don't build the very large solution first and then examine it or examine very large educational systems and attribute these back to A-C-M systems. In this last point, we could learn a lot from the field of economics which does a good job of linking large scale markets and organizations function in relation to self-interest and design features they call incentives. In recent years, they have looked closer at A-C-M like the rise of neuroeconomics and experimental economics that seek to understand how the brain and the thinking behave under certain economic decisions. But this close up work is based on a century of data analysis of market data.
As the educational system begins to aggregate data on a very large scale, we as educational researchers have to start analyzing this data with the individual in mind, we can't just keep looking at broad statistics that reveal little in the way of insight particularly for targeted decision making and design pathways.
I distinguish social scientists from hard scientists who seek to understand physical phenomena. It isn't that such physical phenomena doesn't also exist in the human body and also affects human behavior, it's just that social scientists are working at a large grain size, behavior is a larger unit of analysis than phenomena. For example, ergonomics and physics or psychology and neuroscience. Social scientists have to not only understand the relevant physical phenomena at work (if known and understood by physical scientists), but they must also understand the social and psychological factors as well. Social scientists must synthesize and analyze in order to understand human behavior. Too much analysis and practitioners get little useful actionable knowledge. Too much synthesize and we rely on evidence that is either speculative or anecdotal, a solution that falls apart quickly or is too fragile to change.
Social scientists are a broad class of thinkers that worry about autonomic and cognitive systems. I would put designers in this class. Designers, whether they fall on the artistic side or the industrial side, both try to hack the autonomic and cognitive systems. What distinguishes the artists (in my opinion) is that they are not interested in activating specific human behaviors (more on the side of emotions, thoughts, or perceptions) which is why they fall on the outside of social science (for me anyway). Designers that are artistic, take those hacks and generate human behavior. The best example is marketing and advertising for purchasing behavior (only exists in a society of abundance). Designers on the industrial side can be artistic (best example is Apple products like iPod and iPhone) but they can also be based on function. Designers that worry about function can hack into both the autonomic and the cognitive and the motor systems. iPhone incorporation of multi-touch gesture activates a pre-verbal and less cognitive load (we call this intuitive but I think this term obscures what's going on.). Designers called information architects and web designers worry more about the cognitive, motor, and autonomic (in that order of priority). The mental model of the web user that they bring to the browsing experience can be very different from the assumed model of the designer. This can lead to negative human behaviors (at least as seen from the web site owner), which is moving onto some other website.
Educational researchers also fall into the social scientist category, except we spend most of our deal of time thinking and observing and designing the nurture system (in my framework the cognitive system). We don't pay a lot of attention to the autonomic (which includes physiology in my framework) or motor systems (physical education is probably an exception). For example, we know that sleep, exercise and nutrition play roles in cognition and learning. However, we don't account for such factors in our analyses. Neuroscience particularly the examination of oxygenated blood flow in the brain, along with other methodologies like electrical impulses, may shift our methodologies in educational research to think about the full science of education and learning.
Design research in education works because we start with the designer's process of hacking A-C-M systems in order to generate the positive human behaviors we seek and also in the process revealing the many hacks that don't work. In synchronous mode we are actively conscious of the systems we are hacking and the behaviors we seek. In this flow-like hypothesis generating and testing mode, we are the types of social scientists like inventors or engineers maybe even artists. In asynchronous mode, we are examining our successes and failures retrospectively particularly to deconstruct cause and effect, building a theory.
Since we don't operate on the root physical phenomena like hard scientists, our theories are local, context sensitive and humble (or fragile). Our theories don't scale very well and don't operate throughout the universe. Our theories do account for nurture quite a bit and kind of holds nature as a constant. Our theories don't scale very well, partly because we don't build the very large solution first and then examine it or examine very large educational systems and attribute these back to A-C-M systems. In this last point, we could learn a lot from the field of economics which does a good job of linking large scale markets and organizations function in relation to self-interest and design features they call incentives. In recent years, they have looked closer at A-C-M like the rise of neuroeconomics and experimental economics that seek to understand how the brain and the thinking behave under certain economic decisions. But this close up work is based on a century of data analysis of market data.
As the educational system begins to aggregate data on a very large scale, we as educational researchers have to start analyzing this data with the individual in mind, we can't just keep looking at broad statistics that reveal little in the way of insight particularly for targeted decision making and design pathways.
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