Summary: This article outlines a goal directed design research, laying the foundation with arguments for qualitative data for design perspectives, through various stages of design development. There are also the methods and types of research described with strategies on how to conduct them effectively.
Key takeaway:
1) Sometimes people will propose solutions rather than talk about problems. Its the designer's job to read between the lines and extrapolate to the real problem underneath it.
2) SMEs may prefer not to deviate too far from what they are used to. There are always the "perpetual intermediates" for whom designers are trying to design but SMEs lean towards expert controls.
3) Customer and user may mean very different people - this was an interesting point for me since its easy to forget when designing for the user, especially in the case of learning tools for children, its usually the parents who need to be convinced that the product they are buying is a good investment.
4) Checking back in throughout the design process that the stakeholders have a similar image of the product in mind.
Real world example: When I was learning about data mining principles in my undergrad, we explored how unsupervised machine learning went through big datasets to discover knowledge. The findings usually come out unexpected and reveals patterns in human behavior that researchers did not anticipate. The magic was not in the algorithm or the technical details, but in the way data scientists made sense of seemingly random nonsensical data by using real world context and logic. The key here was to go in without preconceived notions about what to find. The reason I relate this experience is because this domain of strategic interview methods, observations and inquiries also amass data from which the designer needs to extrapolate key findings about user-behavior. They also need to be able to identify if their biases obstruct them from seeing patterns among groups or communities they are studying.
Burning question: Since interviews and contextual studies seem to be two of the most effective forms of user study, I cannot help but wonder how honest the people being observed or recorded will be. Everyone has their insecurities and might not want to seem "dumb" or incompetent, hence they might present a different version of themselves to others that will not be a genuine representation. How much of this skewed behavior should be taken into account?
Review 2:
MagneTracks: A Tangible Constructionist Toolkit for
Newtonian Physics
Andrea Miller, Claire Rosenbaum, Paulo Blikstein
Summary: an educational toolkit for exploring Newtonian physics concepts by allowing learners to simulate how various objects would fall through tracks they built, similar to building their own roller-coaster.
Key takeaway: this toolkit frames its learning modules through the concepts of cognitivism and constructionism. While the various technical components of the system, such as using the computer tracking to relate mathematical equations to the dynamics of the object on the tracks, its the creative and fun aspect of the product that made it far more stimulating. The observations with how adults and university students made use of the toolkit to reinforce their existing mental models and challenge new theories made me consider the toolkit to be a success.
Real life example: while reading about magneTracks, I was reminded of how toy train and race tracks allow children to build their own worlds in a similar way. There are also VR simulations of roller-coaster tracks designed by the people who want to experiment and challenge existing designs.
Burning question: While designing tangible interfaces for educational use, can we not take inspiration from the most popular toys that exist already? Furthermore, can we market such toolkits to adults or will they think its juvenile for them? Does the design and wording through marketing play a factor in that?