The emergence of SMART methods -- non-expert platforms for social science and health research

I would like to thank Mark Elliot, Claire Spencer and the MethodsCon team and the National Centre for Research Methods for the opportunity to run my session on  The emergence of SMART methods -- non-expert platforms for social science and health research.

I presented on a new avenue of methods development that my colleague Corey Schimpf (Department of Engineering Education, University at Buffalo) first identified, which he and I are calling smart and approachable methods or AM-Smart for short.

 

PRESENTATION ABSTRACT

Advances in the integration of smart technology with interdisciplinary methods has created a new genre, approachable modeling and smart methods – AM-Smart for short. AM-Smart platforms address a major challenge for applied and public sector analysts, educators and those trained in traditional methods: accessing the latest advances in interdisciplinary (particularly computational) methods. AM-Smart platforms do so through nine design features. They are (1) bespoke tools that (2) involve a single or small network of interrelated (mostly computational) methods. They also (3) embed distributed expertise, (4) scaffold methods use, (5) provide rapid and formative feedback, (6) leverage visual reasoning, (7) enable productive failure, and (8) promote user-driven inquiry; all while (9) counting as rigorous and reliable tools. Examples include R-shiny programmes, computational modeling and statistical apps, public-sector data management platforms, data visualisation tools, and smart phone apps. Critical reflection on AM-Smart platforms, however, reveals considerable unevenness in these design features, which hamper their effectiveness. A rigorous research agenda is vital. After situating the AM-Smart genre in its historical context and introducing a short list of platforms, we review the above nine features, including a use-case on how AM-Smart platforms ideally work. We end with a research agenda for advancing the AM-Smart genre.

•PURPOSE OF SESSION

This session will introduce this newly emerging field, provide some examples, and then explore with attendees how to critically engage and develop new smart methods for social science and health research.

•The goal is to

•Examine the utility of this field

•Identify key concerns

•Sketch out ideas for possible AM-Smart methods

•Explore possible collaborations or venues for future research

 

 LINKAGES

CLICK HERE for the PDF of the Power Point

CLICK HERE for the paper ON AM-Smart Methods (Open Access)

CLICK HERE to explore COMPLEX-IT and its software, tutorials, etc.

CLICK HERE for a published article on COMPLEX-IT

CLICK HERE for Big Data Mining and Complexity

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Much thanks to those who participated in the event. 

Here are the questions we came up with as a function of the workshop discussions:

•How do AM-Smart methods impact learning due to the speed at which we they work?

•The value or ramifications of datasets that have not been understood?

•The value of pausing and slow science.

•When is it good to have slow versus fast science?

•In terms of scaffolding how do we make sure of not cutting corners.

•How do we decide what to use based on different context and users and different levels of expertise.

•The importance of co-production.

•Throwing the baby out with the bathwater by critiquing conventional methods without being as critical of AM-Smart method. Are they actually learning what we want them to learn?

•Where is the learning taking place or not taking place?

•Are we smart enough for AM-Smart methods?

•The value of gaming environments for AM-Smart environments?

•This tends to favour fast processing.

One of the outcomes of the workshop was the value of figuring out how to add qualitative information to the clustering or classification methods regularly used in many AM-Smart methods.

Another was how to integrate, via smart design, qualitative and quantitative information to evidence both aspects of corroboration of insights as well as gaps in understanding.