How computing education researchers and learning scientists might better collaborate

August 12, 2018 at 11:00 pm 1 comment

Lauren Margulieux has started a blog which is pretty terrific.  I wrote about Lauren’s doctoral studies here, and I last blogged about her work (a paper comparing learning in programming, statistics, and chemistry) here.

In her blog, Lauren is explaining in lay terms papers from learning sciences, educational psychology, and educational technology.  She’s an interdisciplinary researcher, and she’s blogging to help others connect across disciplines.

Her most recent blog post is about an issue I’ve been thinking about a lot lately. I wrote a blog post in the summer about the challenge of bridging the modes of science and truth-seeking in (computing) education vs. computer science. Lauren summarizes a paper by Peffer and Renken about concrete strategies to be used between discipline-based education researchers (like math education researchers, science education researchers, or computing education researchers) and learning scientists. Quoting part of it below:

Challenges in Interdisciplinary Research: Collaboration within a field can be difficult as people attempt to reconcile different ideas towards one goal. Collaboration between fields, each with its own traditions in theory and methodology, can seem like a minefield. Below are some common challenges that DBERers and learning scientists face.

  1. Differences in hard and soft sciences – researchers in the hard sciences can often feel frustrated by the lack of predictability in human-subjects research, and researchers in social sciences can become frustrated when those in the hard sciences have unrealistic expectations or view research in the soft sciences as non-scientific.

  2. Differences in theories and frameworks – What constitutes a theory or framework can be different in different domains, confusing what is often a fundamental building block of research.

  3. Differences in research methodologies – those unfamiliar with human-subjects research can find its methodologies complex, varied, and full of uncertainty, and those who have endured countless hours of training in these methodologies can find it difficult to describe or justify methodological decisions in a concise way.

See more at https://laurenmarg.com/2018/07/29/peffer-renken-2016-dber-and-learning-sciences-collaboration-strategies/

Entry filed under: Uncategorized. Tags: , , .

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1 Comment Add your own

  • 1. Jim Williams  |  August 13, 2018 at 10:43 am

    I just read David Williamson Shaffer’s Quantitative Ethnography, Cathcart Press, 2017. It describes how to systematically integrate quantitative and qualitative methods with lots of educational research examples. Coming from primarily a CS background I found this makes some statistical techniques and qualitative methods more accessible. The integration of both statistics and qualitative research will likely contribute to advances in CS educational research.

    Reply

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