Posts tagged improvement science
Thinking in Systems I

A system is a set of elements interconnected in such a way that it produces its own pattern of behavior over time. Systems are coherently organized in a way that achieves something. The system may be impacted by outside forces, but its response to these forces is characteristic of the system itself, and that response is seldom simple. Systems thinking then is a way of thinking that focuses on recognizing the interconnections between the parts of a system and synthesizing them into a unified view of the whole.

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Goal Setting is Often an Act of Desperation, Part I

At a recent district leadership team meeting, I put the following quote up on a slide: “Goal setting is often an act of desperation.”1 We are in the midst of updating our strategic plan at United Schools Network, so the purpose of the quote was to start a discussion on healthy goal-setting and to provide a framework for any goal-setting the team would do as a part of this process. I think the typical reaction to the quote is something like the following: “But I thought goal-setting was something highly effective people and organizations do?” I would argue however, that this is rarely the case, be it in organizations or accountability systems, and only can be true if a number of conditions are met during the process.

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The PDSA in Action

Now that I’ve outlined the basic idea of the PDSA cycle, it will be helpful to turn to a real PDSA that I used in my work at United Schools Network. This in fact was the first PDSA I ever designed, so it by no means is being held up as an exemplar. However, I think it is useful as an introductory point to the concept because this particular example is so simple. I’m also happy to report that for a first attempt, this PDSA cycle was fairly successful.

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Data Ponds & Streams

Last month, I outlined why data has no meaning apart from their context. The discussion centered on some key ideas for presenting data in context as well as a logical definition of improvement. I also introduced an example of how data is often misinterpreted in the education sector. In this post, I’ll begin to lay the foundation for understanding variation in quality improvement work; this will be a precursor to comprehending why so much of the data analysis that is done in organizations is akin to writing fiction.

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Numerical Naiveté

Dr. Russell Ackoff, the eminent systems thinker said, “Managers do not solve problems, they manage messes.” My take would be more optimistic than Ackoff’s assuming managers knew something about variation. This is in fact why I’ve spent so much time over the last year studying this idea as it fits into W. Edwards Deming’s theoretical framework, the System of Profound Knowledge.

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The Influence of W. Edwards Deming (1900-1993)

In order to achieve equitable outcomes for all students, schools must undergo a transformation on an order of magnitude seldom seen in the history of organizations. One such example from history is that of Japanese industry after World War II. This transformation was assisted by a team of American engineers, scientists, and statisticians, but arguably the most instrumental was W. Edwards Deming.

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Getting Better: Filtering Out the Noise

As schools consider how to restart in the fall, some are galvanizing their community to consider this an opportunity for reinvention, a way to rethink how we educate our young, while others simply want to get students back in the building and return as quickly and safely as possible to the normal that existed prior to the pandemic. Regardless of a school’s position on the spectrum from restart to reinvention, a more sophisticated manner in which to analyze student engagement and track efforts to improve engagement will be necessary.

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How do you measure remote learning engagement?

For the last two months, my colleague Ben Pacht and I have been writing about the work we are doing at United Schools Network (USN) in response to the COVID-19 pandemic and the subsequent school closure order in Ohio (see herehere, and here). In this fourth post in the series, we’ll summarize some key points we’ve made before, plus offer a few ideas specific to measuring and analyzing remote learning engagement.

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Getting Better: What is student engagement, anyway?

As we are all aware, the COVID-19 pandemic has fundamentally altered how our society has functioned over the past few months. The education sector is no exception. As science teachers can attest, one of the unfortunate, enduring realities for life on this planet is that a minuscule virus, just microns in diameter, can leave an outsized impact on life as we know it. Yet a second enduring reality is that the form of life known as Homo sapiens is awfully resilient, and educators across the country are thinking creatively to ensure continuity of education for our students in a remote setting.

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Successful remote learning requires a whole new system

The transition to remote learning due to the coronavirus pandemic has significantly shifted how United Schools Network (USN) plans and delivers educational experiences to its students. We've outlined our remote learning system—and it is helpful to think of it as a system—in our Education Plan. As soon as that document was created, and the new system was outlined, we immediately started thinking about how to improve it.

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Getting Better: Improvement Science during COVID-19

As everybody in the education sector is painfully aware, the COVID-19 outbreak has drastically changed the way we think about and execute our jobs for the foreseeable future. Normal life has been put on hold while we determine how to navigate the uncertain months ahead. Questions materialize much faster than answers, and it is difficult to keep up with the near constant updates coming from federal, state, and local officials. I am hard-pressed to identify a moment in my life that has been as impactful, rapidly-evolving, and confusing. I don’t believe it’s hyperbolic to say that this is a truly unprecedented time for those of us in education. 

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Improvement Science Tool: Fishbone Diagram

Why are we getting the outcomes that we currently do? In my last post, I implored organizational leaders to slow down and take the time to deeply understand this question before moving ahead with solutions.

The only way to understand current outcomes is to step back and see the system - the people, the policies, the attitudes, and the physical environment - in which a problem resides.

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