… the resources are a necessary starting point, but they are not an end point. Particularly if your goal is to “ensure inclusive and equitable quality education and promote lifelong opportunities for all”, then it is the learner support that goes around the content that is vital.
And on this, the recommendations are largely silent. There is a recommendation to develop “supportive policy” but this is focused on supporting the creation of OER, not the learners. Similarly the “Sustainability models for OER” are aimed at finding ways to fund the creation of OER. I think we need to move beyond this now. Obviously having the resources is important, and I’d rather have OER than nothing, but unless we start recognising, and promoting, the need for models that will support learners, then there is a danger of perpetuating a false narrative around OER – that content is all you need to ensure equity. It’s not, because people are starting from different places.
I’ve always thought that too much focus has always been on “the resources”, but I’ve never really got to grips with how the resources are supposed to be (re)used, either by educators or learners.
For educators, reuse can often come in the form of “assign that thing someone else wrote, and wrap it with your own teaching context”, or “pinch that idea and modify it for your own use”. So if I see a good diagram, I might “reuse” it by inserting it in my own materials or I might redraw it with some tweaks.
Assessment reuse (“open assessment resources”?) can be handy too: a question form that someone else has worked up that I can make use of. In some cases, the question may include either exact, or ‘not drawn to scale’ media assets. But in many cases, I would still need to do work to generalise or customise the answer, and work out my own correct answer or marking guide.
(See for example Generative Assessment Creation.)
If an asset is not being reused directly, but the idea is, with some customisation, or change in parameter values, then creating the new asset may require significant effort, as well as access to, and skills in using, particular drawing packages. In some cases the liquid paper method works: Tipp-Ex out the original numbers, write in your own, photocopy to produce the new asset. Digital cut or crop alternatives are available.
Another post in my feeds today – Enterprise Dashboards with R Markdown, via Rbloggers – described a rationale for using reproducible methods to generate dashboards:
We have been living with spreadsheets for so long that most office workers think it is obvious that spreadsheets generated with programs like Microsoft Excel make it easy to understand data and communicate insights. Everyone in a business, from the newest intern to the CEO, has had some experience with spreadsheets. But using Excel as the de facto analytic standard is problematic. Relying exclusively on Excel produces environments where it is almost impossible to organize and maintain efficient operational workflows. …
[A particular] Excel dashboard attempts to function as a real application by allowing its users to filter and visualize key metrics about customers. It took dozens of hours to build. The intent was to hand off maintenance to someone else, but the dashboard was so complex that the author was forced to maintain it. Every week, the author copied data from an ETL tool and pasted it into the workbook, spot checked a few cells, and then emailed the entire workbook to a distribution list. Everyone on the distribution list got a new copy in their inbox every week. There were no security controls around data management or data access. Anyone with the report could modify its contents. The update process often broke the brittle cell dependencies; or worse, discrepancies between weeks passed unnoticed. It was almost impossible to guarantee the integrity of each weekly report.
Why coding is important
Excel workbooks are hard to maintain, collaborate on, and debug because they are not reproducible. The content of every cell and the design of every chart is set without ever recording the author’s actions. There is no simple way to recreate an Excel workbook because there is no recipe (i.e., set of instructions) that describes how it was made. Because Excel workbooks lack a recipe, they tend to be hard to maintain and prone to errors. It takes care, vigilance, and subject-matter knowledge to maintain a complex Excel workbook. Even then, human errors abound and changes require a lot of effort.
A better approach is to write code. … When you create a recipe with code, anyone can reproduce your work (including your future self). The act of coding implicitly invites others to collaborate with you. You can systematically validate and debug your code. All of these things lead to better code over time.
Many of the issues described there are to do with maintenance. Many of the issues associated with “reusing OERs with modification” are akin to maintenance issues. (When an educator updates their materials year on year – maintenance – they are reusing materials they have permission to use, with modification.)
In both the maintenance and the wider reuse-with-modification activity, it can really help if you have access to the recipe that created the thing you are trying to maintain. Year on year reuse is not buying 10 exact clone pizzas in the first year, freezing 9, taking one out each year, picking off the original topping and adding this year’s topping du jour for the current course presentation. It’s about saving and/or sharing the recipe and generating a fresh version of the asset each year, perhaps with some modification to the recipe.
In other words, the asset created under the reuse-with-modification licence is not subtractive/additive to the original asset, it is (re)generative from the original recipe.
This is where things like Jupyter notebooks or Rmd documents come in – they can be used to deliver educational resources that are in principle reusable-with-modification because they are generative of the final asset: the asset is produced from a modifiable recipe contained within the asset.
I’ve started trying to put together some simple examples of topic based recipes as Jupyter notebooks that can run on Microsoft’s (free) Azure Notebooks service: Getting Started With OER notebooks.
To run the notebooks, you need to create a Microsoft Live account, log in to notebooks.azure.com, and then clone the above linked repository.
OU staff and ALs should be able to log in using their email@example.com credentials. If you work for a company that uses Office 365 / Live online applications, ask them to enable notebooks too…
Once you have cloned the notebooks, you should be able to run them…
PS if you have examples of other things I should include in the demos, please let me know via the comments. I’m also happy to do demos, etc.