I stumbled across someone on the Twitter stream from the MSc in Elearning course at the University of Edinburgh (#mscel) who had asked the question:
@Soc_Imagination: The phrase ‘digital humanities’ comes up on my twitter multiple times a day. Is there a social sc equivalent
Excellent question and I immediately jumped to how the tenets of the digital humanities could be applied to the learning sciences (as a subset of the social sciences). I follow a lot of the digital humanities crowd/community and respect what they have to say. More than anything, I admire some of their conviction that the digital realm is culture, history, and society, not some ephemeral anomaly of technology. So, code becomes language and power; textual analysis of these elements of technology is research worthy. Video games are learning platforms encapsulating something about the society in which they arise from (even if it is a predilection towards zombies and Nazis). Using data analysis as a means to a collective textual analysis is not only valuable, it is necessary.
So there is already considerable work being done on the learning benefits of games so no need to tread there as people much smarter than myself are working on that (however, I would love to see mobile GPS games using cities as urban playscapes become more popular and more adaptable to learning communities-another question for another time). I do see some experimentation in open learning formats (MOOCs and #Change11 as macro and micro examples of that), sort of broadening the scope of what is permissable and possible as formats and sources of learning.
What I am talking about is more the power of the empowered, lifelong learner to manage and dictate the direction of their own learning. I generally don’t ascribe to a tools or even pure skills based approach to learning, but this is an instance where I think data analysis tools for individuals would be highly valuable. So, to kickstart/jump on the bandwagon of the discussion of the digital humanities approach to the learning sciences, here are a few things I think we need.
- Robust, predictable data- I will leave this to the experts, but my simplistic view of this (and the sum total of the application of MLIS degree) is that learners need to have a fairly symbiotic relationship with their actions, progress, and failures. This will take the shape of data. Massive amounts of it starting from their first encounters with formalized education (which signifies that first time we started to view learning as a conscious process). We need scores, aptitudes, essays/text (mostly this stuff), projects, etc. Less of the formalized quantitative assessment (which would in this system be reduced to a microcosm of what we are trying to achieve here on a larger scale with ALL the data) and more of the total output. From diaries, to emails, to book reports, essays, artwork, vocabulary, etc. This should ascribe to a machine readable standard (honestly am not concerned with what standard it is, just predictable). The first step is pulling data predictably and on scale (ie, all of it).
- Tools for visualizing robust, predictable data- this is information literacy and learning literacy writ large as far as I am concerned and a valuable project for early learners (say towards the higher end of the K-12 scale). Teach learners to be empowered learners through the analysis of their own learning analytics. Give them tools to visualize writing skills development through visualization. I want to say a dashboard approach here, some central place where they can access and visualize their development in History as opposed to Literature, from Chemistry as opposed to Physics). But not as platform specific. And requiring a greater emphasis on learner empowerment to pull their own data. So, rather than learn programming per se (avoiding the Prensky digital natives nonsense), perhaps learn the tenets of data manipulation via APIs, how to run a SQL query, how to present XML on the website you created. These are all elements of learner empowerment and they are not technologically deterministic (they are all simply statements of logic). You are not teaching programming of data here, but rather the manipulation of data and that is a constant. How do we use the information presented to us? We could take a lot from Digital Humanities here. And by the way, if you can’t game the data driven portions of learning empowerment, well….may the imagination gods protect you from the impending zombie invasion.
- Presentation of disparate streams of data as multimodal learning evidence. Not just to gauge and measure one’s own learning progress, but as a means to create/invent/innovate. To analyze, synthesize, and reflect. The ability to pull this data from the ether and create something new, relevant to a particular epistemological point of inquiry. To truly create representations of learning/knowledge that are multimodal (for purpose), driven by logic (answering the point of inquiry), and creative (to the learner’s needs and vision). Some examples (which should make evident my limited imagination) include interactive mosaics (each pixel tells a story-like pointillism for the virtual age), urban cultural heritage sites embedded with geopositioned ‘notes’, later pulled to form ebooks/essays, collaborative novels (classmates or virtual communities) written in 140 character bursts, embedded with multimedia.
- On a slightly related note, mapping your virtual (emotion and intellectual) geography sounds like a wonderful learning application of this type of approach. Understanding that online learning is positioned (however ephemeral) and that it does involve an orientation (towards or away something). Even rhizomic learning, however amoebic, still implies some structure. I thought of this application partially due to a research project I am working on and partially due to the flood of screenshots and online images I have created that Flickr routinely taunts me with by declaring “Add these photos to your map”. Now that is a project, Flickr, a map of my virtual footprint. How is that for empowering learners?
Nothing original here. We have flooded these new territories with what is possible some time ago. What we need now are coupling strategies to overcome/mitigate current assessment paradigms. Don’t want to lose focus on writing skills? By all means, use these approaches as supplements to current pedagogies. But don’t ignore them.
Understanding and manipulating data for analysis and understanding is what smart people do. It makes sense to them (notice I refer to this group as ‘them’ not ‘us’) and reads like a language. Like any second language, it takes practice. But it isn’t alchemy. It is a bit of an art, but there is a lot of pragmatic science here. Understand the elements involved (data), harness these (with tools), model behavior (here is a possible application), and reflect early and often. Empower your learners to be empowered learners.
These are just first thoughts on a complex, exciting subject so expect more, but many thanks for the question (further evidence of my love of Twitter and affinity circles for stimulating thought) and Digital Humanities for providing some direction.
