Last year, Tammy Shreiner and I published an article in the British Journal of Educational Technology, "The information won't just sink in: Helping teachers provide technology-assisted data literacy instruction in social studies." (I haven't been able to blog much the last year while starting up PCAS, so please excuse my tardiness in sharing this story.) The journal version of the paper is here, and our final submitted version (not paywalled) is available here.
Tammy and I used this paper to describe what happened (mostly during the pandemic) as we continued to provide support to in-service/practicing social studies teachers to adopt data literacy instruction in their classes. Since this was a journal on educational technology, we mostly focused on two technologies:
- The OER Tammy created to support data literacy in social studies education — see link here.
- DV4L, the Data Visualization for Learning tool that we created explicitly for social studies teachers — see link here.
When we started collaborating together, we looked for a theoretical model could inform our work. The end goal was easy to describe: we wanted social studies teachers to teach data literacy. But it's hard to measure progress towards that big, high-levelgoal. Teachers are teaching data literacy, or they're not. How do you know if you're getting closer to the goal? We structured our work and our evaluation around the Technology Acceptance Model (TAM). TAM suggests that adoption of a new technology boils down to two questions: (1) is the technology actually useful in solving a problem that users care about, and (2) is the technology usable by the users? Those were things that we could measure progress towards.
During the pandemic, we ran several on-line professional learning opportunities — a workshop where practicing teachers could try out the OER with some guidance (e.g., "Make sure you see this" and "Why don't you try that?"), and kick the tires on a bunch of tools including DV4L. We gathered lots of data on those teachers, and Tammy did the hard work of analyzing those data over time. We made progress on TAM goals — our tools got more usable and more useful.
But we still got very little adoption. TAM didn't work for us. Adoption didn't increase as usability and usefulness increased.
Why not? That's a really big question, and we barely touch on it in this paper. It's now a couple of years since we wrote the BJET article, and I could now tick off a dozen bullet points of reasons why teachers do not adopt, despite a technology being both useful and usable. I'm not going to list them here, because there are other publications in the pipeline. Bahare Naimipour, the EER PhD student working on our project, is finishing a case study of some teachers who did adopt and how their beliefs about data literacy changed.
I can give you a big meta-reason which probably isn't a surprise to most education researchers but might be a surprise to many computer scientists: It's not all about (or even mostly about) the technology. I led the group that worked on DV4L, and I've been directing students who have been helping Tammy make the OER more usable and useful (including build new tools that we haven't yet released). TAM matters, but the characteristics of the individual teachers and the context of the teacher's classroom are critical factors that technology is unlikely to overcome.
This is work funded in part by our National Science Foundation grant, #DRL2030919
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