The second approach to increased engagement with artificial news is to increase the amount that we distinguish between reporting that requires interpretation of raw data to reporting that clearly has added an overtly editorial bias to the reporting. I’ve put this prong off for a few days because I need your help to think through how to do this well.
For me – this feels like falling on the sword. Danah Boyd’s SXSW Edu Keynote – I’d love to hear what you think – I’m going to do the worst practices described and potentially encourage that kind of ‘gaslighting’ behavior in students thinking about articles. I want them to question, in a class, where the data comes from, what data requires interpretation – my struggle is finding what class to fit this co-instruction into?
In the South Carolina Association of School Librarians Listserv, this eloquent response by Renee Hobbs came up in conversation (I don’t know about you, but I am a listserv lurker) This doesn’t fit into the prong, but it helped me feel better about my practices.
My basic idea involves something about weather data from NOAA – having students try to turn raw weather data into a weather report – or potentially raw scores from sports – but I lack experience to do either.