Often there is an "eye-roll" of exasperation at a student's "target grade", usually based upon (personally sceptical) primary school data and gcse results. What to do when "management" seek to make decisions based upon erroneous, fictitious and/or similar data quality? Enter, the new world of the "ai prompt".
Since initial teacher train days, the awareness of 'educational datalab' (Fischer Family Trust) was appreciated, but was not known (until recently) was that the information published by fft (via services such as 'aspire') uses public data (i.e. National Pupil Database) in conjunction of proprietary algorithms developed by fft. In a capitalist system, it is inevitable that a charity or private entity must create value from public data and charge accordingly, but in the absence of funds (e.g. private research by citc), artificial intelligence to the rescue. Thus, it was impressive to encounter the 16 to 18 transition matrices tool, to indicate relationship between gcse attainment and gce a results. Background guidance via the 'ready reckoner' and for those 'R' fans there is a nice introduction to the software code design of the 16 to 18 transition matrices dashboard. All good stuff! :)
