Some good pointers to [[Brian Eno c]] work and thinking, to follow up.
Also good anecdote from one of those links on Rem Koolhaas notion of n:: premature sheen Making things look nice early takes away from thinking about other points of quality. Jeremy applies it to AI too, the premature sheen generate awe, but not quality output.
Nevertheless, despite the impact of multimedia tools on the improvement of teaching and learning activities, it could be counterproductive if the computer-based tools are not properly designed or the instructional materials are not well composed.
Quality is very important
We also list some best practices and design principles to ensure better quality designs for children of different ages.
The ages thing I needed
The Evaporative Cooling Effect describes the phenomenon that high value contributors leave a community because they cannot gain something from it, which leads to the decrease of the quality of the community. Since the people most likely to join a community are those whose quality is below the average quality of the community, these newcomers are very likely to harm the quality of the community. With the expansion of community, it is very hard to maintain the quality of the community.
via ref to Xianhang Zhang in Social Software Sundays #2 – The Evaporative Cooling Effect « Bumblebee Labs Blog [archived] who saw it
via [[Eliezer Yudkowsky]] in Evaporative Cooling of Group Beliefs
This approach also splits email development for modern email clients and older clients in two. You can use Safari/Chrome to test and develop modern techniques for WebKit-supported clients while using Firefox for your baseline experience for older clients like Outlook.
Research, B. and S. S. at N. (2020, May 25). Imagining a different online world. Behavioural and Social Sciences at Nature Research. http://socialsciences.nature.com/users/397838-philipp-lorenz-spreen/posts/imagining-a-different-online-world
A promising approach that addresses some worker output issues examines the way that workers do their work rather than the output itself, using machine learning and/or visualization to predict the quality of a worker’s output from their behavior [119,120]
This process improvement idea has some interesting design implications for improving temporal qualities of SBTF data: • How is the volunteer thinking about time? • Where does temporality enter into the data collection workflow? • What metadata do they rely on? • What is their temporal sensemaking approach?