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This is the point where Fruchter (2004) suggested the digital integration of the whole data creation, retrieval, and management system within building industry in order to prevent tacit knowledge loss and miscommunication among various parties from different disciplines.
Further research is required to help educators easily identify knowledge loss and the factors that contributed to that knowledge loss.
There is no clear pattern in these relationships and further research is required to explore whatever link there may be between knowledge loss and examination results and student evaluations of courses.
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Spitzer described a de-learning curve with a very rapid initial knowledge-loss and a subsequent steady state starting around 3 weeks and extending to at least 9 weeks.
An outcry ensued, of course, but management and HR began to face the painful fact that they had been aiding and abetting a vicious cycle of capability dependency and knowledge loss.
Given that knowledge loss is multifactorial (initial learning and ongoing rehearsal) it is unlikely that student evaluations (able only to approximate course quality and therefore perhaps initial learning) would be good predictors of long term retention.
A statistically significant main effect of knowledge loss (F = 297.385; p <.001) and an interaction effect by course (F = 46.081; p <.001) were found.
Using a repeated measures MANOVA a statistically significant main effect of knowledge loss (F = 297.385; p <.001) and an interaction effect by course (F = 46.081; p <.001) were found.
At a local level awareness of knowledge loss can be used to target courses for review and revision and identify weaknesses in the overall educational program.
It was expected that some courses would show more and some courses would show less knowledge loss.
As an example, further research should integrate risks of security in managing data and analytics as well as related risks of knowledge loss.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com