Parameter Profiles

Several numeric and text based parameters are elicited during the construction of a Knowledge Structure map. Numeric parameters are given value during interview by the adjustment of sliders. The numeric parameters used in this study are defined on the parameters page.

This page considers how parameters have been assigned to all of the knowledge nodes on the map. The number of knowledge nodes elicited for this map was 107 so the number that parameters have been provided for is one less that this, 106 (excluding the knowledge focus node). A series of graphs show how values for parameters, 0--10, have been assigned for all of the knowledge nodes on the map. Each graph is a plot of number of nodes against the value assigned. Each step along the x axis shows how many knowledge nodes were given a specific parameter value. Therefore if 20 knowledge nodes were given the value 5 then the height of the graph at x = 5 would be 20.

The average value for Importance is 6.29

Expert opinion appears to show that in general, the knowledge is quite important.

The average value for Recovery is 3.47

Expert opinion appears to show that in general, the knowledge is slightly difficult to replace.

The average value for Study-Exp is 3.48

Expert opinion appears to show that in general, the knowledge is generally learned by study.

The average value for StaffAvailable is 2.84

The chart shows that experts believe that on balance, the knowledge area is 3 people available to deliver this knowledge.

The average value for StaffNeeded is 3.32

Expert opinion appears to show that in general, the knowledge is 3 people to deliver this knowledge.

The average value for Specialised is 4.96

Experts interviewed, appear to believe that the knowledge as a whole is partially specialised knowledge.

The average value for Stability is 6.46

Experts interviewed, appear to believe that the knowledge as a whole is quite stable knowledge.

The estimated learn time for the entire knowledge structure of this map is about 7 years. Time savings may be possible by running parallel programmes for knowledge sub structures.