Information Technology Reference
In-Depth Information
Step 5
elaborated at the end of this step. The set of local
solutions is denoted
S
= {
S
1
,
S
2
, …,
S
n
}.
For each performance characteristic a target is
established. Achieving in a very short time high
levels of quality of the web-based course represents
a big challenge for any educational provider. This
automatically requires innovation, which is here
called “target-oriented innovation.” For setting
up appropriate lines of innovation of the web-
based course, TRIZ method is applied at this step
of the methodology. This step is related to each
performance characteristic of each target-function.
Step 8
Local solutions have to be aggregated into a final
solution, which, in principle, should comprise to
the maximum possible extend the strengths of
all local solutions. In order to generate the final
solution of the web-based course as an aggregated
result of the local solutions, a specific algorithm is
required. An aggregation algorithm in five stages
is further proposed.
The aggregation algorithm starts with the
identification of the correlation types and cor-
relation levels between the target-functions
TF
1
,
TF
2
, …,
TF
n
. The correlation coefficients between
the target-functions are denoted here with
C
jk
,
j
=
1, …,
n
,
j
≠
k
. They could have negative values,
positive values or null values, depending on the
characteristics of the target-functions. For defining
the level of correlation, the following values are
used in practice: 0-no correlation; 1-weak posi-
tive correlation; 2-medium positive correlation;
3-strong positive correlation; -1-weak negative
correlation; -2-medium negative correlation;
-3-strong negative correlation.
Further, the aggregation algorithm considers
information from step 2 of the methodology. The
target-function with the highest value weight in
the set will be taken as the starting point in the
aggregation algorithm. It is symbolized with
PTF
the target-function having the highest value weight
from the
n
target-functions
TF
1
,
TF
2
, …,
TF
n
. If
W
max
(
t
) denotes the maximum value weight at the
moment
t
, the following relationship comes up:
Step 6
Development of a web-based course could meet
some supplementary barriers coming up from
financial, organizational and human resources
constrains. This category of barriers necessitates
further innovative solutions in setting up a com-
petitive web-based course. Innovation in this area
is called here “system-oriented innovation.” TRIZ
method could be also used at this point for tackling
properly the innovative problem solving process.
This step is related to each target-function.
Step 7
The set of vectors of innovation identified at steps
4, 5 and 6 are exploited for formulating local
solutions of the web-based course. Each local
solution should move towards satisfying as much
as possible the requirements of the corresponding
target-function, towards a so-called “local qualita-
tive optimality.” Local variants of the web-based
course effectively show the “multiple” facets of
the same system. They reveal the differences in
relevance of the modules of the same system when
it is placed in different contexts. This step actually
brings to live one of the most important properties
of complex systems: in a complex world there is
no single best solution-otherwise the problem
would not be considered complex. Thus, a set
of
n
local solutions of the web-based course are
W t
max
( ) max{
=
W t W t
( ),
( ), ...,
W t
n
( )}
.
1
2
(3)
In the next stage, the other target-functions are
grouped relative to
PTF
.
PTF
is correlated with
the other
n
-1 target-functions in various ways:
Search WWH ::
Custom Search