Download - Academy Publisher

Ⅳ. EVALUATION OF BLENDED LEARNING
Evaluation of blended learning involves in evaluation
on classroom lecturing, virtual teaching and blended
discussion. Every university has its own evaluation
system for classroom lecturing. The paper centered
around evaluation on virtual teaching and blended
discussion.
A. EVALUATION INDICATORS
In order to understand the requirement of students for
virtual classroom and blended discussion board, a
questionnaire was designed. The key questions about
blended learning was selected through interviewing
students, related teachers, and manager in charge of
teaching and learning assessment. After arranging the
investigated materials, an evaluation system for blended
learning was introduced. There are three main indicators
concerning quality of BB resource, interaction in BB
discussion board and quality of blended discussion
respectively. The quality of BB resource is divided into
three sub-indicators, which are abundance of resources in
BB, relativity of resource provided in BB, distribution
reasonability of resources in BB. And interaction in BB
discussion board consists the following sub-indicators
that are punctuality of teacher’s answer, accuracy of
teacher’s answer, energetic discussion inspired by teachers.
The last indicator- distribution reasonability of resources in BB
is represented by the abundance of the materials and its
relationship to the topic, the logic of the team representation
report, frequency of the team discussion, the accuracy and depth
of the problem solving, question posted and frequency of
answers, attitude of the group team.
In assessing the blended learning, the weight of each
indicator should be given. To decide the importance of
each indicator, a questionnaire was designed. The
questionnaire included 12 questions, which can be
classified into 3 categories and the questions can be
worked out by selecting the related choices. The first part
was designed for BB resources, the second part consisted
of questions involving interaction of blended discussion
board and the third part considered distribution reasonability
of resources in BB. After the questionnaire was revised,
420 questionnaires were distributed among teachers, staff
managing teaching affaire, student majored in financial
management, information management and information
system, international economic and trade, computer
science. 405 answered questionnaires were collected, and
399 of them were effective.
There exists fuzzy terms such as “excellent, good” in
the questionnaire, represented model should be selected
to handle it. Inter-valued fuzzy number fuzzy triangular
number, linguistic indices and linguistic 2-tuple
representation model can be used to represent fuzzy
terms[11,12,13]. Linguistic 2-tuple representation model
was selected to deal with fuzzy terms in the questionnaire
because it was more accurate in representing fuzzy terms
and had less loss in carry out the calculation[14].
B. LINGUISITC 2-TUPLE AND ITS OPERATOR
Suppose S={s 0 , s 1 , …,s g } be a set of labels assessed
in a linguistic term set with odd elements, which has the
following properties: 1ordered: when the index i≥j,
there must exist s i ≥s j ; 2a negation operator: Neg(s i )=
s g-i ; 3 there exists a min and max operator: si ≥ s j means
max(s i , sj )=si and min(s i , s j )=s j [13].
Let β be the result of an aggregation of the indexes of
a set S={s0, s1, …,sg}, for example, the result of a
symbolic aggregation operation. β ∈[ 0, g]
and g+1 is
the cardinality of S. Let i = round(β ) and α = β − i
be two values, such that, i ∈ [ 0, g]
and
α ∈[−0.5,0.5] then α is called a Symbolic
Translation[14].
Let S={s0, s1, …,sg} be a linguistic term set and
β ∈[ 0, g]
be a value representing the result of a
symbolic aggregation operation, then the 2-tuple that
expresses the equivalent information to β is obtained
with the following function[14]:
∇ :[0,
g ] → S × [ −0.5,0.5]
(1)
⎧ si
, i = round(
β )
∇(
β ) = ( s i
, α),
with⎨
⎩α
= β − i,
α ∈[
−0.5,0.5]
Where round(.) is the usual round operation, si had the
closest index label to β .
Let S={s0, s1, …,sg}be a linguistic term set and
−1
( s i
, α)
be a 2-tuple. There is always a ∇ function,
such that, from a 2-tuple it returns its equivalent
numerical value β ∈[ 0, g]
, which is:
∇
∇
−1
−1
: S × [ −0.5,0.5]
→ [0, g]
( s , α = i + α = β
i
)
From definition 1 , definition2 and proposition 1, we
can conclude that the conversion of a linguistic term into
a linguistic 2-tuple consist of adding a value 0 as the
symbolic translation, which is :
θ s ) = ( s ,0)
(
i i
(3)
Operation model of linguistic 2-tuple can be obtained
according to the linguistic 2-tuple representation model.
⑴ A linguistic 2-tuple negation operator.
Neg((
s , α))
= ∇(
g − ( ∇
−1
( s , α)))
i
i
(4)
⑵ Linguistic 2-tuple aggregation operators
Let ( s1,
α1),(
s1,
α
2
), L,(
s n
, α
n
) be a set with n
linguistic 2-tuples, the average operator of linguistic 2-
tuples ξ is[15]:
(( s , ),( s
ξ
1
α
1
2
, α ), L,(
s
2
, α )) = ( s,
α )
n
1
(5)
−1
= ∇(
∑∇
( si
, α
i
))
n i=
1
Let ( s1,
α1),(
s1,
α
2
), L ,( s n
, α
n
) be a set with
n linguistic 2-tuples and ω = ω , ω , L,
ω ) be the
n
n
(
1 2 n
(2)
83