January 2012 Volume 15 Number 1 - Educational Technology ...

system also computes a review value for each article, which is the percentage of vocabulary in the article a learner
should review. The Article Recommendation Agent then combines these three criteria to calculate the article
suitability level for every article for each learner.
Fuzzy Inference Mechanism
The fuzzy inference mechanism for finding a suitable difficulty level of articles for a learner consists of four steps,
including the Input, the Fuzzifier, the Inference and the Defuzzifier (Lee, Jian, & Huang, 2005; Zimmermann, 1991),
as shown in Figure 6.
The Input Step
Figure 6. The steps of fuzzy inference mechanism
In order to decide which article from the article database is the most suitable for a learner, the Article Features
Calculation module computes five feature values for every article, including the Average Difficulty of Vocabulary
(ADV), the Average Length of Sentence (ALS), the Total Length of Article (TLA), the Average Ability of Vocabulary
of the learner (AAV), and the Article Correlation (AC). The ADV, ALS, TLA, AAV and AC values all relate to the
content of an article, while AAV and AC are also related to a learner’s English ability and learning portfolio. The first
step is the formation of Input Linguistic Features, which involves computing the five feature values for articles and
processing them in the next step of the algorithm.
The Fuzzifier Step
This step computes the degree of membership for the linguistic feature values, i.e., the ADV, ALS, TLA, AAV and AC
of each article. This study uses the trapezoidal membership function for each linguistic term. Each fuzzy input
variable has three linguistic terms, namely, Low, Median, and High, each of which has a membership function to
represent its degree of membership. Table 2 summarizes the membership functions of the linguistic terms of the five
fuzzy input variables as defined for this research, and Figure 7 depicts an example of a membership function with
respect to the feature variable ADV. The variables d1, d2, u1 and u2 represent the parameters which are used to
define a trapezoid membership function of the linguistic term (Yen & Langari, 1998). For example, the membership
function of ADV_Median is defined by four parameters, namely, d1 = 0.25, d2 = 0.625, u1 = 0.4 and u2 = 0.425 as
shown in Figure 7.
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