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

memory cycle updates and analytic hierarchy process (AHP) to help a learner in an extensive reading environment.
By intensive reading of suitable articles, a learner can maintain their interest in learning English and, at the same
time, improve his/her English ability efficiently. The system architecture is shown in Figure 4.
System Description
The major modules for carrying out the computation procedures are the Article Preprocessing Mechanism, the
Degree of Vocabulary Cognition Calculation, the Memory Cycle Calculation, the Fuzzy Inference Mechanism, the
Review Value Calculation and the Article Features Calculation. The descriptions of them are given as follows:
The Article Preprocessing Mechanism adds a newly collected article to the article database and computes
correlations with other articles already in the database. It also calculates the frequencies of the words in the article
and includes these values, along with the vocabulary, into the GEPT vocabulary database. A learner interacts with
the system via a User Interface. To newcomers, the system provides a self-assessed questionnaire to identify their
interests in different topics to create learner profiles in the user portfolio database.
For every news article collected, the Article Features Calculation module has five generators for computing the five
main feature values, i.e., the Average Difficulty of Vocabulary (ADV), the Average Length of Sentence (ALS), the
Total Length (TLA), the Average Ability of Vocabulary of a learner (AAV), and the Article Correlation (AC). The
five feature values are used by the Fuzzy Inference Mechanism to generate a document fitness degree for each article
for each learner. The Review Value Calculation module computes the review value for each article for a given
learner by counting the words that should be reviewed by that learner. The higher the value is, the greater number of
words the article contains that should be reviewed by the learner. The Document Recommendation Agent calculates
the recommendation score (RS) of every article for a given learner according to that learner’s interest, the document
fitness degree, and the review value of the article. The system suggests the article with the highest recommendation
score to a learner.
After the learner reads the article, the system picks from the article words that the learner is supposed to have
comprehended based on his/her ability level and generates an immediate test, which serves to check whether the
learner has understood the meaning of newly-encountered words. The test result feeds back via the Learning
Feedback Agent to the Degree of Vocabulary Cognition Calculation module for computing and updating the
vocabulary abilities in the learner's profile in the user portfolio database; at the same time, the Memory Cycle
Calculation module re-evaluates the memory cycles of the learner for the learned vocabularies that appear in the
content of the article and updates them in the user portfolio database. When a learner logs in again, a suitable article,
which contains words with memory cycles that are due to be reviewed, will be recommended by the Article
Recommendation Agent for the learner. The details of each process are discussed as follows.
User Interface
User registration
A learner’s English vocabulary size matters much in learning English (Mochizuki & Aizawa, 2000); in this study, the
Degree of Vocabulary Cognition (DVC) is regarded as a learner’s English ability. Table 1 shows the DVC used in
this research with its corresponding values. To calculate the English ability of a first-time user, the Computerized
Adaptive Testing (CAT) approach is adopted. Ten questions are tested. The vocabulary for CAT is selected from
elementary, intermediate, and high-intermediate levels of GEPT; 50 questions are generated for each level. A
question contains one English word and four Chinese translations, among which one is correct. According to the
result of the CAT and using a one-parameter IRT model and the maximum likelihood estimation method, the
vocabulary volume of a learner is evaluated.
The proposed system recommends English news articles of various topics collected from Internet for intensive
reading. To address learners’ Degree of Article Preferences (DAP), collected news articles are classified into eight
categories, including Politics, Society, Sports, Business, Arts, Technology, Health and Travel. The proposed system
uses a learner-assessment interface to interact with a newcomer in order to evaluate the preferences of the learner
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