Transcript Methodology-Fitness analysis

國立雲林科技大學
National Yunlin University of Science and Technology
N.Y.U.S.T.
I. M.
Development of a reading material recommendation
system based on a knowledge engineering approach
Presenter : Yu-hui Huang
Authors :Ching-Kun Hsu, Gwo-Jen Hwang , Chih-Kai Chang
CE 2010
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Outline
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Motivation
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Objective
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Methodology
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Experiments
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Conclusion
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Comments
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Motivation
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It is importance of assigning proper articles to individual students for
training their reading ability.
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In traditional English classes, a teacher needs to guide dozens of
students to learn;
therefore, it is quite often that identical instructional materials,
especially reading articles, are prepared for every student .
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For some students, the articles could be too easy to read, while for
others, the articles might be too difficult.
Such an article assignment strategy is likely to cause the students to
lose interest in learning English
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Objective
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To improve the reading performance of students, it is important to
provide personalized reading recommendations to individual students
by taking their profile or learning performance into consideration.
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A knowledge engineering method is proposed to assist teachers to
cooperatively define English article recommendation rules for individual
students.
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Methodology-Establishing repertory grids
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A single repertory grid is represented as a matrix whose columns have
element labels and whose rows have construct labels. Elements
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Elements could be decisions to be made, objects to be classified, or
concepts to be learned.
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Constructs are the features for describing the similarities or
differences among the elements.
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Methodology
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Each construct consists of a trait and the opposite of the trait. A 5-scale
rating mechanism is usually used to represent the relationships
between the elements and the constructs.
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‘‘5” represents that the element is very likely to have the trait;
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‘‘4” represents that the element may have the trait;
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‘‘3” represents ‘‘unknown” or ‘‘no relevance”;
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‘‘2” represents that the element may have the opposite characteristic of the trait;
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‘‘1” represents that the element is very likely to have the opposite characteristic of the
trait in the study.
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Methodology
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In the study, the expert knowledge was obtained by interviewing two
experienced English teachers in a senior high school.
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Furthermore, the features and preferences for characterizing the
learners were acquired from both the students and the experts.
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That is, two kinds of repertory grids were established.
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One is the repertory grid for categorizing the selected reading articles;
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the other kind is for characterizing the learners based on their preferences for English
readings.
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Methodology
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For the first kind of repertory grid developed by interviewing the
teachers, the data concerning the articles with different difficulty levels
were collected and presented in the corresponding grids.
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The English teaching experts determined the difficulty levels of 100
articles based on the vocabulary and sentence difficulty degrees as
defined by the General English Proficiency Test (GEPT).
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Methodology
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For the second kind of repertory grids, the preferences of individual
students for English readings are collected and recorded.
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Methodology-Fitness analysis
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A fitness analysis formula is used to compare the preferences of
individual students with the traits of each article.
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N is the number of constructs (or traits),
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MaxScore is the maximum rating in the repertory grid,
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Ai represents the ith article, Sj represents the jth student
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|gi,k gj,k| represents the distance between the ith article and the jth student based on the kth
trait in the repertory grid.
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Methodology-Fitness analysis
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Methodology-Article recommendation strategy
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Based on the fitness analysis results, an English article recommendation
strategy is proposed for developing the expert system:
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Step 1: Identify the English reading ability of the student.
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Step 2: Determine the candidate list of articles based on the English reading ability of the
student.
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Step 3: Test whether the condition |gic gjc| < 3 is true.
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Step 4: If |gic - gjc| < 3, calculate the fitness degree between the student preference and the
topic of the article.
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Step 5: If |gic - gjc|>=3, test whether the condition (gic - gjc) < 3 is true.
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Step 5.1: If (gic - gjc) < 3, calculate the fitness between the student preference and the topic
of the article.
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Step 5.2: If (gic - gjc)>=3, no recommendation will be given and fitness = 0.
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Experiments
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Experiments
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Experiments
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Experiments
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Conclusion
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The major contribution of this study is to propose a way for developing
an expert-like English reading recommendation by taking both
preferences and knowledge levels of individual students as well as
categories and traits of articles into consideration.
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In the future, it is suggested that the number of articles should not only
be continually increased to promote the chances of successfully
matching the users’ needs, but that the number of article categories
should also be increased so that more students can get recommended
articles.
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Comments
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Advantage
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Drawback
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Application
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