資料探勘 : 學習與讀書策略

淡江大學機構典藏 > 工學院 > 資訊工程學系暨研究所 > 學位論文 > Item 987654321/35161

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题名:	資料探勘 : 學習與讀書策略
其它题名:	Data mining for learning and study strategies inventory
作者:	李延震;Lee, Yan-zhen
贡献者:	淡江大學資訊工程學系碩士班蔣定安;Chiang, Ding-an
关键词:	決策樹;關聯式法則;決策分析;自我評量表;Decision Trees;Association rules;Decision analysis;Self-Assessment Table
日期:	2006
上传时间:	2010-01-11 06:05:43 (UTC+8)
摘要:	當我們想要了解多數人的想法和心理時，經常透過問卷的方法來獲得客觀的數據，一份設計完成的問卷經常是由數十題的題目組成，塡寫起來相當費時容易另人產生排斥感，為避免此情況之發生我們利用資料發掘方式進行問卷的採礦，根據決策樹分析的樹狀分類圖，提供使用者利用少數重要問題來評量受測者，得知某徵狀後再利用關聯式法則判斷其它徵狀，以決定是否對其它徵狀做評量，再綜合各徵狀的評量結果決定是否需進一步評量，如此可以用少量的題目卻有近似的效果，簡化後的題目就可以不需要透過正式的問卷來詢問，可以在日常對話或一般訪談中提出，在不產生排斥感的情況下獲得初步的數據，找出可能有學習問題的疑似高困惱群，更可以將分析結果應用於程式中，提供使用者一個自我評量的管道；自我評量的結果還可以在經過統計分析後，將需要追縱輔導的使用者資料提供給諮商輔導員，供諮商輔導員做進一步的追縱輔導。在本論文中我們先使用IM8.1(Intelligent Miner for Data 8.1)將範本資料(學習與讀書策略問卷)做決策樹(Decision Trees)及關聯式法則(association rules)的分析運算，並將其規則存放入資料庫，利用ASP設計使用者介面，一般使用者可以利用此系統自我評量，諮商輔導員則可以在程式中檢視所有諮商結果，達到問卷調查的效果。 When we wish to understand the thought processes and psychology of a large number of people, we often utilize surveys to obtain objective data. These surveys, if they are structured in a thorough manner, are frequently composed of dozens of topic questions that can be quite time-consuming for the subject to complete and may therefore be looked upon by the subject with a feeling of dread. To avoid this sort of a situation, we have come up with a method of extracting valuable answers from surveys using the classification charts of decision tree analysis to evaluate the subject through a lesser number of more relevant questions. Once certain indicators have been pinpointed, associated rules can then be used to assess other indicators by deciding whether they can be assessed by the original indicator. We can then utilize our assessments of several indicators in conjunction with one another to decide whether another level of assessment is needed. It is possible in this way to reduce the number of topic questions while obtaining approximate results, and the simplified topic questions do not then need to be posed through a formal survey but can rather be presented in a regular dialogue or interview. Preliminary data can then be obtained without causing the subject to shrink away from the task and subjects that experience difficulties studying and stress at handling their problems can be pinpointed. It is possible then to apply the analysis results to the formula and provide the subject with an outlet for self-assessment. In addition, once the results of this self-assessment have undergone statistical analysis, the data for users that need to seek counseling can be provided to school counselors who can then provide yet another level of counseling. In this paper, we first use IM8.1 (Intelligent Miner for Data 8.1) as a model for this data (a questionnaire concerning learning and study strategies) to draw up the analytical operations for decision trees and association rules. Those rules can then be inputted into the database. Most users of the ASP design user interface can perform self-assessment using this system, and school counselors can inspect counseling results by looking at the equations and acquire survey results.
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