System for assisting teachers to analyze elementary school students' decimal misconceptions and supporting decision making of teaching strategies

碩士 === 國立新竹教育大學 === 應用數學系碩士班 === 95 === The conceptions of decimal fraction are complicated for elementary school students. The misconceptions may be occurred because of the difficulties of understanding the knowledge of decimal fraction. Thus, teachers need to detect students’ misconceptions before...

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Bibliographic Details
Main Author: 黃孟文
Other Authors: 區國良
Format: Others
Language:zh-TW
Published: 2007
Online Access:http://ndltd.ncl.edu.tw/handle/20037406151084401573
Description
Summary:碩士 === 國立新竹教育大學 === 應用數學系碩士班 === 95 === The conceptions of decimal fraction are complicated for elementary school students. The misconceptions may be occurred because of the difficulties of understanding the knowledge of decimal fraction. Thus, teachers need to detect students’ misconceptions before the adaptive teaching process applying on students’ learning. Parallel testing is widely used in assessing teachers to detect students’ learning performance; however, traditional testing sheets are time-consuming for teachers to construct the parallel tests in a short time. Furthermore, it do not support teachers to detect students’ misconceptions. This paper proposed an online parallel test generator for teachers to construct parallel testing sheets in a short time and detect elementary school students’ misconceptions about decimal fraction. The parallel test generator is performed under 53 item templates predefined with restrictions and reduces teachers’ efforts in constructing the parallel tests. After students’ misconceptions are detected. Each student’s misconception structres could be illustrated graphically in a tree structure by the concept map technique, which is helpful for teachers to analyze student’s learning performance and make a decision to classify each student into adaptive teaching strategies. The adaptive teaching strategies are categoried into four classes: (1) supervised study (2) reciprocal peer tutoring (3) abidance of family, and (4) guidances of teachers. The machine learning techniques are used to assist teachers classifying each student into adaptive teaching strategy with a high accuracy in time.