Digital E-Learning Path Recommendation Model for the 5th Grader of Math Course of Elementary School Basedon Petri Net—An Example by Using PaGamO E-Learning Platform.

碩士 === 亞洲大學 === 資訊工程學系 === 106 === The subject of fractional calculation is very difficult for many high-grade students in elementary school. The lacking clarity of fractional concept for elementary student will result to some disadvantages of advanced math learning in the future. The teacher should...

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Bibliographic Details
Main Authors: WANG, YEN-CHUN, 王彥君
Other Authors: CHEN, HSING-CHUNG
Format: Others
Language:zh-TW
Published: 2018
Online Access:http://ndltd.ncl.edu.tw/handle/a592ug
Description
Summary:碩士 === 亞洲大學 === 資訊工程學系 === 106 === The subject of fractional calculation is very difficult for many high-grade students in elementary school. The lacking clarity of fractional concept for elementary student will result to some disadvantages of advanced math learning in the future. The teacher should understand each student whether she or he achieves the skilled level in order to reduce the learn difficulty in next stage. In this paper, the participants in this research are total 14 students who are the 5th graders of an elementary school in Taichung city. They are assigned into two groups. One is the general group, and anothor one is the underachiever group. PaGamO E-Learning patform is adapted in this study for testing envirement as well as learning path control. In addition, Petri Net is used to construct and monitering the learing recommendation models. Finally, three results are gotten below. First, more than 70% of the students in general group still need to change the learning path in order to achieve the skilled level of fraction count. Second, after the third unit, students who pass the test at one shot in first two units, may need the same unit is repeated more than three times be able to achieve skilled level of fraction count. Third, more than 50% of students in underachiever group could not achieve skilled level of fraction count, when only learn once. For above three conclusions, this study propsed three learning path recommendation models.