A Study of the Data Mining Applied on the Test Score Analysis of Digital Learning- As a Case of Organ Donation Internet Learning Platform

• Main Authors: Yeh-Chia Huei, 葉佳惠
• Other Authors: Ming-Ju- Hsu
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/99feck

碩士 === 佛光大學 === 傳播學系 === 103 === Data mining was introduced in this study for the test score analysis of digital learning on the selected Organ Donation Internet Learning Platform. The subjects participating in this study were medical workers, social workers and the lay people that had learning experience with the platform and took the online test. 12,679 valid pieces of data were obtained from the platform database using data scrubbing. The study was intended for Knowledge Discovery in Database (KDD) through data mining with the database of the platform mentioned as the mining target. The program, SPSS Statistics 18.0, Chinese version, specialized in statistics and data management was adopted for the study. The analysis was performed based on descriptive statistics, t-test with independent samples, one-way ANOVA in the attempt to discover the potential, valuable knowledge of individual trainees in the digital learning test and establish the KDD model for the “Organ Donation Internet Learning Platform.” The purpose of the study was to identify whether the background variables selected (gender, age, expertise, identity background, service location, medical institute of employment, transplant hospital of employment, type of department of employment, year, type of article) display significant difference in the score of digital learning test from 2012 to 2014. According to the study results, gender, expertise, medical institute of employment, transplant hospital of employment and type of article displayed significant difference in the digital learning test score as tested with the t-test with independent samples. Age, identity background, service location, type of department of employment and year displayed significant difference in digital learning test score as tested with one-way ANOVA. The verification with Scheffé posteriori comparison suggested significant difference in “year,” and partially significant difference in “age, identity background, service location and type of department of employment.” The test score was higher in the age group of 46-65 than 18-35; nursing staff had higher scores than doctors; the score was higher in central Taiwan than northern and southern Taiwan; and those who work at a non-medical department scored higher that those working at a department associated with emergency or critical conditions. For the variable of year, the score was higher in 2014 than in both 2012 and 2013. The data mining performed on the “Organ Donation Internet Learning Platform” for KDD yielded the following findings: “those who are male, younger (between 18 and 35), not a professional worker, doctor or nursing staff, worked in southern Taiwan, worked at a medical institute, worked at a transplant hospital and worked at a department associated with emergency or critical conditions” required improvement in the organ donation training and knowledge, and the KDD model of the “Organ Donation Internet Learning Platform” was developed. Demassification was suggested for medical workers and the lay people; that is, to identify the learning need of individual subjects before providing articles of different depths and tests of varied difficulties according to the subjects. This part of findings may help the Ministry of Health and Welfare and stakeholders with the modification and addition of online testing systems for digital learning, determination of digital learning strategies, development of test questions in terms of contents, types and difficulties, and the establishment of policies in this regard. Hopefully, the quality of digital learning will be improved and the concept and knowledge of organ donation are more widespread.