[[alternative]]A Study on Relevant Factors of Computer Learning Achievement of Junior High School Students

[[abstract]]In this age of information, information has become an important part of our life. Due to this demand, computer education has been recognized its importance and added to our junior high school curriculum. The purpose of this study is to investigate factors that affect computer learning achievement of junior high school students. The method used here is by random survey. A sample size of one thousand two hundred and twenty four junior high school students were chosen. These samplers were asked about their attitude towards computer learning. Samplers’ grades of Computer course and intelligence quotient (IQ) were examined for relation. A total of 1,160 students out of 1,224 were valid samplers. T-test, One-way ANOVA, Person Product-moment Correlation and multiple regression were applied in the examination. The results are as follows: A.Student’s gender, IQ, subject preference, parent’s educational level and social status have correlation with their computer attitude and computer learning achievement. B.Class size, school location and school’s historical development have correlation with student’s computer attitude; however, computer learning achievement is not correlated with these variables. C.The more computer-learning experience a student has, the better computer attitude he/she has. So is his/her computer learning achievement. Starting learning computer at different age will result in the difference in computer attitude and learning achievement. D.Students with home computer facilities, with people to discuss with, and with computer books or magazines have better attitude as well as learning achievement in computer. E.There is significant correlation among computer attitude, learning achievement and the three aspects: cognitive, psychomotor and affective domain. F.There are six valid predictive factors: IQ, computer anxiety, gender, class size, access to computer books, and weekly computer usage hours. The predictive accuracy value was 78.1%.