Factors Influencing Scholastic Achievement in the Study of Mathematics for Pre Engineering Students

Abstract

study of Mathematics of the Pre-Engineering students of first year, Certificate Level, to
find a good forecaster and build equations forecasting the achievement in learning Mathematics of
the Pre-Engineering Students, First Year of Certificate Level. The amount of sample group in
this research is 229 peoples of first year pre-engineering students of College of Industrial
Technology, King Mongkut’s University of Technology North Bangkok at school year 2010
which are chosen by stratified random sampling. The research result found that the factors
influencing the scholastic achievement in learning Mathematics were as follows:
1. The whole aspect-the result of Mathayomosaksa 3 learning (x3), statistics of absence
from learning Mathematics II (x4), personal extra class attending (x5), and the result total
marks from the questionnaires asking the attitude toward Mathematics (J2), there was the
regression equation Ŷ = 3.111+2.191x3-3.464x5-3.639x4+0.343(J2), with the co-efficient determinant
(R2) equal to 60%.
2. Mechanical Option-the result of Mathayomosaksa 3 learning (x3), statistics of
absence from learning Mathematics II (x4), personal extra class attending (x5), and the result
total marks from the questionnaires asking the attitude toward Mathematics (J2), there was
the regression equation Ŷ = 6.161+2.336x3-4.024x5-3.265x4+0.279(J2), with the co-efficient
determinant (R2) equal to 61.4%.
3. Electrical Option-the result of Mathayomosaksa 3 learning (x3), statistics of absence
from learning in Mathematics II (x4), and the result total marks from the questionnaires
asking the attitude toward the teachers (J1), and toward Mathematics (J2), there was the
regression equation Ŷ = 20.909+2.093x3-3.22x4-0.344(J1)+0.381(J2), with the co-efficient
determinant (R2) equal to 67.7%.
4. Civil Construction Option-the result of Mathayomosaksa 3 learning (x3), monthly
income of the parents (x7), personal extra class attending (x5), and the statistics of absence in
learning Mathematics II (x4), there was regression equation Ŷ = 22.340+1.453x3-1.27x7-2.735x5
-7.138x4 with the co-efficient determinant (R2) equal to 55%