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Determinants of academics performance in mathematical analysis for biology / |
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Prepuse, Paul Angelo A. |
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201046 |
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Undergraduate Thesis (Bachelor of Science in Mathematics) - Pamantasan ng Lungsod ng Maynila, 2010. |
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ABSTRACT: Statement of the Problem The main objective of the study is to analyze determinants of academic performance in Mathematical Analysis for Biology. Specifically, this study sought to answer the following questions. 1.) What is the academic performance of the respondents in the following subjects: a) College Algebra (Math 11) b) College Trigonometry (Math 12) c) Mathematical Analysis (Math 131) d) Grade in 4th year High school mathematics 2.) How do the cognitive determinants when taken singly relate to the academic performance in mathematical analysis? A. Cognitive Determinants a) College Algebra (Math 11) b) College Trigonometry (Math 12) c) Mathematical Analysis (Math 131) 3.) To what extent do the above mentioned variables when taken singly predict individual achievement in Mathematical Analysis? 4.) Which of the above variables is the best predictor of academic performance in Mathematical Analysis? The subjects of this study were the 60 B.S. Biology Students who were admitted since year 2006-2007 and then enrolled in Mathematical Analysis during the second semester 2008-2009 in College of Science at Pamantasan ng Lungsod ng Maynila. The school records of the biology students will serve as an instrument used in the study. The researcher undertook data collection on February 25, 2010 to get records at the College of Science with the following Mathematics Subjects such as College Algebra, College Trigonometry and Mathematical Analysis. The researcher also considered grade in High School Mathematics of biology students and this was obtained from College of the Registrar with a letter of request given on March 16, 2010. Data analysis was performed through the testing of the 3 null hypotheses. The Percentage and Means are used to give the participant's profile. Mean as a measure of central tendency and Standard Deviation were used to describe academic performances of the students in the subject covered in this study. Correlation Coefficient, Coefficient of Determination and Linear Regression Formula were used to determine how each determinant relates to predict individual achievement in Mathematical Analysis. FINDINGS From the results computations, the following were revealed: 1.a Academic performance of the BS Biology students are described by means and Standard Deviation and this are the following results in each determinants: College Algebra, 2.317 & .419, College Trigonometry, 2,206 & .510, Mathematical Analysis, 2.498 & .452, Grade in 4th year High School Mathematics, 86.385 & 4.050. The means of College Algebra, College Trigonometry, Mathematical Analysis and Grade in 4th yr. High School Mathematics were verbally interpreted as Satisfactory, Good, Satisfactory and Very Good respectively. 2.a Based on the coefficient of determinants (r2), 36.7% of the variation in the academic performance in Mathematical Analysis is explained by academic performance in College Algebra. It was found to have a high correlation and direct relationship. 2.b Based on the coefficient of determination (r2), 42.0% of the variation in the academic performance in Mathematical Analysis is explained by academic performance in College Trigonometry. It was found to have a high correlation and direct relationship. 2.c Based on the coefficient of determination (r2), 11.2% of the variation in the academic performance in Mathematical Analysis is explained by academic performance in grade in 4th yr High School Mathematics. It was found to have a substantial correlation and inverse relationship 3.a Extent of individual achievement in Mathematical Analysis for each determinant are as follows: Each determinant was considered independent variable (x) as the extent in which the individual achievement in mathematical analysis as dependent variable (y) could be predicted for every value of independent variable (x) there is a corresponding value for dependent variable (y). a. College Algebra: Y =.987+ .652x with an std. error of estimate of .362 b. College Trigonometry Y = 1.232+ .574x with an std. error of estimate of .347 c. Grade in 4th yr. High School Mathematics Y=5.715-.037x with an std. error of estimate of .429 4.a The academic performance in College Trigonometry gave the highest variation in the academic performance in Mathematical Analysis based on the value of r2, simply means that academic performance in College Trigonometry is therefore the best predictor of academic performance in Mathematical Analysis. CONCLUSION The following conclusions were drawn on the bases of the findings of this study: 1. A typical B.S. Biology student enrolled in Mathematical Analysis at Pamantasan ng Lungsod ng Maynila has a Satisfactory performance in College Algebra (Math 11), Good performance in College Trigonometry (Math 12), Satisfactory performance in Mathematical Analysis (Math 131) and Very Good Performance in 4th yr High School Mathematics. 2. Academic Performance in mathematical analysis is a highly affected by determinants such as College Algebra (Math 11) and College Trigonometry (Math 12) as indicated in the value of r2. 3. Among the determinants the best predictor based on the value of r2 is the College Trigonometry which best explains academic performance in Mathematical Analysis. RECOMMENDATIONS Based on the conclusions arrived at, the following recommendations are proposed: 1. Biology Students must be encouraged in to pursue to do well in Mathematical Analysis and other subjects. 2. Concepts and fundamentals used in Mathematical Analysis should be emphasis and integrated with other subject like College Algebra and College Trigonometry as well as in the other biology subjects. 3. Diagnostic test should be given and administered at the start of the semester to determine the weak points which were found to have a considerable effect in the academic performance in Mathematical Analysis. By this, appropriate actions will be taken place to make a remedial teaching to provide preparations for the students to meet adequate requirements of Mathematical Analysis. 4. Further investigation should be conducted along the other variables especially to the other subjects and non-cognitive determinants not covered in this study such as study habit, interest in the subject, English proficiency instructions and age. |
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| Related parts |
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| Relationship information |
|
| Title |
|
| Record control number |
|
| 800 ## - SERIES ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
|
| -- |
|
| Dates associated with a name |
|
| Language of a work |
|
| Date of a work |
|
| Title of a work |
0 |
| Fuller form of name |
|
| Volume/sequential designation |
|
| 810 ## - SERIES ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
|
| -- |
|
| Subordinate unit |
|
| Date of a work |
|
| Title of a work |
|
| -- |
|
| Volume/sequential designation |
|
| 830 ## - SERIES ADDED ENTRY--UNIFORM TITLE |
|---|
| International Standard Serial Number |
|
| -- |
|
| Uniform title |
|
| Name of part/section of a work |
|
| Number of part/section of a work |
|
| Language of a work |
0 |
| Volume/sequential designation |
|
| 942 ## - ADDED ENTRY ELEMENTS |
|---|
| Institution code [OBSOLETE] |
|
| -- |
lcc |
| Item type |
Book |