Biology Interest Among Asasipintar Students

biological science participation among ASASIPINTAR STUDENTS A MINI PROJECT REPORT Submitted by 1. AHMAD SYAZWAN BIN SUHAIMI AP00161 2. IZZATY SHAIMA BINTI SHAMSUDIN AP00164 3. MUHAMMAD FAIZUAN BIN AMINUDDIN AP00159 4. SITI NABILA AMIRA BINTI SAMSUDIN AP00158 Submitted toMiss Noraniza Binti IbrahimSTATISTICS (PNAP0154)ASASIpintarPUSAT PERMATApintarTM NEGARAUNIVERSITI KEBANGSAAN MALAYSIA (UKM)APRIL 2013 get across of Contents Content Page Abstract Introduction to project upshot Methods of Data abbreviation Analysis and Results Conclusion References Appendix ABSTRACT about students have to take biology as 1 of the subjects graded in their CGPA. But not all students want to be a prep are or have much touch in biology. Quizzes and canvas are frequently used to measure the level of understanding of students towards limited theme of a subject Biology quizzes are common, and their marks or win in these quizzes can be used to measure either their suit in the quizzes or their wager in biology or perchance both.This look into paper discussed the relationship surrounded by the avocation in biology and their aggregate realise, sexual activity and their flying field expressive panache and lastly the relationship amongst holds 1. INTRODUCTION 2. 1. Overview Biology is one of the compulsory courses that have to be taken by ASASIpintar students. This course aims to enhance the students understanding and knowledge in biological sciences. Teaching methods include small group lecture, tutorial, laboratory experiments, autarkical learning and problem based learning. Students will be assessed by periodic quizzes, lab reports, and mid-semester and final semester examination.However, the beguile level of students in Biology differs from one another. Other than that, their style of take oning Biology or doing their revision on this particular subject is also different in the midst of students. This project aims to guinea pig the relationship surrounded by these devil factors, which are the level of interest in Biology and their style of learning and get wording the subject with the scores that these students gained in their topical quizzes. 2. 2. Objectives 2. 3. 1. To study the relationship in the midst of interest and primitive score 2. 3. 2.To investigate the distribution of interest in biology among student 2. 3. 3. To investigate the relationship between sex activity and study style 2. 3. Research school principal 2. 4. 4. Does interest has any relationship with the gist scores gain by student in their quizzes? 2. 4. 5. What are the distribution of interest in biology among student? 2. 4. 6. Is there any relationship between gender and their style of study biology? 2. 4. Research assumption A statistical conjecture is a conjecture about the population parameter. This conjecture may or may not be true.Null surmise (Ho) is a statistical hypothesis states that there is no difference between a parameter a nd a specific value, or that there is no difference between the two parameters while choice hypothesis (H1) is a statistical hypothesis that states the earth of a difference between a parameter and a specific value, or states that there is a difference between two parameters. 2. 5. 7. possibleness 1 Ho in that location is no relationship between interest and total score H1 There is relationship between interest and total score 2. 5. 8. Hypothesis 3 Ho The students interest in biology are distributed as follows 17. % are not interested, 20% are comprise and 62. 5% are interested in biology. H1 The distribution are not the same as stated in Ho. 2. 5. 9. Hypothesis 2 Ho There is no relationship between gender and study style H1 There is relationship between gender and study style 2. 5. 10. Hypothesis 4 Ho There is no relationship between interest and study style H1 There is relationship between interest and study style 2. METHODOLOGY Herein, the chosen respondents were randomly sel ected from ASASIpintar students. The survey methods are the research instruments used for the info collection. 0 students of ASASIpintar were chosen in this study civil a questionnaire to assess their biology quizzes marks. The computed values are compared to the Likert scale for data interpretation. The collected data were analyzed utilize SPSS software. These will be presented to a lower place 3. 5. Descriptive statistics The descriptive method is used to collect the necessary data. In the descriptive statistic, the measures of tendency (mean, mode, median and variance) will be calculated. Measures of tendency are numerical values that locate, in some sense, the center of a data set.The data will be presented in bar chart or pie chart for qualitative data and histogram for quantitative data. 3. 6. Inferential statistics The inferential statistics utilise sample data to draw coclusions about the ASASIpintar students. The sample random is selected and the study gained from it is used to make generalizations about the ASASIpintar students. 3. 7. 11. Correlation 3. 7. 12. 1. Pearsons correlation coefficient render was used to determine the relationship of non-parametric data. One of the tests is to check the relationship between gender and the study style.The linear correlation coefficient (r) is used to measure the force out and direction of a linear relationship between two variables 3. 7. 12. 2. Spearmans correlation coefficient test was used to determine the relationship between parametric and non-parametric data. One of the tests is to check the relationship between interest of the students towards biology and their total score. The linear correlation coefficient (r) is used to measure the strength and direction of a linear relationship between two variables 3. 7. 12. Comparison Test 3. 7. 13. 3. Chi-squareThe Chi-square goodness-of-fit test is used to how well a particular statistical distribution, such as a binomial or a normal. The null hypothes is Ho is that the particular distribution does provide a model for the data the alternative hypothesis H1 is that it does not. 3. ANALYSIS AND RESULTS 4. 7. Descriptive statistics 4. 8. Inferential statistics 4. 9. 13. Relationship between interest and total score VARIABLES R R SQUARE stakes and add together score . 399 . 159 Since r = 0. 399, there is weak positive correlation between total score and interest. Since r= 0. 159, this indicates that 15. % of the variant in total score can be attributed to the linear relationship with the interest. 15. 9% of total variation in total score is explained by regression line using the interest. Another 84. 1% is explained by other variable. Since the P-value is 0. 011 and it is less than ? -value, the null hypothesis is rejected. There is decent evidence to show that there is relationship between the interest and the total score. It is proven that the interest does affect the total score. 4. 9. 14. scattering of interest in biology VAR IABLES P-VALUE Interest in biology 0. one hundred ninetySince the P-value is 0. 19 and it is more than ? -value, the null hypothesis is failed to be rejected. There is sufficient evidence to show that the students interest in Biology are distributed as follows 17. 5% are not interested, 20% are moderate and 62. 5% are interested in biology. 4. 9. 15. Relationship between style and gender VARIABLES P-VALUE Style and Gender 0. 558 Since the P-value is 0. 558 and it is more than ? -value, the null hypothesis is failed to be rejected. There is sufficient evidence to show that there is no relationship between the study style and gender.It is proven that the gender is independent to the study style. The study style may affected by environment and the students self. 4. CONCLUSION 5. REFERENCES 6. concomitant 7. 9. Questionnaire Personal information Age Gender Interest in biology 1 2 3 4 5 Which of the following is the appearance you study? get wind alone Group study Cont inuous study Stay up What is your marks in following quizzes? The cell cellular respiration Biochemistry Photosynthesis Plant physiology 7. 10. Analysis of interest & total score Correlations pithScore InterestSpearmans rho innateScore Correlation Coefficient 1. 000 . 399* Sig. (2-tailed) . . 011 N 40 40 Interest Correlation Coefficient . 399* 1. 000 Sig. (2-tailed) . 011 . N 40 40 *. Correlation is significant at the 0. 05 level (2-tailed). 7. 11. Analysis of gender style Correlations Style Gender Style Pearson Correlation 1 -. 095 Sig. (2-tailed) . 558 N 40 40 Gender Pearson Correlation -. 095 1 Sig. (2-tailed) . 558 N 40 40 Case Processing appendmary Cases Valid absentminded extreme N share N Percent N PercentGender * Style 40 100. 0% 0 0. 0% 40 100. 0% Gender * Style Crosstabulation Style centre Discussion Study Alone Stay up continuous study Gender staminate figuring 4 6 5 5 20 judge compute 4. 0 7. 5 4. 0 4. 5 20. 0 young-b earing(prenominal) believe 4 9 3 4 20 expect Count 4. 0 7. 5 4. 0 4. 5 20. 0 Total Count 8 15 8 9 40 evaluate Count 8. 0 15. 0 8. 0 9. 0 40. 0 Chi-Square Tests treasure df Asymp. Sig. (2-sided) Pearson Chi-Square 1. 211a 3 . 750 Likelihood Ratio 1. 221 3 . 748 Linear-by-Linear Association . 355 1 . 551 N of Valid Cases 40 . 6 cells (75. 0%) have expect turn over less than 5. The minimum expected count is 4. 00. 7. 12. Analysis of gender interest Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent Gender * int 40 100. 0% 0 0. 0% 40 100. 0% Gender * int Crosstabulation int Total not interested moderate interested Gender Male Count 2 6 12 20 Expected Count 3. 5 4. 0 12. 5 20. 0 % within Gender 10. 0% 30. 0% 60. 0% 100. 0% % within int 28. 6% 75. 0% 48. 0% 50. 0% % of Total 5. 0% 15. 0% 30. 0% 50. 0% female Count 5 2 13 20 Expected Count 3. 5 4. 0 12. 5 20. 0 % within Gender 25. 0% 10. 0% 65. 0% 100. 0% % within int 71. 4% 25. 0% 52. 0% 50. 0% % of Total 12. 5% 5. 0% 32. 5% 50. 0% Total Count 7 8 25 40 Expected Count 7. 0 8. 0 25. 0 40. 0 % within Gender 17. 5% 20. 0% 62. 5% 100. 0% % within int 100. 0% 100. 0% 100. 0% 100. 0% % of Total 17. 5% 20. 0% 62. 5% 100. 0% Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 3. 326a 2 . 190 Likelihood Ratio 3. 461 2 . 177 Linear-by-Linear Association . 63 1 . 686 N of Valid Cases 40 a. 4 cells (66. 7%) have expected count less than 5. The minimum expected count is 3. 50. ANOVA Sum of Squares df Mean Square F Sig. Score1 Between Groups 87. 811 4 21. 953 2. 331 . 075 Within Groups 329. 689 35 9. 420 Total 417. 500 39 Score2 Between Groups 31. 709 4 7. 927 1. 950 . 124 Within Groups 142. 266 35 4. 065 Total 173. 975 39 Score3 Between Groups 9. 376 4 2. 344 . 710 . 591 Within Groups 115. 599 35 3. 303 Total 124. 975 39 Score4 Between Groups 21. 78 4 5. 494 1. 217 . 321 Within Groups 158. 022 35 4. 515 Total 180. 000 39 Score5 Between Groups 24. 961 4 6. 240 1. 195 . 330 Within Groups 182. 814 35 5. 223 Total 207. 775 39 We used the Other than that, Check relationship between interest and total score weak relationship Style and total score no correlation Between score correlation pearson Correlation coefficient spearman Style and interest no correlation pearson Gender and score -weak relationship spearman Style and gender chi square test = no relationship