A Case Study of 'High-Failure Rate' Mathematics Courses and its’ Contributing Factors on UiTM Sarawak Diploma Students

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CONFERE NCEONS CIENTIFI C&SOCIAL RESEARCH

A Case Study of 'High-Failure Rate' Mathematics Mathematics Courses and its’ Contributing Factors on UiTM Sarawak Diploma Students 1

2

3

Tang Howe Eng , Voon Li Li and Nor Hazizah Binti Julaihi

Faculty of Information Technology and Quantitative Science, Universiti Teknologi MARA, Sarawak [email protected] , [email protected] [email protected] 2 and [email protected] [email protected] ABSTRACT There have been some concerns raised by Heads of Programs in UiTM Sarawak over the influence of the Mathematics courses passing rate on the full-time diploma students’ academic performance. th According to the Academic Affairs Division (HEA) of UiTM Sarawak, during the 57 Staff Academic Meeting, Mathematics courses were the courses that had been identified as the ‘high-failure rate’ courses. Inspired by the need to improve students’ performance in the Mathematics courses, this research was embarked to identify the ‘high-failure rate’ Mathematics courses offered to full-time diploma students in UiTM Sarawak and to investigate the relevant factors that contributed to the ‘high-failure rate’. Suggestions from lecturers were also determined in order to improve students’ performance in ‘high-failure rate’ mathematics courses. From the findings, MAT133, MAT183, MAT192 and MAT293 were recognized as ‘high-failure rate’ Mathematics courses. These courses were offered in science-based programs and had a significant portion of Pre-Calculus and Basic Calculus. SPM Additional had the strongest influence on the course marks as compared to other variables such as SPM Mathematics grades, size of mathematics class and gender. Although SPM Mathematics grades correlated to the course marks, it was found to be insignificant in the regression model. Class size had a significant influence on MAT133 but not the other three ‘high-failure rate’ Mathematics courses. Female students were found to perform slightly better than their male counterparts in all the four ‘high-failure rate’ Mathematics courses. This research concluded with some suggestions by the Mathematics lecturers’ of UiTM Sarawak to improve the existing Mathematics courses situation. Keywords: high-failure rate, factors, mathematics course, academic performance

1. INTRODUCTION

Mathematics is recognized as a gateway to future professions in variety of fields. Every area of Mathematics has its own unique applications to different career options. In the university level, most programs of study require Mathematics, as the ability to master mathematical skills is an important indicator of potential for students’ success in all levels of academics endeavors. Thus, it would be wise for students to enter university with a good background in Mathematics. Experience had shown that students who came to university with a poor grade in Mathematics had a difficult time progressing in the disciplines they had chosen to major in. International research studies had reported some underachievement in Mathematics (Blankley, 1994; Nongxa, 1996, Gerardi, 1990). In UiTM Sarawak, there have been some concerns raised by Heads of Programs over the influence of the Mathematics courses passing rate on the full-time diploma students’ academic performance. According to the Academic Affairs Division (HEA) of UiTM Sarawak, during the 57 th Staff Academic Meeting, Mathematics courses were the courses that had been identified as the ‘high-failure rate’ courses besides Law and Economic courses. According to Professor Dr Zainab Abu Bakar, the Dean of Faculty of Information Technology and Quantitative Sciences (FTMSK), during the Faculty meeting on 2 July 2008, five Mathematics courses namely MAT126, MAT133, MAT183, MAT149 and MAT199 had been identified as ‘high-failure rate’ courses. As considered by the top management of UiTM, ‘high-failure rate’ course is a course with the passing percentage of less than 70%. Paper number: 1

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Inspired by the need to improve students’ performance in the Mathematics courses, this research was embarked to identify the ‘high-failure rate’ Mathematics courses offered to full-time diploma students in UiTM Sarawak and to investigate the relevant factors that contribute to the ‘ high-failure rate’. Suggestions Suggestions from lecturers were also determined in order to improve students’ performance in ‘high-failure rate’ Mathematics courses. It is hope that the findings obtained from this research could give valuable inputs to the faculty and also the administrators of UiTM, especially in the Sarawak campus to set clear goals and devise new strategies to tackle these problems inherent in any ‘high-failure rate’ courses with a view to improve the students’ CGPA. 1.1 Research Objectives The following are the objectives of this research: 1. To identify the ‘high-failure rate’ Mathematics courses offered to full-time diploma students in UiTM Sarawak. 2. To investigate the relevant factors that affects the students’ performance of ‘high-failure rate’ Mathematics courses. 3. To determine the lecturers' suggestions in improving students' performance in 'high-failure rate' Mathematics courses. 1.2 Research Questions Based on the research objectives, this research was ca rried out to answer the following questions: questions: 1. Which Mathematics courses offered to full-time diploma students in UiTM Sarawak are identified as ‘high-failure rate courses’ for the past 7 semesters i.e. from semester January-May 2004 to semester January-May 2007? 2. What is the major field of the identified ‘high-failure rate’ Mathematics courses? 3. Do factors like SPM Mathematics grades, SPM Additional Mathematics grades, size of Mathematics class and gender affect the course marks of the ‘high-failure rate’ Mathematics courses? 4. What are the suggestions given by Mathematics lecturers to improve students' performance in 'high-failure rate' Mathematics courses? 1.3 Research Significance The following are the significance of this research to UiTM Sarawak, lecturers, students and the researchers. a) To UiTM Sarawak The findings of this research could serve as inputs for the administrators of UiTM Sarawak to plan and organize various academic programmes on Mathematics aiming to improve students’ performance on ‘high-failure rate’ Mathematics courses. b) To Lecturers and Students The findings of this research could provide the lecturers with necessary information to improve their teaching. As for students, the findings could serve as inputs of the need expected of them. The findings of this research could also provide the information that the students need to know to be successful in their academic performance. c) To the Researchers The findings of this research could serve as inputs for future studies on this area. The data collection and the methods used in this research would be very useful to anyone who is interested in exploring the related field of research. 1.4 Research Scope This research involved only full-time diploma students. Only four factors such as SPM Mathematics grades, SPM Additional Mathematics grades, size of Mathematics class and gender were taken into consideration due to time and resource constraints. Paper number: 2

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2. LITERATURE REVIEW

This section reviewed on some pertinent factors that influenced Mathematics achievement, and some strategies implemented by experienced and excellent Mathematics educators to improve Mathematics education. 2.1 Class Size Class size had been under research since the 1920s (Biddle & Berliner, 2002). The findings obtained from a large-scale study in Indiana in 1984 indicated that Mathematics achievement gained in class sizes of no more than 18 as compared to average class sizes of 23.8 (Gilman, Swan & Stone, 1988). The Tennessee’s Student-Teacher Achievement Ratio Project which was conducted in 1985 found that the average effect of small classes was positively significant in Mathematics at each grade level (Finn & Achilles, 1990). By then, class size reductions had been implemented extensively in California, Netherlands, New Zealand, China and Taiwan (Blatchford, Bassett, Catchpole, Edmonds, Goldstein, Martin & Moriaty, 2003). Some politicians and policy makers makers were in the view that class size did not matter, but it was believed that they were fearful of the cost implications. Eric Forth, the Minister of State for Education did not believe that there was any proven connection between class size and quality of education (Blatchford, 2003). Findings from several studies conducted by Hanushek (1999) suggested that students performed better in big classes. Hanushek had also argued against class size reduction, favoring cost effectiveness on teacher training (Rivkin, Hanushek & Kain, 2000). 2.2 Gender Gender equity and differences in Mathematics performance had been widely studied and documented (Manger & Gjestad, 1997; Forgasz & Leder, 1999; Kaiser, 2003; Wedege, 2007). Dossey, Mullis, Lindquist and Chambers (1988) indicated that male students performed better in geometry and measurement, while numbers and operations were better performed by female students. According to Li (2004), female students in grade seven tended to perceive geometry as tougher than male students. Casey, Nuttall and Pezaris (2001) suggested that it could be due to the fact that male students had better spatialmechanical skills. According to report from College Board (2006), the ratio of boy to girl, scoring between 750-800 points was 2.6:1 for the Scholastic Aptitude Test Examination in 2006. Parsons, Jacquelynn, Kaczala, Caroline and Meece (1982) reported that the lower Mathematics performance for females was due to differences in expectations for boys and girls, both from the parents and teachers. Traditionally, girls perceived Mathematics as a discipline dominated by boys (Paulsen, Karen & Johnson, 1983). However, Tsui (2007) reported on no gender differences in the overall Mathematics achievement in the 2002 College Entrance Examination in China. In Malaysia, the statistics obtained from the Malaysian Examination Syndicate for the year 2000 – 2004 showed that female students had outperformed their counterpart peers. This might carry an explanation to an underlying trend of gender differences in Mathematics achievement among UiTM Sarawak diploma students. 2.3 SPM Mathematics and SPM Additional Mathematics Grades Many researchers acknowledged the importance of basic Mathematics knowledge in learning and understanding of new Mathematics knowledge (Yudariah & Roselainy, 1997; Gynnild, Tyssedal & Lorentzen, 2005; Hailikari, Nevgi & Lindblom, 2007). Gynnild, et al. (2005) reported that lacked of basic skills and knowledge in Mathematics was one of the three major reasons for engineering students in Norwegian University of Science and Technology to fail their Calculus course. Concisely, most high achievers in secondary Mathematics education did well in Calculus. A series of studies by Saudah Hanafi (1996) and Yudariah and Roselainy (1997) on University of Technology Malaysia (UTM) students indicated that those who performed poorly in the first year Basic Mathematics and Basic Calculus examinations usually belong to the group that scored poorly in SPM Additional Mathematics or did not take the subject at all. Researchers from UTM implied that students having learned only SPM Paper number: 3

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Mathematics would not have sufficient mathematical background for learning advanced Mathematics at university level (Yudariah & Roselainy, 1997).

2.4 Strategies Taken For Students’ Improvement in Mathematics Learning Researchers and academicians everywhere around the world are implementing various strategies to address students’ poor performance in Mathematics. In Singapore, a group of researcher from Nanyang Technological University (Ahuja, Lim-Teo, Suat & Lee, 1998) had suggested improvements on curriculum and teaching strategies, use of technology, infusing thinking and creativity, and provision of training as solutions to improve Calculus and Mathematics education. Ponte (2007) reported of students’ development in Mathematical understanding through investigation and exploration tasks in the classroom by using a specific teaching unit which he had constructed. 3. METHODOLOGY

This section briefly describes the research design, population, research instruments, data collection as well as data analysis procedures. 3.1 Research Design This was an ex post facto research whereby the relationships and effects among the variables were studied as they occurred in a natural setting (Wiersma, 1995). In this research, the variables included students’ course marks of ‘high-failure rate’ Mathematics courses, students’ gender, SPM (Sijil (Sijil Pelajaran Malaysia) Malaysia) Mathematics grades, SPM Additional Mathematics grades and size of class. In the later part, this research studied on the lecturers' suggestions to improve students' performance in 'high-failure rate' Mathematics courses. 3.2 Population The population of the research consisted of all full-time diploma students in UiTM Sarawak who had taken the ‘high-failure rate’ Mathematics courses starting from semester January-May 2004 to semester January-May 2007. Besides this, the lecturers who had experiences in teaching ‘high-failure rate’ Mathematics courses were asked to give suggestions in improving students' performance in 'high-failure rate' Mathematics courses. 3.3 Research Instruments The research instruments comprised the report of final examination analysis for those students who had taken ‘high-failure rate’ Mathematics courses starting from semester January-May 2004 to semester January-May 2007; and students’ particulars as noted in the registration database; and also the open-ended questionnaire for lecturers. 3.4 Data Collection Procedure The reports of the final examination analysis for the ‘high-failure rate’ Mathematics courses from semester January-May 2004 to semester January-May 2007 were obtained from the respective Heads of Programmes and the Academic Affairs Division (HEA). From the report, information such as students’ course marks of ‘high-failure rate’ Mathematics courses and size of Mathematics class for the ‘highfailure rate’ Mathematics courses could be obtained. Next, the students’ particulars as noted in the university registration database were obtained from HEA. The particulars obtained included the students’ SPM Mathematics grades, SPM Additional Mathematics grades and students’ gender. An open-ended questionnaire was distributed to the lecturers who had taught the ‘high-failure rate’ Mathematics courses to gather their suggestions in improving students' performance in 'high-failure rate' Mathematics courses. 3.5 Data Analysis Procedure The data collected were analyzed by using Statistical Package for Social Sciences (SPSS). A bar chart was drawn to identify the ‘high-failure rate’ Mathematics courses. Pearson product-moment correlation Paper number: 4

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coefficients were calculated to identify correlations, if any, for the course marks of ‘high-failure rate’ Mathematics courses and the contributing factors. A multiple regression analysis was performed to determine the factors that contributed significantly to the variance in course marks. Step-wise discriminant analysis was performed to construct a predictive model that might predict the performance of students’ ‘high-failure rate’ Mathematics course marks (dependent variable) based on the contributing factors (the independent variables). The content analysis was also used to analyze the open-ended questions to categorize the suggestions given by the lecturers. 4. FINDINGS

This section reports the findings of the data analysis that was carried out to study ‘high-failure rate’ Mathematics courses and the factors affecting the course marks of the ‘high-failure rate’ Mathematics courses for diploma students in UiTM Sarawak. The results generated from each process were observed, recorded, and the overall impact on students’ academic performance was reported. In addition, the best model for each ‘high-failure rate’ Mathematics course was also predicted by using the step-wise regression method. The suggestions by the lecturers are enclosed at the e nd of this section. 4.1 ‘High-Failure Rate’ Mathematics Mathematics Courses In this research, a ‘high-failure rate’ Mathematics course was defined as a course offered in UiTM Sarawak which achieved an average passing rate below 70% for the past 7 semesters. In accordance to UiTM Malaysia academic policy, a written report was required by lecturer for any courses with passing rate below 70%. Data collected from the past 7 semesters were used, as the data for the semesters prior to these were inaccessible.

100 90

) % ( e g a t n e c r e P

80

68.66

70

66.46

66.43 58.47

60 50 40 30 20 10 0 MA T133

MAT183

MAT192

MA T293

Mathematics Course

Figure 1: Average passing rate from semester Jan-May 2004 to semester Jan-May 2007

On the basis of the definition declared above, four Mathematics courses were recognized as ‘highfailure rate’ courses, and they were MAT133, MAT183, MAT192 and MAT293 (refer Figure 1). While MAT133 and MAT183 respectively were taken by part 1 and part 2 students of Diploma in Science, MAT192 was taken by part 2 students of Diploma in Electrical Engineering and MAT293 was taken by part 6 students of Diploma in Civil Engineering. Overall, these courses were offered in science-based programs. Essentially, these ‘high-failure rate’ courses had a significant portion of Pre-Calculus and Basic Calculus.

Paper number: 5

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4.2 Mathematics Course Marks and the Contributing Factors Firstly, correlation analysis was carried out to investigate the impact of the contributing factors such as class size, SPM Mathematics grades and SPM Additional Mathematics grades on course marks of ‘high-failure rate’ Mathematics courses. Table 1 showed that significant positive correlation was found between course marks and SPM Mathematics grades across all ‘high-failure rate’ courses, which indicated that SPM Mathematics directly affected these ‘high-failure rate’ Mathematics courses. Similarly, a stronger positive correlation could also be observed between course marks and SPM Additional Mathematics, in which SPM Additional Mathematics significantly influenced students’ Mathematics performance at the university level. However, there was no significant correlation between the class size and the course marks of MAT133, MAT192 and MAT293 (p>.05). There was a positive but weak relationship between MAT183 course marks and the class size. The class size varies between 15 and 55. Table 1: Pearson Correlation between contributing factors and course marks of ‘high-failure rate’ Mathematics courses

MAT293 Course Mark

Pearson Correlation Sig. (2-tailed) N MAT192 Course Mark Pearson Correlation Sig. (2-tailed) N MAT183 Course Mark Pearson Correlation Sig. (2-tailed) N MAT133 Course Mark Pearson Correlation Sig. (2-tailed) N ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at at the 0.05 level (2-tailed). (2-tailed).

Class Size -.097 .116 267 .072 .255 254 .166(**) .012 228 .059 .304 301

SPM Math .124(*) .046 258 .163(**) .010 250 .199(**) .003 222 .386(**) .000 278

SPM Add Math .215(**) .001 253 .328(**) .000 244 .355(**) .000 222 .563(**) .000 277

Secondly, to compare the course marks of ‘high-failure rate’ Mathematics courses across gender, the mean marks between genders were determined. The overall mean course marks of ‘high-failure rate’ Mathematics courses for both male and female students varied between 46 and 56. In general, Diploma in Science was dominated by female students, and they performed better as compared to the male students. The mean course marks for MAT133 are 55.75 and 52.36 for female students and male students, respectively. Similarly, the mean course marks for MAT183 are higher for the female students (53.32) in comparison to the male students (51.48). Even though Diploma in Civil Engineering and Diploma in Electrical Engineering had more male students as compared to their counterparts, female students had shown higher mean Mathematics course marks for both MAT 192 and MAT293. MAT293 which was taken by part 6 students of Diploma in Civil Engineering had the largest disparity (4.30) in its mean course mark between the genders. Overall, female students tend to perform better than male students in the ‘highfailure rate’ Mathematics courses regardless of program. 4.3 Relationships between Mathematics Course Course Marks and the Contributing Factors In examining the relationship between the whole set of predictors (gender, Mathematics class size, SPM Mathematics grades and SPM Additional Mathematics grades) and the dependent variable (course mark), multiple regression was carried out. By using the standard model for regression, all the predictors were entered into the regression equation simultaneously. In the scatterplots of residuals an oval shape f or all the four ‘high-failure rate’ Mathematics courses was obtained, in which the rule of homoscedasticity was not violated. Hence, the assumption of constant variance was valid. Results obtained in Table 2 indicated that for MAT293, all the independent variables together explained 9.4% of the variance (R square) in its course mark. For MAT192 and MAT183, all the predictors accounted for 12.6% and 14.6% of the variation in course mark respectively. In the case of Paper number: 6

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MAT133, 34.3% of the total variation in course mark was attributed to the variation in the independent variables. Independence observations evaluated from the Durbin-Watson statistics displayed values between 1.5 and 2.5, consistent with the assumption of no autocorrelation in the residuals. Table 2: Statistics of Standard Regression Model MAT133 MAT183 MAT192 MAT293 Coefficient Beta t Beta t Beta t Beta t Math Class Size .101 2.030* .115 1.790 .019 .312 -.114 -1.879 SPM Modern Math .063 1.036 -.047 -.591 -.017 -.231 .077 1.000 SPM Add Math .529 8.842* .369 4.770* .354 4.763* .189 2.483* Gender (Code) -.109 -2.187* -.094 -1.472 -.133 -2.138* -.197 -3.178* F-statistic 35.576 9.246 8.619 6.399 Significance .000(a) .000(a) .000(a) .000(a) R Square .343 .146 .126 .094 Adjusted R Square .334 .130 .111 .079 Durbin-Watson 1.638 1.604 2.020 1.814 a Predictors: (Constant), Gender (Code), Math Math Class Size, SPM Add Math, SPM Modern Math b Dependent Variable: Variable: Course Mark * p < .05

This regression model was significant for all the four ‘high-failure rate’ Mathematics courses (p<.05). It was further indicated by the F-values i.e. F(4, 248) = 6.399 for MAT293; F(4, 239) = 8.619 for MAT192; F(4, 217) = 9.246 for MAT183 and F(4, 272) = 35.576 for MAT133. This showed that at least one of the suggested independent variables was a significant predictor to the course marks of ‘high-failure rate’ Mathematics courses. For MAT293 and MAT192, gender and SPM Additional Mathematics were significant predictors to its course mark, as indicated by the significant t-values (p<.05). SPM Additional Mathematics was the only significant predictor to MAT183 course mark, while MAT133 had three significant predictors to its course mark, namely the size of Mathematics class, gender and SPM Additional Mathematics. However, SPM Mathematics was a not a significant predictor to all the four ‘high-failure rate’ Mathematics course marks, as shown by the non-significant t-values (p>.5). SPM Additional Mathematics had the strongest influence on all the ‘high-failure rate’ Mathematics course marks except for MAT293, which was influenced more by gender. Observation on the t-values indicated that SPM Additional Mathematics was a high significant predictor in determining students’ course marks (p<.05). Our results also indicated that SPM Additional Mathematics had the strongest impact on the Mathematics course marks in Part 1 (MAT133), but seemed to diminish in higher parts. Obviously, the results underscored the importance of basic skills and knowledge in Additional Mathematics for Mathematics learning at the university level. Moreover, the statistics on the variable gender indicated that female students performed better than their male counterparts in all the ‘high-failure rate’ Mathematics courses. The tolerance values between .616 – .987 and variance inflation factor (VIF) values of 1.013 – 1.623 showed that the assumption of no collinearity was valid. The regression equation was written as: Course Mark = β 0+ β 1(Math Class Size)+ β 2[Gender(Code)]+ β 3(SPM Math)+ β 4(SPM Add Math)

wher e β i , i = 0,1,2,3,4 are the regression constants Further regression analysis was carried out using the stepwise method. Three models had been generated for MAT133 from the suggested four variables; one with SPM Additional Mathematics only, another with SPM Additional Mathematics and gender, and another with SPM Additional Mathematics, gender and class size. Only one model had been generated for MAT183 with SPM Additional Mathematics as the variable. Both MAT192 and MAT293 had two models generated from the four variables we suggested; one with SPM Additional Mathematics only and another with both SPM Additional Mathematics and gender, respectively. Paper number: 7

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The output in Table 3 indicated that about 34.1% of the variation in the MAT133 course mark could be explained by the regression model (Model 3) with three predictors i.e. gender, Mathematics class size and SPM Additional Mathematics. SPM Additional Mathematics Mathematics itself explains 12.6% to the variability in MAT183 course mark. In Model 2, both SPM Additional Mathematics and gender accounted for about 12.6% and 7.8% of the variance in the MAT192 course mark and MAT293 course mark, respectively. For all the ‘high-failure rate’ Mathematics courses, these findings were statistically significant (p<.05). For MAT133, F(3, 273) = 47.064; for MAT183, F(1, 220) = 31.785; for MAT192, F(2, 241) = 17.298; and for MAT293, F(2, 250) = 10.559. Table 3: Statistics of Best Regression Model MAT133 Coefficient Beta t Math Class Size .108 2.188 SPM Add Math .564 11.455 Gender (Code) -.118 -2.387 F-statistic 47.064 Significance .000 R Square .341 Adjusted R Square .334 Dependent Variable: Course Mark

MAT183 Beta t .355

5.638 31.785 .000 .126 .122

MAT192 Beta t .347 -.136

5.701 -2.241 17.298 .000 .126 .118

MAT293 Beta t .240 -.180

3.918 -2.930 10.559 .000 .078 .071

Subsequent results generated using the stepwise method showed the best regression model of only significant predictors (p<.05). MAT133 course mark was best predicted using Model 3 with three variables i.e. SPM Additional Mathematics, gender and class size. The best model in determining MAT183 course mark could be done by using only the variable SPM Additional Mathematics, while MAT192 and MAT293 had their best models predicted with two variables, namely SPM Additional Mathematics and gender. For MAT133, MAT183 and MAT192, with the exclusion of some variables to obtain the best models, SPM Additional Mathematics still remained as the most influential predictor in determining the course marks. However, for MAT293, with the exclusion of the variables class size and SPM Mathematics, SPM Additional Mathematics became more influential as compared to gender in determining its course mark. Overall, SPM Additional Mathematics had the strongest influence on course mark, while SPM Mathematics was not a useful variable to be included in any of the models. SPM Additional Mathematics was the best predictor. Hence, parsimony suggests that SPM Mathematics should not be included in the prediction of ‘high-failure rate’ Mathematics course marks. 4.4 Suggestions to Improve Students’ Performance in ‘High-Failure Rate’ Mathematics Courses Because students underachieved for so many different r easons, there is no single intervention strategy that can possibly reverse all these behaviors in all underachieving students. Nevertheless, both lecturers and students need to play their roles to improve students’ performance in ‘high-failure rate’ Mathematics courses. The following are some useful suggestions given by UiTM Sarawak Mathematics lecturers that can be practised by students, lecturers and the top management of UiTM. Students must develop regular study habits by attending lectures and tutorial as well as attempting to do assignments and exercises in order to master the knowledge. For ‘high-failure rate’ Mathematics courses, a thorough knowledge of the previous material is essential to reach an understanding of new material. Hence, falling behind tends to be c umulative and is one of the most frequent causes of failure, as understanding grows with time and experience. Therefore, students should play their part by studying hard and always make an effort to consult the lecturers concerned when they are having difficulty in learning ‘high-failure rate’ Mathematics courses. They should not delay in asking for assistance or prolong their problems until a day before the exam because it is quite impossible for them to cram Mathematics knowledge and concept at the very last minute. Learning ‘high-failure rate’ Mathematics courses involve development of skills and understanding that must be consolidated over a period of time. Paper number: 8

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Based on the experiences in teaching ‘high-failure rate’ Mathematics courses, most of the lecturers have encouraged the students to take the course during a less packed semester with no other killer subjects. This is to help the students to have sufficient time and energy to concentrate more on the subject. Students who are re-taking a particular subject should not try to load themselves with too many subjects in order to avoid any future failures. In order to improve students’ performance in ‘high-failure rate’ Mathematics courses, the lecturers’ teaching method must be properly sequenced and well-organized. The teaching approach must be effective and tally with the level of understanding of the students. The use of certain courseware and the implementation of new teaching and learning methods such as concept mapping and mind mapping can help students to visualize the abstract concept and enhance their understanding in the process of learning. The lecturers must be competent and show the ability to guide students in identifying the correct skills in answering various Calculus problems. Instead of simply giving them the solutions to mathematical problems, lecturers could train their students to actively work for alternative solutions which help them to think creatively. 5.

CONCLUSIONS AND RECOMMENDATIONS

In identifying the ‘high-failure rate’ Mathematics courses offered in UiTM Sarawak, the findings in this research showed that there were four Science-based courses that fell into this category namely MAT133, MAT183, MAT192 and MAT293. These courses had a significant portion of Pre-Calculus and Basic Calculus. Thus, students generally faced problems in understanding Calculus concepts. The difficulty in acquiring a good knowledge of Calculus was well documented from previous studies (Cipra, 1988; Madison and Hart, 1990; NCTM and MAA, 1987). Although SPM Mathematics grade was correlated to the course marks of ‘high-failure rate’ Mathematics courses, it was found to be insignificant in the regression model. This finding was supported by Yudariah and Roselainy (1997) which reported that students having learned only SPM Mathematics would not have sufficient mathematical background for learning advanced Mathematics at university level. SPM Additional Mathematics was a good predictor of the course marks of ‘high-failure rate’ Mathematics courses, so it was recommended that all future intakes of students into Science-based programs should have a strong grade in Additional Mathematics. SPM Additional Mathematics is taught to introduce Pre-Calculus to Science-based students as preparatory fundamentals to Mathematics learning in tertiary level. Class size had significant influence on MAT133 (Part 1 Mathematics course) but not the other three ‘high-failure rate’ Mathematics courses. This is due to the reason that students initially encountered transition problem from school to university, but as they progressed to the later semesters they have adjusted to the university culture and could cope better with large class. This research finding was similar to Gilman et al. (1988) who reported that size of class was the factor associated with students’ academic performance. Gender plays a significant role in determining the course marks. This finding was consistent with many of the research which had been documented (Manger & Gjestad, 1997; Forgasz & Leder, 1999; Kaiser, 2003; Wedege, 2007; Brandell, Leder & Nystrom, 2007). Generally, this research finding showed that female students outperformed male students in all the four courses. This research finding was different from Tsui (2007) who reported that no gender differences were found in overall Mathematics achievement among 1,078 high-school seniors on the 2002 College Entrance Examination in China. Our results indicated that SPM Additional Mathematics had the strongest impact on the ‘high-failure rate’ Mathematics courses marks in Part 1 (MAT133), but seemed to diminish as the students progressed to higher parts. This is is partly due to the fact that students on entering university are directed gradually to more specialized areas of Mathematics. Generally, this finding was parallel to several documented findings reported by Garton, Dyer, King and Ball (2000), Murtaugh, Burns and Schuster (1999), and Wold and Worth (1991) which identified high school grade point average as a predictor for students’ first year academic performance in tertiary education. Paper number: 9

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In order to help students to succeed in ‘high-failure rate’ Mathematics courses, the lecturers and the students should cooperate to overcome the arising problems which might prevent the students to succeed. The lecturers should try their best to educate the students, and in return, the students should study hard to thank the contributions of their lecturers. To encourage students to practise their preferred learning approach and not just rely heavily on lecture notes, an environment that is conducive for learning needs to be implemented by the lecturers. Further, to increase the students’ understanding on Mathematics ideas, lecturers should put in effort to increase the usage of concrete materials in the teaching and learning of Mathematics and Calculus. The suggestions given by the lecturers generally were parallel with Ahuja, et al. (1998), which suggested that improvements on curriculum and teaching strategies, use of technology, infusing thinking and creativity, and provision of training could be the solutions to improve Calculus and Mathematics education. Future research can be carried out for a larger sample size; that is, it can cover the students’ academic performance of ‘high-failure rate’ Mathematics courses at least for the past ten semesters. The future research can also look into the trend analysis of the students’ future performance of ‘high-failure rate’ Mathematics courses. The future research can also investigate on all types of students, which consist of full-time diploma students, part-time diploma students, the degree students, the master students and also the PhD (Doctor of Philosophy) students. Additionally, consideration should also be given to investigate the factors which are not studied in this research due to the design, the time and resource constraints. ACKNOWLEDGEMENT

Sincere appreciation and thanks are conveyed to Mr. Foo Kien Kheng, Mr. Lau Sie Hoe and Madam Ling Siew Eng for their assistance in the successful completion of this research project. REFERENCES

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A Case Study of 'High-Failure Rate' Mathematics Courses and its’ Contributing Factors on UiTM Sarawak Diploma Students

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