Factors Impacting General Mathematics Success for first-year Business’ students:

1. ICES 3° International Conference on Educational Sciences 2014 TIRANA ALBANIA, April 24-25, 2014 INTRODUCTION Quality of education improves the quality of human resources and is directly related to increased individual earnings and productivity, and economic growth. Educational quality is typically measured by higher achievement in examinations. In the learning process significant influence have families, communities and peers as well as formal institutions; and social, economic and cultural factors influence the extent to which family members support children’s classroom learning. Determinants of student performance have attracted the attention of academic researchers from many areas. They have tried to determine which variables impact student performance in positive and negative direction. Determining the factors that affect the student performance is important, because, primarily institutions and lecturers have to find out ways to increase student performance, and to motivate students for better performance. In order to do this, first they need to determine which factors play significant role in student performance. Socio-demographic factors such as age, income, gender, psychological factors such as motivation, stress, study strategies, and other factors like study hours and understanding the language of instruction are among the factors that could play substantial roles in students’ academic success. A number of studies have shown that there is a strong link between mathematical background and performance in Economics and Finance units, and hence overall performance in the degree. Lagerlof and Seltzer (2009) found that, consistent with previous studies, the level of and performance in secondary school mathematics has strong predictive power on students' performance at university level economics.  Butler et al. (1994) studied the effect of Calculus on learning intermediate microeconomics and macroeconomics and found a positive and significant association between intermediate microeconomics, but failed to establish the relationship with macroeconomics. Mallik and Lodewijks (2010) found that higher level mathematics (with calculus) and economics in high school can increase the marks in first year introductory economics subject significantly. The purpose of this study was to identify and analyse the factors that influence academic performance in General Mathematics course for first-year business students. This study contributes towards unraveling those significant determinants of students’ performance that need to be addressed. The findings of this study may also be applicable to related courses (those that require the application of Mathematics knowledge) with high failure rates. Factors Impacting General Mathematics Success for first-year Business’ students: Evidence from Albania Alma Spaho University of Tirana, Albania E-mail: alma.spaho@unitir.edu.al RESULTS Majority of the students in the sample were females (89%), 19 years old (83%), have attended an urban high school (91%) and a public high school (89%). Around 38% of students spend more than 5 hours every week using computer, mostly for projects, home-works, and social networks. Only 14% of them spend time working during the week and 78% of them had average grade in mathematics in high school more than 8. Around 46% of the students had family monthly income more than 90,000 leks; mother education level was high school for 43% of the sample, whereas father education level professional high school for 35% of the students. About 60% of the students did not take a passing grade in General Mathematics course. Logistic regression model was estimated to identify the variables associated to success in the General Mathematics course in the sample. Pearson correlation coeficients between independent variables were lower that 0.57. Note: * p-value < 5%, ** p-value < 10%. Results of the logistic regression model indicated that the variables lecture attendance and average grade in mathematics in high school were statistically significant at 5% level. The other variables, study hours and working during the semester were significant at 10% level. The variable, average monthly family income was significant at 10% level, that is students with average family monthly income more than 60,000 leks had less chances (OR = 0.52) to pass the final exam. ABSTRACT One of the benefits of good education is that it enables individuals to contribute to the development and improvement in the quality of their life, their communities and the nation as a whole. Students’ achievements in mathematics in high school are prerequisites for admission into university and have a significant effect on their academic performance in university. In addition, mathematical and quantitative skills create possibility for better chances for employment, higher wages and higher productivity on job. Thus, mathematics learning and students’ performance in mathematics receive considerable attention from educators, teachers and parents and is important to identify the factors that could influence students’ mathematics achievement to help them improve and make substantial academic progress. This study aims to investigate factors associated with business student’ success in General Mathematics course. The population of the study consists of business students attending first-year at the Faculty of Economy, at University of Tirana for 2012/2013 academic year. Using the simple random sampling, 143 business students were randomly selected and completed a questionnaire during the first week of June 2013. The questionnaire included questions about student characteristics, family characteristics and high school characteristics. The binary logistic regression was used to estimate the impact of lecture attendance, study hours, working status, average grade in mathematics in high school, father education level, mother education level and monthly family income on final examination success. The results indicated that lecture attendance, average grade in mathematics in high school, study hours and working status were statistically significant variables that influenced their final success in General Mathematics course. These findings suggest that, primarily, institution and lecturers must find out ways to increase student performance, and to motivate students for better performance. Enhancing student participation should be a crucial aspect of administration, in order to improve performance. At the same time, the lecturer should also create a good learning environment, to motivate students and engage their interest about the course. DISCUSSION • The data analysis of the study highlighted significant linear association between lecture attendance, average grade in mathematics in high school, study hours during the weeks, working during the semester and family monthly income and final success of General Mathematics course for first-year business students. • The attendance of lecture was positively associated with student success in the General Mathematics course. A number of studies have examined the relationship between students' attendance and academic performance, generally finding that attendance impact significantly the academic achievement (Cohen and Johnson, 2006; Kirby and McElroy, 2003). • The number of study hours per week during the semester was positively and significant associated with the success. This finding was in support to the study of Erdem et al (2007). Unlikely previous studies of, Hijazi and Naqvi (2006) that found that study hours had negative impact on student performance, and Kara et al (2009)that found that the number of study hours per week was not significant. • Working during the semester was positively related to final success, this might be due to the fact that working students take more responsibilities than other students, so they look at education as seriously. This finding was consistent with the studies of McInnes and Hartley (2002) and inconsistent with the findings of Kara et al., 2009. • High school grade point average is also important as predictors of performance at other levels of education (Kuncel et al, 2005). According to the study of Anderson et al. (1994), students who received better scores in high school also performed better in college. The average grade in mathematics in high school was also significant and positively associated with student success in the General Mathematics course. This finding was consistent with the studies of Uyar and Güngörmüş (2011), and Olaitan (2012). • Family monthly income was found negatively significant in this study. This finding was inconsistent with the study of Goldhaber and Brewer (1997) that found a positive relationship between family income and maths achievement. METHODS • The study population consists of business students in the 2012-13 academic year attending first-year at the Faculty of Economy, at University of Tirana. Using the simple random sampling, 143 students (N = 450) were randomly selected and completed a survey at the last week of the second semester. The questionnaire included personal questions about age, gender, number of hours per week using computer, current working status; questions about general mathematics course such as lecture attendance, study hours; questions about high school such as average grade in mathematics, residence, and type; and family characteristics such as monthly income and parents’ education level. The outcome variable was the grade in General Mathematics course, which was obtained from the lecturers of the course. STATA was used to perform logistic regression analysis. • A logistic regression model with a dichotomous response of success or failure was modeled. Success was defined as earning a final grade of 5 or higher in the mathematics course. Students who received a final grade of 4 were considered to have been unsuccessful. For the analysis, the response was coded as 1 or 0, respectively. The independent variables of the logistic regression model were dummy variables that indicated the characteristics of the students: lecture attendance coded 1 if the student attended more than ten lectures (weeks) and 0 otherwise; study hours coded 1 if a student spent averagely more than 5 hours learning mathematics during the weeks of the semester and 0 otherwise, working during the semester coded 1 if the student worked and 0 otherwise, and average grade in mathematics in high school coded 1 if the average grade was more than 8 and 0 otherwise. Other independent variables were dummy variables that indicated family characteristics such as monthly income coded as 1 if the monthly income of the family was more than 60,000 leks and 0 otherwise; father education coded 1 if the father education level was university or more and 0 otherwise; and mother education coded 1 if the mother education level was university or master and 0 otherwise. • Logistic regression is recommended over linear regression when modeling dichotomous responses and allows the researcher to estimate probabilities of the response occurring (Hosmer and Lemeshow, 2004). The logistic regression equation takes the following form • ln(p/1-p) = β0 + β1x1 + β2x2 +… + βkxk • where p is the estimated probability of success, and x1, x2, …, xk are independent variables. • The estimated probability of the response occurring or success (p) divided by the probability of it not occurring or not success (1-p) is called the odds ratio. Maximum likelihood method is used to estimate the odds ratios of the model. Values of odds ratios higher than 1 indicate positive association between the variables, odds ratios equal to 1 indicate no association, while odds ratios lower than 1 indicate negative association between each independent variable and the dependent variable of the model. LITERATURE REVIEW • Many studies have been carried out to identify and analyse the numerous factors that affect academic performance in various centers of learning. Their findings identify students’ effort, previous schooling, parents’ education, family income, self motivation, age of student, learning preferences, class attendance and entry qualifications as factors that have a significant effect on the students’ academic performance in various settings (Mlambo, 2011). • According to Uyar and Güngörmüş (2011), grade point average, high school type, age of the student and attendance were significant variables which influence student performance. Grade point average, high school type, attendance had positive influence on student performance, whereas age of the student had negative influence. In their study, Hijazi and Naqvi (2006) found that class attendance; mother education and study hours had significant impact on student performance. Class attendance and mother education had positive impact on student performance, while study hours had negative impact. Olaitan (2012) in his study found significant association between high school results and student performance, and between family income and student performance. Among the factors affecting students’ grades according to Kara et al (2009) significant factors were gender, SAT score, number of hours worked, and number of missed classes, while the number of hours per week spent on studying for the class was not significant. Moreover, gender and SAT score had positive effect on students’ grades, while the effect of number of missed classes and number of hours per week worked at a job was negative. The findings of Erdem et al., (2007) indicated that, the type of high school, residence of the family, number of sisters/brothers in school, and weekly study hours were statistically significant and positively related to the cumulative grade point average. The variables gender, father education and mother education were statistically significant and negatively related to student performance. CONCLUSION The educational performance of the students is one of the main factors of their success in the future. Thus, focusing on the effective factors in students’ success is one of the vital factors in higher education system and this is the reason why many research papers have been published about these factors. One of the important factors that have been frequently referred is the essential differences among the students’ personal characteristics which are effective in their efficiency in learning. The results of logistic regression models indicated significant linear association between lecture attendance, average grade in mathematics in high school, study hours during the weeks, working during the semester and family monthly income and final success of General Mathematics course for first-year business students. At the beginning of the semester, students should be informed of the empirical relationship between class attendance, study hours, working status and performance. Based on these findings enhancing student participation should be a crucial aspect of administration, in order to improve performance. Classes scheduled between 10 a.m. and 3 p.m. have better attendance, thus scheduling classes, particularly core or required courses, during these hours is strongly recommended (Devadoss and FoltzSource, 1996). To encourage attendance, instructors may assign a certain percentage of the total marks to lecture attendance. To encourage the study of the subject during the semester, instructors may assign a certain percentage of the total marks to home-works, quizzes and projects. Excessive financial stress and working odd jobs affect students' academic performance; therefore, students need to be counseled to attempt to find work related to their studies or to seek financial aid to alleviate their financial burden (Devadoss and FoltzSource, 1996). These findings have important contribution to the university administrations, policy makers, students and students’ families in terms of providing them which factors have impact on student success. Also, the lecturers should create a good learning environment, to motivate students and engage their interest in the course. This study examined the impact of some factors on the student success. Other factors such as student self-motivation, stress, entry qualifications, study strategies, instructor and understanding the language of instruction was not examined. In future research, these factors can be examined.