European Journal of Education Studies ISSN: 2501 - 1111 (on-line) ISSN-L: 2501 - 1111 (print) Available on-line at: www.oapub.org/edu 10.5281/zenodo.51451 Volume 1│Issue 3│2016 REFRAMING NON-ATTENDANCE OF STUDENTS: CASE STUDY OF A MALAYSIA PRIVATE UNIVERSITY Sing Ong Yu Associate Professor, Acting Dean, Faculty of Business and Management, Southern University College, Malaysia Abstract: This paper examined the impact of attendance on student achievement. An analysis of students taking Statistics course in the third semester of 2015 was carried out to investigate the relationship between class attendance and their final exam grades. Students’ achievements were affected by absenteeism from class. The study showed that students who missed classes regularly performed poorly in their final exam. There is a strong correlation between absenteeism rate and failure rate. Students who recorded more than 20% absentee rate in class failed their final exam. This paper also suggests that we look at Reframing as a technique to reduce the non-attendance and to improve students’ achievements. Keywords: reframing, absenteeism, failure rate, attendance Introduction The delivery of material in a didactic lecture format is prevalent in almost all university courses. This approach may not be effective to some students as they have different learning styles. The concept of Cognitive Reframing involves changing the person’s mental perspective which leads to more positive change. Lecturers and counsellors should help students with poor attendance to identify and restructure their negative and irrational thoughts by replacing them with more realistic and factual information in order to interact well with their learning environment. One way to reduce the absenteeism rate and to improve student learning is implementing a progressive assessment strategy which consists of a series of assignments, quizzes, tests and presentations, followed by a final exam which carries a Copyright © The Author(s). All Rights Reserved Published by Open Access Publishing Group ©2015. 11 Sing Ong Yu – REFRAMING NON-ATTENDANCE OF STUDENTS: CASE STUDY OF A MALAYSIA PRIVATE UNIVERSITY lower weightage of the total scores. This strategy will enhance perceived course quality and promotes consistent study habits of students. University leaders need to relook at strategies to increase student engagement. There has to be a good instructional match between academic tasks and student abilities. The use of media and technology should be encouraged as some students learn better using technology compared to traditional classroom lecture. Lecturers need to be trained on teaching pedagogies so that they are able to engage students better and to help them see the connections between university life and real world events. By incorporating the skills for learning, understanding and reasoning, lecturers can improve the cognitive abilities of their students. Many research studies have shown the close correlation between class attendance and student characteristics. These characteristics include personal discipline, academic motivation, self-evaluation and cognitive ability. Class attendance is a manifestation of student motivation and abilities. Our proposed framework on improvement in student achievement showed that student performance in terms of grade outcome is influenced by internal factors (personal characteristics and motivation), external factors (reframing efforts of lecturers and counsellors) and class attendance. (Figure 1) Internal factors: Personal characteristics Motivation Student achievement: Grades External factors: Reframing efforts Class attendance Figure 1: Proposed framework on improvement in student achievement European Journal of Education Studies - Volume 1 │ Issue 3 │ 2016 12 Sing Ong Yu – REFRAMING NON-ATTENDANCE OF STUDENTS: CASE STUDY OF A MALAYSIA PRIVATE UNIVERSITY Literature Review Cognitive counselling regards thinking errors as the cause for emotional upsets and inappropriate behaviours. Cognitive reframing is a way that we can change our perceptions of stressors and to reduce significant amounts of stress to create a more positive life. It involves changing one’s emotion and replacing it with a better one (Jackman and Strobel, 2003). Kearney and Bates (2001) define school refusal behaviour as the refusal to attend school for an entire day by a child. King and Bernstein (2001) define school refusal as difficulty attending school associated with emotional distress, specifically anxiety and depression. Lauchlan (2003) noted that the problem of school non-attendance is heterogeneous and we should not make unnecessary distinctions when addressing the problem. The problem of school absenteeism has many negative implications for students who do not attend school regularly. These include poor performance in school, expulsions and dropouts (Petrides, et al., 2005). Factors which have identified as causal or correlated to non-attendance include school culture, school environment, poor relationship with teachers and other students, and dissatisfaction with school (CorvilleSmith et al., 1998). Higher average school attendance has been associated with higher performance (Roby, 2004). Jones (1984) concluded a negative correlation between absences and grades whereby absenteeism rate correlated with low grades. Instructors’ efficacy plays a large role in course attendance Romer, 993 . Hansen (1990) found that class attendance was higher when instructors offered a grade point bonus compared to those who did not offer such an incentive. They pointed to the effective use of incentives as a motivator for students to attend classes. Attendance feedback is one technique that could be used to improve student attendance (Gaudine and Saks, 2001). They noted that students attendance improve after receipt of a feedback letter comparing the students absence rate with other students in class. Davadoss and Foltz (19996) concluded that motivation has a strong impact on attendance rate. However, it has been difficult to determine if attendance rates should be treated as endogenous indicators of inherent motivation or they should be regarded as exogenous indicators. Grump (2004) reported that the highest motivator for attendance was interesting instructor and lecture materials. This conclusion was also supported by Fjortoft (2005) who noted that teaching effectiveness has an effect on class attendance. European Journal of Education Studies - Volume 1 │ Issue 3 │ 2016 13 Sing Ong Yu – REFRAMING NON-ATTENDANCE OF STUDENTS: CASE STUDY OF A MALAYSIA PRIVATE UNIVERSITY Research Questions 1) Is there a statistically significant difference in student attendance and student achievement? 2) Is there a statistically significant difference in student achievement between students with low absenteeism rate and students with high absenteeism rate? Methodology The study was carried out on a class of students enrolled in Statistics in the first semester of 2016 at a Malaysia private university. The sample size consisted of 88 students enrolled at the diploma level. To analyse the relationship between student achievement and attendance, the ANOVA statistics was utilized. An analysis was carried out to examine how absentee rate (independent variables) may affect exam grades (the dependent variable). We have 4 categories of ordinal variables, namely No absentee rate (denoted by 0), Low absentee rate (denoted by 1), Intermediate absentee rate (denoted by 2), and High absentee rate (denoted by 3). Low absentee rate is defined as those absent for 3 hours or less, intermediate absentee rate as those absent between 4 to 6 hours, and high absentee rate as those absent for more than 6 hours. A significance test was performed to decide if there is any or no evidence to suggest that liner correlation is found in the population. We test the null hypothesis, Ho, that there is no monotonic correlation in the population against the alternative hypothesis, H1, that there is monotonic correlation. Let ρs be Spearman’s population correlation coefficient, and we can express the test as: Ho: ρs = H: ρs ≠ no monotonic correlation present monotonic correlation present Data Analysis and Results There was a mean difference in the grades between the different groups. Students who attended all classes had a mean grade of 59.04. Students with low absentee rate had a mean grade of 56.7, followed by medium absentee rate with a mean grade of 52.06 and a high absentee rate with a mean grade of 39.4. The results showed that students who had high absentee rate failed their final exam as the passing score was set at 50 points (Table 1). The output of the ANOVA analysis showed that we have a statistically significant difference between our group means. The significance level of 0.002 is below European Journal of Education Studies - Volume 1 │ Issue 3 │ 2016 14 Sing Ong Yu – REFRAMING NON-ATTENDANCE OF STUDENTS: CASE STUDY OF A MALAYSIA PRIVATE UNIVERSITY 0.05, and therefore, there was a statistically significant difference in the mean score between the groups of students with different attendance levels (Table 2). The Multiple Comparisons table showed which groups differed from each other. There was a significant difference in the exam grades between students who attended all classes and the students with high absentee rate (p=0.001). There was also significant difference in the students with low absentee rate and those with high absentee rate (p=0.011). However, there were no significant difference between the groups with low absentee rate and the group with intermediate absentee rate (p=0.799). There was also no significant differences between the groups with medium absentee rate and the group with high absentee rate (p=0.146) (Table 3). The Spearman’s correlation was used to measure the ordinal scale of attendance. The results presented a Spearman’s correlation coefficient of negative 0.375 indicating a fairly weak negative correlation between attendance and grades. The p-value for this test was reported as .000, indicating that we have strong evidence to reject the null hypothesis, Ho, in favour of the alternative hypothesis, H1, i.e. attendance and grades are monotonically correlated (Table 4). Discussions The first research question examined the significance of the relationship between student attendance and student achievement. The results revealed a fairly weak negative correlation between attendance and exam grades. This suggests that attendance alone does not determine good grades and other factors such as motivation and self-discipline may play a role. By simply focussing on attendance does not provide the solution to poor academic performance. The second research question examined the significance of the relationship between students with low absentee rate and those with high absentee rate. The result showed that students who have low absentee rate performed better than students with high absentee rate. While attendance is compulsory, having a graded attendance policy may serve as a motivator for improving class attendance. This approach, together with other continuous assessment methods may facilitate classroom learning and reduce absenteeism. Class attendance alone does not contribute to good grades. Both students and lecturers have to be engaged to ensure effective student learning takes place. Students have to take responsibility for their own learning while lecturers are obligated to provide informative materials to keep the students engaged. Lecturers must make students cognizant of the importance of regular attendance. As such, pedagogical European Journal of Education Studies - Volume 1 │ Issue 3 │ 2016 15 Sing Ong Yu – REFRAMING NON-ATTENDANCE OF STUDENTS: CASE STUDY OF A MALAYSIA PRIVATE UNIVERSITY approached to education should shift from teacher-centred approach to student-centred approach where active engagement between teacher and student takes place. Conclusion This research study suggests that there is a correlation between attendance rate and exam scores, although the correlation is relatively weak. University lecturers and counsellors need to work closely with disengaged students to improve their attendance. This includes talking to students and their families about the students’ problems, reframing non-attendance, and reengaging them to address the absenteeism issue. Giving students’ feedback in the form of letters informing them of their poor attendance could be a technique to reduce absenteeism. This feedback letter also serves as a form of warning letter informing students that they would be barred from taking exams if their attendance falls below a certain level. The development of pro-social attitudes and behaviours as well as improving self-discipline will lead the way to improved academic performance of students. However, high-risk students who have no interest in learning will not maintain good attendance despite the efforts of lecturers. More research needs to be done to understand the forces influencing good attendance. With the information, universities can continue to implement policies and practices needed to reduce absenteeism and improve student achievement. Limitations of the Study This study has several limitations. The sampling process was carried out in one class of students taking Statistics and more research needs to be conducted to see if the findings are applicable to students enrolled in other programs. It also did not differentiate students who may require some form of incentive to motivate them to attend class from those who are intrinsically motivated to attend class regularly. Further research on the contribution of student attitude and self-discipline will provide additional insights on improving student achievement. References 1. Corville-Smith, J., Ryan, B.A., Adams, G.R., & Dalicandro, T. (1998). Distinguishing absentee students from regular attenders: The combined influence of personal, family and school factors. Journal of Youth and Adolescence, 27, pp: 629-640. European Journal of Education Studies - Volume 1 │ Issue 3 │ 2016 16 Sing Ong Yu – REFRAMING NON-ATTENDANCE OF STUDENTS: CASE STUDY OF A MALAYSIA PRIVATE UNIVERSITY 2. Devadoss, S. and Foltz, J. (1996). Evaluation of Factors Influencing Student Class Attendance and Performance. American Journal of Agricultural Economics, 78, pp:499-507 Fjortoft, N. (2005). Students' Motivations for Class Attendance. American Journal of Pharmaceutical Education, 69(1-5), pp. 107. 3. Gaudine, A. & Saks, A. (2001). Effects of an absenteeism feedback intervention on employee absenteeism. Journal of Organizational Behaviour, 22(1), pp:15-29. GUMP, S.E. (2004). Keep students coming by keeping them interested: Motivators for class attendance. Journal article by Steven E.Gump; College Student Journal, 38. 4. Hansen, T.L. (1990). A positive reinforcement program for controlling student absenteeism. College Student Journal, 24, pp:307-312 5. Jackman, J., & Strobel, M. (2003). Fear of feedback. Harvard Business Review, 81, 101-107 6. Jones, C.H. (1984). Interaction of absences and grades in a college course. The Journal of Psychology, 116, pp:133-136 7. Kearney, C.A., & Bates, M. (2005). Addressing school refusal behavior: Suggestions for frontline professionals. Children & Schools, 27(4), pp:208-216 8. King, N.J., & Bernstein, G.A. (2001). School refusal in children and adolescents: A review of the past 10 years. Journal of the American Academy of Child and Adolescent Psychiatry, 40(2), pp:197-205 9. Lauchlan, F. (2003). Responding to chronic non-attendance: A review of intervention approaches. Educational Psychology in Practice, 19(2), pp:133-146. 10. Petrides, K.V., Chamorro-Premuzic, T., Frederickson, N., & Furnham, A. (2005). Explaining individual differences in scholastic behavior and achievement. British Journal of Educational Psychology, 75, pp: 239-255. 11. Roby, D.E. (2004). Research on School Attendance and Student Achievement: A Study of Ohio Schools, Educational Research Quarterly, 28, pp:3-16. 12. Romer, D. (1993). Do Students Go to Class? Should They? Journal of Economic Perspectives 7, pp:167-174 European Journal of Education Studies - Volume 1 │ Issue 3 │ 2016 17 Sing Ong Yu – REFRAMING NON-ATTENDANCE OF STUDENTS: CASE STUDY OF A MALAYSIA PRIVATE UNIVERSITY TABLES Table 1: Descriptives Grades 95% Confidence Interval for Mean N Mean Std. Deviation Std. Error Lower Bound Upper Bound Minimum Maximum 0 35 59.0429 13.78958 2.33086 54.3060 63.7797 25.00 81.50 1 24 56.7083 19.56201 3.99308 48.4480 64.9686 15.50 92.00 2 16 52.0625 10.72206 2.68051 46.3491 57.7759 32.00 72.50 3 13 39.3846 18.10865 5.02243 28.4417 50.3276 .00 56.50 Total 88 54.2330 16.89197 1.80069 50.6539 57.8120 .00 92.00 Table 2: ANOVA Grades Sum of Squares Between Groups df Mean Square 3898.316 3 1299.439 Within Groups 20926.158 84 249.121 Total 24824.474 87 European Journal of Education Studies - Volume 1 │ Issue 3 │ 2016 F Sig. 5.216 .002 18 Sing Ong Yu – REFRAMING NON-ATTENDANCE OF STUDENTS: CASE STUDY OF A MALAYSIA PRIVATE UNIVERSITY Table 3: Multiple Comparisons Grades Tukey HSD (I) Attendance (J) Attendance (I-J) 0 dimension3 1 dimension3 95% Confidence Interval Mean Difference Std. Error Sig. Lower Bound Upper Bound 1 2.33452 4.18303 .944 -8.6301 13.2992 2 6.98036 4.76317 .463 -5.5049 19.4656 3 19.65824 * 5.12649 .001 6.2206 33.0959 0 -2.33452 4.18303 .944 -13.2992 8.6301 2 4.64583 5.09412 .799 -8.7070 17.9986 3 17.32372 * 5.43536 .011 3.0765 31.5710 0 -6.98036 4.76317 .463 -19.4656 5.5049 1 -4.64583 5.09412 .799 -17.9986 8.7070 3 12.67788 5.89349 .146 -2.7702 28.1260 0 -19.65824 * 5.12649 .001 -33.0959 -6.2206 1 -17.32372 * 5.43536 .011 -31.5710 -3.0765 2 -12.67788 5.89349 .146 -28.1260 2.7702 dimension2 2 dimension3 3 dimension3 *. The mean difference is significant at the 0.05 level. Table 4: Correlations Grades Spearman's rho Grades Correlation Coefficient Attendance 1.000 Sig. (2-tailed) N Attendance Correlation Coefficient Sig. (2-tailed) N -.375 . .000 88 88 ** 1.000 .000 . 88 88 -.375 **. Correlation is significant at the 0.01 level (2-tailed). European Journal of Education Studies - Volume 1 │ Issue 3 │ 2016 ** 19