Executive Summary

The main purpose of this study was to focus on determining the relationship between student marks among three semesters. The average students’ score on 1^st semester is 70 ± 14.9, the average students score on 2^nd semester is 53 ± 9.6 and the average students’ score on 3^rd semester is 44 ± 8. In overall view the results of the different semesters were analyzed and it was observed that the mean mark of the semesters was reducing from the 1^st to the 3^rd semester with mean marks of 70, 53 and 44 respectively. This trend was witnessed in all measures of dispersal including the median and the mode which had 68.11, 49, 44 and 59.71, 42, 43 for the 1^st, 2^nd and the 3^rd semesters respectively. This results can be attributed the students losing academic interest as the year and the semesters elopes, this can be due increased gaming, internet activities and other non-academic activities. Therefore the school management is expected to tackle these problems by bringing in necessary actions that needs to be undertaken.

In the 21^st Century, there has been quite an overwhelming acknowledgement of the value of individual expertise and human resource due to a widespread realization that these aspects have turned into the main drivers of economic affluence and societal well-being. In modern-day knowledge-driven civilizations, personal and communal progress is mainly driven by advancements in the technological sector hence, the need for total economic success calls for nations to preserve their competitive edge in the global economic arena by creating and maintaining a highly trained workforce, sustaining a globally competitive research base and refining their knowledge sharing techniques in order to benefit all individuals.

Consequently, as reported by Ewell and Van Vaught (2010), higher education, embodies a critical aspect in human resource development and overall innovation and plays a key role in the prosperity and sustainability of the knowledge-driven economies such as Australia. In Australia for example, higher education has become one of the most important aspects among the gazetted national agendas and has consequently undergone intense changes all of which are geared towards improving the quality of higher education in Australia. Dawkins (2014) argued that there is need for the targets and scales of success in the higher education sector of Australia to be recalibrated in a manner that they would be able to accurately reflect the value addition that higher education is meant to cultivate on Students.  

Currently in Australia, the students’ overall performance academic wise has become one of the key concerns in the tertiary level education sector. A wide variety of studies have been conducted in recent years with the aim of probing the key aspects related to the overall academic-based achievements of university students. Most of the studies carried out recently ventured into the exploration of aspects that influence the ultimate academic excellence of university students. Bone and Reid (2013) recounted that, the overall academic performance of any student is affected by a number of factors namely; gender, individual learning abilities, cost of higher education and race.  These aspects have a big impact on the overall academic performance of students hence the reasons why there is need to study the students’ academic performance. This study will seek to understand the important differences in students’ performance across gender, campuses, trimesters and Student Status so as to enable the Business School to come up with policies geared towards improving student performances in the subject of Management Accounting. According to AlphaBeta (2018), we currently live in a world, in which a major percentage of employment creation and growth is in areas aligned to acquired knowledge, skills and competences produced by the higher education sector. To assist the Business School in making the difficult decisions ahead, they shall have full access to the findings and recommendations of this study.

A demand driven system of higher education was first introduced in Australia in 2012 and since then, participation in higher education has increased dramatically. The Business School therefore needs to pursue a reform agenda that comes up with a new approach that ensures that higher education services respond to the increasing diversity of Students mainly in the field of Management Accounting which is the main area of concern in this Study. The Business School needs to develop an all-inclusive, clear and consistent learning system that facilitates maximum improvement of the academic performance of its students. This means, seeking an in-depth view of its learning systems, education involvement trends and doing their best to ensure that the all students taking the subject of management accounting achieve the acceptable results, knowledge and skills that meet future workforce requirements.

Statistical Problem 

In order to be able generate a set of meaningful findings, there is need to understand the specific aspects that the study needs to address. Having understood the goal of the study I came up with a set of Statistical Problems.

• First, I need to establish whether there is any significant difference in the average marks of the students between different trimesters and if yes, the causative agents of the said differences.
• Secondly, since there is a claim that gender is one of the key determinants of academic performance, I need establish whether there is any difference in the mean scores of the female students when compared to the mean scores of the male students. If there is any difference, there is need to test whether the difference across the two groups is significant.

I need to consult a wide range of individuals including students of different genders, lecturers, subject representatives and other people in the department of management accounting in order to obtain the relevant data and information necessary for the study to be successful.  

 

Business Problem

The challenge in this study is that there was no possible scenario where the statistical analysis

could be done in a cost effective manner. Despite the fact that the Business School’s

administration has tried to integrate the use of technology across various departments, with an

aim of easing the process of sharing information, the lack of harmony between the school’s

research department and the academic department, and the lack of coherence between these

departments, if anything, made the study more expensive because it brought about the need to

higher research assistants to aid in the collection of the relevant data, which would have

easily been made available by the academic department. This challenge is elevated by the fact

that the Business School does not have in its current budget the kind of funding that is

required to carry out such studies. Furthermore, the School’s overall expenditure on research

Studies have declined at an alarming rate over the last couple of years. Although the Business

School’s administration has made research one of the leading policy priorities, there is no

overall inter-departmental commitment and the finance department to address this funding

problem. The lack of funding also affected the study in the sense that I not have access to

Enough funds to be able to pay the research assistants well. It is a well-known fact in the

Business world that employees who lack proper motivation do not perform their duties as well

as they are required to.

Task one

Semester one results output

MARKS
85	85	53	60	99	76
52	75	68	60	91	78
68	98	65	60	90	91
54	85	54	66	76	90
53	75	53	60	76	63
52	98	57	60	78	60
68	65	57	66	99	66
52	52	57	60	91	51
54	68	57	66	90	51
53	65	57	60	99	60
52	54	99	66	91	66
52	53	51	76	90	63
85	65	60	76	76	60
75	54	63	78	76	60
98	53	60	99	78	66
75	68	60	91	57	51
98	65	66	90	99	63
85	68	63	76	91	60
75	65	60	76	90	66
98	54	51	78	76	51

Analysis of the semester one results

Mean	70
Mode	59.71
Median	66.11
Variance	218.4565
standard deviation	14.78028

From the analysis performed on the results of the first semester, the location and the descriptive statistics were performed and were found as shown above.

Semester two results 

MARK
73	39	73	51	44	49
58	49	58	52	47	44
57	47	57	52	47	47
58	39	49	54	42	47
57	64	49	51	49	42
66	66	47	52	41	49
70	70	56	54	44	42
73	64	39	51	47	49
58	66	56	41	47	42
66	70	39	44	49	49
58	73	56	47	41	68
47	64	39	47	44	54
56	57	49	42	47	51
39	64	56	49	47	68
49	70	52	44	42	68
47	73	54	47	49	52
49	57	51	47	44	54
47	64	68	42	47	51
47	66	52	49	47	68
56	70	54	41	42	68
41	42	41

Analysis of the results 

Mean	53
Mode	42.44
Median	49
Variance	93.38516
standard deviation	9.6636

From the analysis performed on the results of the second semester, the location and the descriptive statistics were performed and were found as shown above.

Semester three

MARK
54	44	45	45	37	48
56	48	54	40	26	43
59	45	56	43	40	46
44	40	59	48	46	48
28	43	44	48	37	43
34	44	54	40	26	46
28	44	59	46	40	48
34	44	59	37	52	43
56	44	34	26	43	46
44	45	59	40	46	37
28	40	34	37	48	26
34	43	43	26	43	46
54	44	44	40	46	48
56	44	43	46	48	52
44	45	44	26	43	52
28	40	44	40	52	52
54	43	48	46	46	52
56	44	45	37	48
44	44	40	40	43
28	48	48	46	46

Analysis results 

Mean	44
Mode	43
Median	44
Variance	62
standard deviation	8

From the analysis performed on the results of the second semester, the location and the descriptive statistics were performed and were found as shown above.

Overall discussion of the inter-semester results

In overall view the results of the different semesters were analyzed and it was observed that the mean mark of the semesters was reducing from the 1^st to the 3^rd semester with mean marks of 70, 53 and 44 respectively. This trend was witnessed in all measures of dispersal including the median and the mode which had 68.11, 49, 44 and 59.71, 42, 43 for the 1^st, 2^nd and the 3^rd semesters respectively. This shows the location statistics of the results were all in tandem. The variation statistics done included the variance and the standard deviations which also depicted results  which were trending in the same manner with the location statistics having marks of  14,9.6, 8 and 218, 93, 62 for the 1^st, 2^nd and the 3^rd semesters respectively.

Task 2

Domestic results marks

MARK
85	85	58	66	56	43	59
52	75	57	70	54	44	34
68	98	66	73	56	44	43
54	65	70	64	59	44	44
53	52	73	57	44	44	43
52	68	58	64	28	45	44
68	65	66	70	34	40	44
52	54	58	73	28	43	48
54	53	47	57	34	44	45
53	65	56	64	56	44	40
52	54	39	66	44	45	48
52	53	49	70	28	40	45
85	68	47	73	34	43	40
75	65	49	58	54	44	43
98	68	47	57	56	44	48
75	65	47	49	44	48	48
98	54	56	49	28	45	40
85	53	39	47	54	54
75	68	49	56	56	56
98	65	47	39	44	59
85	54	39	56	28	44
75	53	64	39	44	54
98	73	66	56	48	59
	58	70	39	45	59
	57	64	49	40	34

 

Analysis of the domestic results 

Mean	55
Mode	52.37
Median	54
Variance	210.7081
standard deviation	14.51579

International results

MARK
57	76	99	63	49	47	37	52
57	76	91	52	41	47	26	46
57	78	90	54	44	42	40	48
57	99	76	51	47	49	46	43
57	91	76	68	47	42	26	46
99	90	78	52	42	49	40	48
51	76	91	54	49	42	46	43
60	76	90	51	41	49	37	46
63	78	63	52	44	68	40	48
60	99	60	52	47	54	46	43
60	91	66	54	47	51	37	46
66	90	51	51	49	68	26	48
63	76	51	52	41	68	40	43
60	76	60	54	44	52	46	46
51	78	66	51	47	54	37	37
60	99	63	41	47	51	26	26
60	91	60	44	42	68	40	46
60	90	60	47	49	68	52	48
66	99	66	47	44	41	43	52
60	91	51	42	47	42	46	52
60	90	63	49	47	41	48	52
66	76	60	44	42	46	43	52
60	76	66	47	49	37	46
66	78	51	47	44	26	48
60	57	63	42		40	43
66

mean	56
mode	59.71
median	51
variance	268.4478
standard deviation	16.38438

Explanation 

In-terms of performance based on the students’ status; the domestic and the international students, international students had performed better than the domestic students since they had a bigger mean of 56 compared to 55 achieved by the domestic students. Also mode also showed that majority of the domestic students scored 52 marks while their counterparts’ international students had a silvering score of 59 achieved by many of them; however the domestic students had a bigger median value as compared to the international students 54 to 51. The data location and the descriptive shows that the international students out-shined the local students despite having a bigger number of 197 students while the domestic students were 165

In variation angle of view, it is observable that the domestic students had a standard deviation of 14.51 while the international students had a 16. This implies that the international students results were more dispersed than those of the domestic students.

Task 3

Results representation in graphs

Semester 3 domestic results

mean	45
mode	43.98
median	44
variance	65.9509
standard deviation	8.121016

Semester 3 international results 

Mean	42
Mode	42.52
Median	46
Variance	54.32544
standard deviation	7.370579

Discussion

Basing on the results for 3^rd semester alone, the domestic students’ performance outshined the international students since they had a bigger mark mean of 45 exceeding that of their counterpart by 3.  Also their mode value was larger; 43.98 to 42.52. Basing on the data locations, it is depicted that the domestic students had better bite of the grades.

In variations angle of view, it is seen that the domestic students had a bigger standard deviation; 8.121 compared to 7.370, which also shows that their results marks were also widely  dispersed.

Task 4 

HYPOTHESIS TESTING

H₀: μ₁ – μ₂ = 0
H₁: μ₁ – μ₂ ≠ 0

t-Test: Two-Sample Assuming Unequal Variances
	Var 1	Var 2
Avg	61.82994	50.4144
Var	388.5578	65.41635
Obs	162	200
Hyp avg Diff	0
Df	205
t Stat	6.914589
P(T<=t) one-tail	2.92E-11
t Critical one-tail	1.652321
P(T<=t) two-tail	5.84E-11
t Critical two-tail	1.971603

Discussion

 T test is used to attest if the mean score of the female students was greater than that of the male students, since T test is a measure of equity.  lf t Stat < -t Critical two-tail or t Stat > t Critical, then we will have to reject  the null hypothesis. We have, -6.914589< 1.971603<6.914589. Therefore, we do reject the null hypothesis and conclude that the female students mean marks were significantly greater than those of the male gender. The two means are not equal.

Task 5

ANOVA
Mark
	Sum of Squares	Df	Mean Square	F	Sig.
Betwn Grps	42010.908	2	21005.454	167.655	.000
Within Grps	44978.970	359	125.290
T	86989.878	361

To find the relationship among data in dispersed in more than two groups, T test not could have been have used. Therefore, One way Anova was selected as the best tool to be used in investigating the relationship between the 1^st 2^nd and the 3^rd semesters.

Discussion

We have the value of F as 167.655, which realize significance with a p-value of .000 (which is less than the .05 alpha levels). This interprets that there is a statistically significant difference between the means of the different semesters. The means are not related they do differ.

TASK 6

HYPOTHESIS TESTING

H₀: μ₁ – μ₂ = 0
H₁: μ₁ – μ₂ ≠ 0

	Var 1	Var 2
Mean	54.8703	55.91342
Var	206.5239	269.821
Obs	164	196
Hyp avg Diff	0
Df	357
t Stat	-0.64249
P(T<=t) one-tail	0.260485
t Critical one-tail	1.649133
P(T<=t) two-tail	0.520969
t Critical two-tail	1.966631

Discussion 

 T test is used to investigate whether the mean score of the domestic students was greater than that of the international students.  lf t Stat < -t Critical two-tail or t Stat > t Critical, then we will have to reject  the null hypothesis. We have, 0.64249<1.966631<-0.64249. Therefore, we do reject the null hypothesis and conclude that the international students mean marks were significantly greater than those of the domestic students.

TASK 7

HYPOTHESIS TESTING

H₀: μ₁ – μ₂ = 0
H₁: μ₁ – μ₂ ≠ 0

t-Test: Two-Sample Assuming Unequal Variances
	Var 1	Var 2
Avg	48.5203	63.90994
Var	98.5182	282.0944
Obs	200	160
Hyp avg Diff	0
Df	245
t Stat	-10.2468
P(T<=t) one-tail	4.88E-21
t Critical one-tail	1.651097
P(T<=t) two-tail	9.76E-21
t Critical two-tail	1.969694

 

Discussion 

T test is used to determine whether the mean mark of the Wollongong students was greater than that of Sydney students.  lf t Stat < -t Critical two-tail or t Stat > t Critical, then we will have to reject  the null hypothesis. We have, 10.2468<1.969694<-10.2468. Therefore, we do reject the null hypothesis and conclude that the Wollongong students mean marks were significantly greater than those of the Sydney students.

Conclusions and recommendations

• It is concluded that the female students performed better than the male students. To enhance equity in learning and equitability it is recommended that the learning staff should be also keen on performance of the male students  by focusing more on the what the male students do that inhibits and minimizes  their  learning power this might include involvement in betting and missing classes.
• From the analysis done it was concluded that the performance of the students were smoothly reducing as the semester’s shifts from the 1^st to the 3^rd semester. It is then recommended that the teaching staff and school should consider revising terminal based activities throughout the year and to ensure leveled results throughout.
• Basing on the student’s status it is concluded that the international students performed better than the domestic students. It is recommended that the domestic students should be motivated to undertake academic work with more desire and passion with will motivate and feed, open up their minds.
• Finally, from the data and its analysis it is concluded that that the Wollongong students were academically better than the students from Sydney. It is recommended that Sydney students should be given extra academic guidance and coaching that will help them to raise their grades.

 

 

 

 List of Reference

            

 AlphaBeta. (2018). Future Skills. Retrieved from https://www.alphabeta.com/wpcontent/uploads/2019/01/google-skills-report.pdf

Bone, E., & Reid, R.  (2013).  First course at university:  Assessing the impact of student age, nationality and learning style.  The International Journal of the First Year in Higher Education, 4(1).  95-107.  

Dawkins, P. (2014). Reconceptualising tertiary education and the case for re crafting aspects of the Abbott Government’s proposed higher education reforms. Melbourne: Mitchell Institute.

Ewell P., V. D’Andrea, P. Holland, K. Rust and F. Van Vaught (2010), AHELO TAG Report, December 2010.