students’ academic performance in different subjects in private education industry in Singapore
06-03, 2014
The main purpose of this survey is to identify the students’ academic performance in different subjects in private education industry in Singapore.
Survey methodology and Frame
In this case, a mix research method will be planed to utilize which combines quantitative method and qualitative research method. Lots of advantages of mix method are classified by previous research. Both Tashakkori and Tddie (2003) pointed out that qualitative research is usual used to dig out “factors” and qualitative research is always related to “exploratory”. The combination of qualitative method and quantitative method can collect descriptive data and also explore the deep meaning of respondents (Kaplan, 2004). Self-complementation survey questionnaire will be conducted to collect primary data due to the limited time and resources. Robson (2002) revealed the benefits of questionnaire that can gather data form large group in a shot time with lower price compared with other surveys. Besides the traditional questions, some open questions will be designed in the questionnaire to collect qualitative data. On the other hand, historical research method will utilized to collect secondary data. The official websites of private institutions in Singapore will be investigated to collect information about students’ academic performance. Besides the methods of data collection, the methods of analysis quantitative data and qualitative data need to be illustrated. A software package for statistical analysis in social science named “SPSS” is suggested to utilize to solve the data analysis problem. The correlations between different factors that have an effect on students’ academic performance can be clarified by the function of correlation analysis in “SPSS”. Furthermore, Linear regression model can be set to identify the relationship between the time students spent on study weekly and their final results of the examinations.
Questionnaire Design
In general, academic performance is defined as how well a student can complete his or her studies. In fact, numerous aspects that contributed to the level of students’ academic performance can be established based on the previous research which includes grades, attendance, standardized tests, extracurricular activities and behavior. The significant relationship between teachers’ practices in classroom and students academic performance is demonstrated by Harold (2002) in the Journal of Report-Research. Moreover, the link between the time students spent on study and students’ scores of tests also discovered by previous scholars.
Based all the issues discussed above, important factors that have strong impacts on students’ academic performance will be designed in questionnaire.
The structure of questionnaire reflects three main phrases. Firstly, students’ personal information is designed which consist of sex, age, major, nationalities, native language and subject area. In keeping with the data of official website, there are four schools in the college which contains School of Languages, School of Preparatory, School of Hospitality and School of Higher Learning. Therefore, different subjects considered as the main segmentation of students market should be planed in questionnaire in order to find out the differences of students’ academic performance in different major. In the second part, students’ academic performance in English learning will be investigated which can divided into a number of pieces such as score of the examination, situation of attending social activities and behavior. Whether students have interests to learn English, the history of learning English and how well they will performance all investigated to find out the different relationships among those variables. In the third part, different factors of affecting students’ academic performance of English learning will be investigated. Besides, open questions are designed to gather students’ different recommendations to improve test results. The whole questionnaire can be found in appendix 1.#p#分頁(yè)標(biāo)題#e#
Sampling Strategies
It no doubt that sampling is an essential aspect of survey, the sample selection and sample size determination is closely related to the validity, reliability and generalizabilty of the research results. Random sampling strategies which drawn in with erratic components are applied in this research with the purpose of enhancing reliability of data (Hirze & Guisan, 2002). According to the data from official website of Singapore government, there are 29 private universities in Singapore are accredited in Edu Trust System. In such case, Dimensions International College with 30 years history as a typical famous private institution in Singapore will be planed to use as a case study owing to the convenience to approach respondents and the feature of broadly representative. It is reasonable to believe that students of Dimensions International College can represent the real situation of students’ academic performance of private universities in Singapore. Wilson (2006) indicated that statistical methods are generally used to determine sample size of probability sampling. Most people believed that the larger sample size conducted the less sample error can be avoided, but it also depends on the companies expenditure on R& D. Obviously, The sample size is affected by several factors which comprises of the confidence interval of study, confidence level and the predict proportion of the survey. In the light of the statistical data of official website, there are approximately 2850 international students in Dimensions International College which can be considered as the whole population of this survey. In this research, the standard deviation of the population is expected as 1.96 which means that the value of Z score is 1.96 and confidence interval is 95%. Besides, it suppose that 80% students will complete the questionnaire namely that the response rate is 0.8 (P=0.8). Furthermore, the acceptable level of precision of results is assumed to ±5% and the value of i is 0.05. Based on the formulation to calculate sample size illustrated by Israel(1992):
S=Z2 * P (1-P)/ i2 = (1.96) 2 * 0.8* (1-0.8)/ (0.05) 2= 245.8624≈246 Students
Therefore, 246 students of Dimension International College are required to answer the questionnaire in order to ensure the confidence interval is 95% and the questionnaire will be distributed to students in campus randomly.
Task3
In this survey, more than 256 independent students are required to complete questionnaire and 256 data was selected to process in SPSS. From the descriptive analysis of data in SPSS, there are some significant results can be found as the following tables which include descriptive data analysis, correlation analysis and linear regression model analysis.
gender
Frequency Percent Valid Percent Cumulative Percent
Valid Female 125 48.8 48.8 48.8#p#分頁(yè)標(biāo)題#e#
Male 131 51.2 51.2 100.0
Total 256 100.0 100.0
gender * Study Level Crosstabulation
Count
StudyLevel Total
freshman sophomore junior Senior
gender Female 47 67 4 7 125
Male 61 61 5 4 131
Total 108 128 9 11 256
Obviously, there are 125 female students joined this research took 48%, and the number of male respondents is 6 more than female students which can be considered as the results of random sampling strategy.
From the bar chat and pie chat above, there are some surprised results can be found. Firstly, senior and junior students seem spend more time on study than freshman and sophomore which may lead by the ability of self-disciplined that can be influenced by age. In this situation, decision makers need focus on the young students especially freshmen who are lack of autonomy in study.
Correlations
gender Average Score Study Level Hours on Study Weekly
gender Pearson Correlation 1 -.250** -.092 -.488**
Sig. (2-tailed) .000 .144 .000
N 256 254 256 256
Average Score Pearson Correlation -.250** 1 .171** .674**
Sig. (2-tailed) .000 .006 .000
N 254 254 254 254
Study Level Pearson Correlation -.092 .171** 1 .152*
Sig. (2-tailed) .144 .006 .015
N 256 254 256 256
Hours on Study Weekly Pearson Correlation -.488** .674** .152* 1
Sig. (2-tailed) .000 .000 .015
N 256 254 256 256
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
After descriptive data analysis, the correlation analysis was applied to discover whether there are strong associations between variables. There are several factors that have significant relationship with test score can be found in the table above which include gender, level of study and the time students spent on study weekly.
Task3. 3
Additionally, a regression model is set to test the relationship between the hours that students spend on weekly and the test results, and the output is described as the following tables.
Model Summaryb
Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson
1 .663a .440 .438 .587 1.278
a. Predictors: (Constant), HoursonStudyWeekly
b. Dependent Variable: AverageScore
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 68.160 1 68.160 197.910 .000a#p#分頁(yè)標(biāo)題#e#
Residual 86.789 252 .344
Total 154.949 253
a. Predictors: (Constant), HoursonStudyWeekly
b. Dependent Variable: AverageScore
Residuals Statisticsa
Minimum Maximum Mean Std. Deviation N
Predicted Value .50 2.13 1.63 .519 254
Residual -1.591 1.953 .000 .586 254
Std. Predicted Value -2.178 .965 .000 1.000 254
Std. Residual -2.711 3.328 .000 .998 254
a. Dependent Variable: AverageScore
From the results, the value of R square is 0.44 which means that 44% possibility of test results can be influenced by the number of hours which students cost to study per week. In addition, the value column of “sig” of regression is 0.000 which means that this regression model can explain something has meaning. The average value of residual mean is 0 which can proof that the residual distribution meets the previous assumption and the residual error is normal distributed.
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig. Correlations
B Std. Error Beta Zero-order Partial Part
1 (Constant) .495 .087 5.679 .000
HoursonStudyWeekly .552 .038 .674 14.491 .000 .674 .674 .674
a. Dependent Variable: AverageScore
Scatter plot chat
Linear regression can be believed as the linear equation, and scatter plot graph was described to check the validity of the linear regression model. Clearly, all the data are distributed around the diagonal line of the scatter plot.
From the table of coefficient, the value of B column “unstandardized coefficient of Hours spent on study weekly is 0.552. In other words, the relationship between hours spent on learning per week and scores of examination can be expressed as the equation:
Test Score = 0.552* Hours spent on study per week + b
Task 4.1
With ever-increasing development race of technology, it is no longer for enterprises only use traditional management system, the application of management information system has become an inevitable trend. There all numerous benefits brought by different types of information in such a system. In 2002, Effy introduced several types of information in his book which include Transaction processing systems, Supply chain management systems, Customer relationship management systems, Business Intelligence Systems, Decision support and expert systems and Geographic Information Systems. In this report, the essential advantages of transaction information and customer information will be emphasized. Firstly, supply chain system called enterprise resource planning (ERP) system which focuses on the optimization of shipping resources such as raw materials and transportation. For instance, the stock size of particular product in different retail stores can be checked through the internet which will promote inventory transfer among retail to meet the customer needs due to the shortage of goods. In addition, the storage of customer information which can transformed into data that can be used for data mining with the intention of clarify the trends and patterns of customers. For example, WalMart discovered that large number of men will buy diaper when they purchase beer through the data mining. WalMart modified the display of goods in the market that sell diaper and beers in the same counter which improve the consumption of diaper quickly. Through the analysis of customer information, decision makers of companies can understand the common characteristics of customer which is useful to evaluate marketing strategies and enable effective decision-making. #p#分頁(yè)標(biāo)題#e#
Task 4.2-4.3
Time
(week) 1 2 3 4 5 6 7 8 9 10 Total
Demands 650 650 650 650 650 950 950 950 950 950 8000
Initial stock 320
Added demands 3250 4200
Inventory 2340 390 950
Ship to stock 3320 4360
Stock plan 7680
Initial Stock Level = 320
The required order quantity in 10 week= 8000 - 320=7680
EQQ = 450 items
One year= 5.2 * 10 weeks
Annual requirement (AR) = 5.2* 8000 = 41600 items
The number of annual order = 41600 items / 450 items = 92.4
Order interval = 5.2/92.4= 0.56278 Order Quantity=0.56278*7680=4360.7
Order Time= 0.56278 * 10 = 5.6278 week
The first week order quantity= 7680-4360 = 3320
EOQ is fundamentally an accounting method that decides the position at which the arrangement of order costs and inventory carrying expenditure are the least. In this Material requirement plan, in the first week 3320 items should be shipped to the stock, and in 4360 items should be shipped to stock in Sixth week under the economical calculation. In the spreadsheet, the inventory after meeting customers’ needs can be clearly found.
The importance of inventory control has already clarified by previous application in manufacture industries and by previous research. As a decision maker, it is necessary to know what you should order and when you should order to create sales for the company. The lack of the management of inventory may cause costly waste and pursue of zero stock by IT technology is the key point of lean production which is also the successful way to improve efficiency and optimize resources.
Task 4.4
Activity Label Description Preceding activities Duration (days)
N Design Hull
Prepare Boat Shed
Design Mast and Mast Mount
Obtain Hull
Design Sails
Obtain Mast Mount
Obtain Mast
Design Rigging
Prepare Hull
Fit Mast Mount to Hull
Step Mast
Obtain Sails and Rigging
Fit Sails and Rigging -
B,D
F,J
E,H,G,K
E,H,
L,M 9
According to the concept of critical path method and the activities described in above table, a network diagram which can show the process of launching a new ship can be draw as the following.
#p#分頁(yè)標(biāo)題#e#
Derived from the processing flow of launch a new ship, the project costs 29 days to finish which can be calculated as T= 9+ 8+ 6+2+ 4= 29. If the company wants to reduce the time to complete project, the process of design hull should be speed up because which is direct related to the final hours spent on the project.
Task 4.5
NPV is a typical time-adjusted measure of investment worth. Hence, the choice of more profitable project depends on the value of NPV.
year 0 1 2 3 4 5 Total
Project 1 -80000 18000 20000 25000 38000 45000 66000
NPV of Project 1 -80000 15652.17 15122.8733 16437.9058 21726.6233 22372.9531 11312.53
Project 2 -120000 30000 50000 50000 50000 15000 75000
NPV of Project 2 -120000 26086.96 37807.1834 32875.8116 28587.6623 8576.29868 13933.91
Based the cash follow provided above and the discount rate with 15%, NPV of project 1 and project 2 can be calculated as the followings:
NPV1=-80000+18000/ (1+15%) + 20000/(1+15%)2 + 25000/(1+15%)3 + 38000/(1+15%)4 + 45000/(1+15%)5 + 80000 = 15652.174 + 15122.873 + 16437.906 + 21726.623 + 22372.953= 11312.53
NPV2=-120000+30000/ (1+15%) + 50000/ (1+15%)2 + 50000/(1+15%)3 + 50000/(1+15%)4 + 15000/(1+15%)5 + 120000 = 26086.956 + 37807.183+ 32875.811+ 28587.663 + 7457.651+ 120000= 13933.91
NPV2 ＞ NPV1, therefore, Project 2 is more profitable than Project 1 which is more suitable for investment.
The internal rate of return (IRR) is another time-adjusted measure of investment worth. The selection of project can be judged by the value of IRR.
Assume the discount rate is 20%.
NPV of these two projects is as bellows:
year 0 1 2 3 4 5 total
Project 1 -80000 18000 20000 25000 38000 45000 66000
NPV of Project 1 -80000 15000 13888.8889 14467.5926 18325.6173 18084.4907 -233.41
Project 2 -120000 30000 50000 50000 50000 15000 75000
NPV of Project 2 -120000 25000 34722.2222 20093.8786 16744.8988 7233.7963 -16205.2
IRR of project 1 is: 15%+11312.53/[11312.53-(-233.41)]*(20%-15%)=19.90%
IRR of project 2 is: 15%+13933.91/[13933.91-(-16205.2)]*(20%-15%)=17.31%
Because IRR of project 1 is larger than IRR of project 2, we prefer to choose project 1.
Reference
Dimension International College , About Dimension, [Online]. Available at
: http://www.dimensions.edu.sg/About-Dimensions/About-Dimensions.php
[accessed 28 September 2010]#p#分頁(yè)標(biāo)題#e#
Hirzel. A., & Guisan., 2002, Which is the optimal sampling strategy for habitat suitability modeling ,
Journal of Ecological Modelling , Permissions & Reprints, 157( 2-3), P 331-34.
Israel. D., 1992, The Evidence Of Extension Program Impact, London: Thomson learning publishing
Kaplan, D., 2004, The SAGE handbook of Quantitative Methodology for the social sciences. California: SAGE publications Inc.
Robson, C., 2002, Real world research. 2nd ed. Oxford: Blackwell Publishing.
Singapore Government, 2010, Private education. [Online]. Available
at: http://www.moe.gov.sg/education/private-education/ [accessed 27 September 2010]
Wenglinsky. H., The Link between Teacher Classroom Practices and Student Academic Performance.
Journal of Education Policy Analysis Archives,10(12)
Appendix 1: Questionnaire
Questionnaire of Students’ performance in Singapore
Thank you very much for your cooperation to complete this questionnaire. It will probably only take you about
Part one: Personal Information (only tick one box for each question)
1. Gender:
○ Male ○ Female
2. Age:
○ Under 19 ○ 19 - 24 ○ 25 -30 ○ over 30
3. Nationality:
○ Singapore ○ Malay ○ Chinese ○ British ○ Thai ○ French
○ Indian ○ Others¬___
3. Native language:
○ English ○ Chinese ○ Malay ○ French ○ Others
4. Education level:
○ freshman ○sophomore ○ junior ○senior
5. Subject area:
○English Language ○ Psychology ○Elementary Maths
○ Social Science ○Art ○ Humanities
○Others________(Please specify)
Part two: Students’ academic performance in English learning
1. Do you have interests to learn English?
○ Yes ○ No
2. Do you think it is necessary for you to learn English well?
○ Yes ○ No
3. Do you have any experience to study English from private tutor?
○ Yes ○ No
4. How long have you been learning English?#p#分頁(yè)標(biāo)題#e#
○ Under 10 year ○ More than 10 year ○Native speaker
5. Do you attend every English class required in your subject?
○ Yes ○ No
6. How much time do you spend on learning English per day except English lesson?
○ Less than one hour ○ 1-2 hours ○ 2-3 hours ○ more than 3hours
7. Which way is your most favorite method to improve your English skills?
○ watch English movie ○ listen English songs
○ read English newspaper ○ browse English website
8. How much mark have you got in the last final English exam?
________________________________________________( please specify)
9. Do you usually set a target of exam result?
○ Yes ○ No
Part Three: Factors of affecting students’ test results
1. What is the most important environment factor to improve your English?
○ family ○school ○ friends ○ country
2. Answer how well you evaluate the importance of elements of the course.
(1= not important, 2= less important, 3= Undecided, 4= fairly important, 5=very important)
1 2 3 4 5
1) Class discussion
2) Interact with instructors
3) Questions and answers
4) Lecture handouts and other materials
5) Clear content
6) Interesting
7) Useful feedback
9) Results of the course
3. What is the most useful way to enhance your test result?
○ remedial teaching after class
○ Discussion with classmate
○ Independent learning after class
○ Others__________ (Please specify)
4. Do you have any specific suggestions to improve students’ test results in your university?
____________________________________________________________________
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