Intelligent Higher Institution Student Selection System

Itaza Afiani Mohtar, Nurul Adila Zulkifli, Siti Salihah Shaffie Faculty of Computer and Mathematical Sciences

Universiti Teknologi MARA (Perak) Seri Iskandar, Perak, Malaysia itaza328@perak.uitm.edu.my sitis064@perak.uitm.edu.my

Abstract— Higher education institutions admission faces the need for a precise and effective method to evaluate and select the most qualified applicants to be in their institution. Currently, admission officers have to manually evaluate every applicant's data against the set of admission requirements before selecting the few successful ones. The manual process contributes many problems such as inaccurate decision resulting from human error, needs a lot of effort and is time consuming. The objectives of the paper is to identify and suggest an intelligent selection method using fuzzy system to assist higher institutions to select the most suitable applicants and also suggest suitable course based on their high school certificate result. There are two phases involved in this project. The first phase requires user to input the data which will be processed in the fuzzy inference system. If the applicant qualifies, the system will proceed to the second phase which is course suggestion phase. The result shows whether the applicant qualify and if so, will be suggested a course. This project uses Sugeno-Style Fuzzy Inference technique to select the suitable applicants. A prototype was developed and tested with thirty sample data. From the analysis, this prototype achieved 83% accuracy. As a conclusion, this technique is suitable to be applied to automatically suggest suitable applicants for intake into higher institutions.

Keywords—Student Selection; Fuzzy Inference

I. INTRODUCTION Selecting suitable students for an institution of higher

education requires tremendous focus and effort on the part of the Admission Officers. This process is complex and difficult because the officers have to work with the set of applications along with the constraints set by the institution. The complexity of the process is made further difficult by the ever increasing number of applications year after year [1].

Identifying suitable students requires meeting the minimum and optional criteria set by the institution and the programme the students’ want to enrol. Among the criteria considered are cognitive assessment and also the non-cognitive assessment [2] such as high school results [1], transferred credits hours [3], specific entrance examination [4] and interview [5]. Other criteria such as the applicant’s artistic, athletic and academic interest, the total fees that the applicants have to pay and the

applicants’ race were also considered when determining the qualification of the applicant [6].

The selection process is mostly done manually [7]. It involves having the admission officers evaluate every applicant’s data and then match them with the set of admission requirements. This is a very difficult process because time and effort is needed to process the data, especially if a large number of applications have to be evaluated. Aside from error resulting from inaccurate judgement, another setback to the manual selection process is when an applicant does not meet the requirement for a certain course, he would not have the chance of being considered for other courses even though he may be qualified for that course [7].

The manual process which is highly dependent on the officers’ evaluation is open to mistakes due to human error and inconsistent judgement. Therefore there is a need for a precise and effective method to evaluate and select the most qualified applicants and at the same time reduce the amount of time needed to complete the selection process.

Efforts were made to automate or simplify the task of selecting suitable applicants for admission into institutions of higher learning using a number of techniques. Among the techniques used are neural networks [8,9,10], data mining algorithms [11,12,13] and centralised matching schemes [14,15]. These techniques were reported to be effective in determining the suitable applicants to be offered a place in the institutions of their choice. However, these techniques do not clearly show the users, on what basis or reasons the techniques took to achieve the list of qualified applicants.

We would like to propose another technique which is fuzzy inferencing combined with a weightage system in determining the suitable applicants.

II. WHY FUZZY INFERENCE Fuzzy inferencing uses rules that take into

consideration vague verbs such as “highly qualified” or “less qualified” to rank the applicant. In fuzzy inferencing, the knowledge of the problem domain is transformed into rules with "degrees of truth" rather than the common "true or false" (1 or 0) Boolean logic on which the modern computer is established. Vague verbs

2011 International Conference on Computer Applications and Industrial Electronics (ICCAIE 2011)

978-1-4577-2059-8/11/$26.00 ©2011 IEEE 396

such as ‘very’, ‘highly’ or ‘somewhat’ are used alongside the rules. The use of these verbs, models how we as humans categorise the elements in our surrounding. For example, it is better to say, “the applicant is somewhat qualified’ rather than strictly ‘not qualified’. This is because with the former sentence, there is indication that the applicant is partially qualified for admission and can be considered further. Based on this argument, we feel there is an opportunity to extend the use of fuzzy inferencing into the student selection process.

A public university in Malaysia was chosen as a case study whereby the applicants were to be considered into the Mengubah Destini Anak Bangsa Programme.

The Mengubah Destini Anak Bangsa (MDAB) programme gives opportunity to students from the low income group and with minimum qualification, to further their tertiary education. The admission officers have to first verify the eligibility of the applicants based on the income. This requires mapping the income to the number of dependents. After the applicants are authenticated as being in the low income group, they are then offered suitable preparation courses based on the high school certificate result.

III. RESEARCH METHODOLOGY This research underwent four phases; knowledge

acquisition, prototype development, result analysis and research documentation.

In knowledge acquisition, literature reviews focused on how other higher education institutions select their students, what are the criteria’s that have been set and the techniques that have been applied to automate the selection process.

The second phase, the prototype development phase involves developing the prototype using SDLC (System Development Life Cycle). SDLC has five phases; planning, requirement analysis, design and implementation, result analysis and maintenance.

After the prototype was completed, the research underwent the third phase; result analysis. Thirty sample data was given to the system and the admission officers. The result from the system and the admission officers were then compared and analysed.

In the final phase which is documentation, all the processes and the activities involved in this research were documented.

IV. SYSTEM ARCHITECTURE The prototype for the student selection system using fuzzy inference was developed. The system model is shown in Fig. 1. The system has 4 modules: 1) Input Module 2) Fuzzy Inference Module 3) Course Suggestion Module 4) Result Module In the first module, the user is asked to input numerical values pertaining to the applicant’s household income, number of dependents and the high school results.

In the Fuzzy Inference module, those inputs are then fuzzified. This means they are represented in terms of fuzzy logic. Each input value is transformed into a degree of membership in the relevant fuzzy set. The next step is evaluating the fuzzy rules against the set of fuzzified inputs. The values from the evaluation are then aggregated and later defuzzified to produce a single value. The output from the fuzzy inference phase will determine whether the applicant “highly qualify”, “qualify” or “not qualify” for the MDAB Programme. If the applicant qualifies, then the data is input into the Course Suggestion Module. The module will rank the preparation course (1 – most suitable, 2 – suitable, 3 – least suitable) for the applicant based on the high school result. There are three courses available; Pre-Diploma Science, Pre-Diploma Commerce, and also Pre-Diploma Accountancy.

The Result module will display the result achieved from the fuzzy inference.

Figure 1.

Figure 2. Architecture of Intelligent student Selection System

Course Suggestion Module

Fuzzy Inference Module

Crisp Output (Result)

Fuzzification

Rule Evaluation

Aggregation Rule Output

Defuzzification

Crisp input value (User)

Programme Suggestion

Result

397

A. Fuzzy Inference Module The Fuzzy Inference module has three important

components; the fuzzy sets, the fuzzy rules, and the Sugeno inference engine. The inference technique selected for this prototype is Sugeno-style Inference.

B. Lingusitic Variables Linguistic variables must first be identified before

fuzzy sets can be created. Three linguistic variables have been identified; income, number of dependents and student qualification. The income variable is a very important criterion in this module. This is because the requirement of MDAB programme stated that the programme is for the low income group. Thus, the ranges of the income value must be carefully decided. This module also takes into consideration the number of dependents in the household.

For the income variable the linguistic value that has been considered is low, average and high. For the dependent variable the linguistic value is less, average and many. On the other hand, the linguistic values for student qualification is not qualify, qualify and highly qualify. Each output value is defined by increments of four (not qualify=1, qualify = 5, highly qualify=9). Table 1 shows the linguistic variable Income along with the range. The range was derived from the average household income in Malaysia according to the Household Income Survey 2009 [16].

TABLE I. LINGUISTIC VARIABLE, INCOME WITH RANGE

LINGUISTIC VARIABLE

LINGUISTIC VALUE

NUMERICAL RANGE (RM)

Income

Low 0 – 1200 Average 800 – 2000

High 1700 – 2500

C. Fuzzy Sets To represent the fuzzy set, the membership function

must be determined first. The fuzzy sets in various fuzzy logic systems have many types of the shape. However a triangle or trapezoid provides the suitable representation in order to make the process of computation easier. This prototype applies trapezoid and triangle shape. Figure 2 shows the Fuzzy set for linguistic variable income.

Figure 3. Fuzzy Set for Linguistic Variable, Income

D. Fuzzy Rules Fuzzy logic approach applies if-then rules to recreate

the results of human’s natural common sense. There are two distinct parts in the rule which is evaluating the rule antecedent (the IF part of the rule) and the other one is applying the result to the consequence (the THEN part of

the rule). The antecedent part can be multiples and then will be calculated to be in a single number, using fuzzy set operation. For this prototype, there are multiple antecedents, and the fuzzy operator AND have been used to obtain a single number that can represent the result of the antecedent evaluation. The following are some examples of the rules used in the prototype: 1. If (income is low) and (dependent is less) then (student is qualify) 2. If (income is average) and (dependent is less) then (student is qualify) 3. If (income is high) and (dependent is less) then (student is not_ qualify) The rules were constructed after interview sessions with the admission officers. The rules are then verified again with the officers to ensure that the logic suits the problem at hand.

E. Sugeno Inference Engine There are several types of fuzzy inference technique

such as Mamdani - Style inference and Sugeno-Style inference. The most commonly used fuzzy inference technique is Mamdani-Style. However, this technique involves substantial computation. On the other hand, Sugeno-Style inference technique is more compact and computationally efficient compared to Mamdani-Style inference [17]. The Sugeno-Style inference is implemented in this prototype because it provides efficient aggregation and defuzzification functions that can be used to calculate the output range and well suited to mathematical analysis.

Basically, the Sugeno-Style is similar to the Mamdani-Style in many aspects. The main difference is that the Sugeno-Style output membership function is either linear or constant. Sugeno-Style uses a single spike (singleton) as the membership function of the rule consequent. A singleton means a fuzzy logic set with a membership function that is unity at a single particular point on the range of possible value of linguistic variables. The following are the steps in Sugeno-Style inference technique:

1. Fuzzification. 2. Rule evaluation. 3. Aggregation of the rule output. 4. Defuzzification.

Fuzzification. First take the two crisp inputs; input

crisp value 1 (i.e income=1000) and for input value 2(i.e. number of dependents = 3). Then, determine the degree to each of these inputs belong to the correct fuzzy set. After obtaining the crisp inputs, they are fuzzified as shown in fig. 3. The crisp input for income corresponds to the membership function low and average to the degree of 0.5 and 0.3 respectively. The number of dependents variable maps the membership functions less to the degree of 1.0.

Rule Evaluation. For the second step, the fuzzified input for income and number of dependents are applied to

0 800 1200 1700 2000 2500

Low Average High

1

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the antecedents of the fuzzy rules. In this step, the zero-order Sugeno fuzzy model is applied:

IF x is A AND y is B THEN z is k

Where, k is constant value. Thus, the output of each fuzzy rule is a constant, which means, all the consequent membership functions are represented by single spikes. For example, the rules evaluations are: If (income is low) and (dependent is less) then (student is qualify)

if (0.5) & (1.0)= 0.5 k2 If (income is average) and (dependent is less) then (student is qualify) if (0.3) & (1.0)=0.3 k2

Figure 4. Fuzzification of Input Variables

Aggregation of the rule consequent. For the aggregation process, all the single spikes are combined into one fuzzy set.

Defuzzification. The last step in the Sugeno-Style procedure is to find the weighted average (WA) of the aggregation process. Because in Zero-order Sugeno-style the consequent is constant and the constant value is already defined, the rule that have been fired is multiply by that value. The equation is:

WA = ∑ r (k ) (1)

∑ r where,

WA = weighted Average r = rule k = constant value For this example, the calculation of WA is: WA = 0.5 * 5 + 0.3 *5 = 5 0.5 + 0.3 The result shows that this applicant is qualified for the MDAB Programme. After getting the result from the fuzzy inference process, the system proceeds to the next phase which is Course Suggestion Phase.

F. Course Suggestion Module After getting the results of the applicant’s

qualification from the fuzzy inference module, the next step is to rank the suitable course to be offered to the applicant which will be handled by the Course

Suggestion Module. The suggested course will be ranked either 1 - most suitable, 2 - suitable, or 3 - least suitable.

In the Course Suggestion module, the first stage is to transform the high school certificate result. The following subjects were taken into consideration, Bahasa Melayu (BM), English (BI), Mathematics (MATH)/ Additional Mathematics (ADD MATH), Science (SC)/ Chemistry (CHEM)/ Physics (PHY)/ Biology (BIO), History (HIS), Accountancy (AC) and Commerce (CM). The result is transformed into a numerical value so that it can be used to calculate the score. The numerical value for each subject in the high school certificate result is based on the meritocracy system. This system is applied by the Ministry Of Higher Education [18] to rank the students’ eligibility for entrance into public institutions of higher learning. Table 2 shows the value for each grade in the meritocracy system.

TABLE II. MERITOCRACY SYSTEM

There are three courses offered in the MDAB

programme; Pre-Diploma Science, Pre-Diploma Commerce, and also Pre-Diploma Accountancy Programme. The applicants will be suggested to the course offered according to their qualification. For example, if the applicant’s get higher scores in Science and Mathematics then the course Pre- Diploma Science will be ranked as 1. To identify the suitable course for the applicant, the highest score from the SPM result with the minimum requirement for each programme is calculated.

The calculation for the score for each course is based on the equation by [19].

∑≥

=n

knk qrs

1

(2)

where, S =Score for each course rk =Represents the high school certificate results qn =Represents the minimum requirements for each course in the MDAB programme.

The minimum requirement for each course score is shown in Table 3. The marks for each subject are based on equation 2. For example, for each programme offered, the minimum requirement for “Bahasa Melayu” is six marks. The six marks represents grade “C” in the high school certificate result. This requirement implies that the grade must be no less than six marks. If the applicant took mathematics and additional mathematics subject, the highest grade between these two subjects will be chosen.

GRADE VALUE A+ 18 A 16 A- 14 B+ 12 B 10

C+ 8 C 6 D 4 E 2 G 0

399

The same principle is applied to science, physics, chemistry, and biology subject.

The following shows an example of the score calculation for the Pre-Diploma Science Score: Pre-Diploma Science Score = (BM marks * 6) +(BI marks * 4) + (MATH/ADD MATH marks * 6) + (SC/PHY/CHEM/BIO marks *4) + (HIS marks * 2)

TABLE III. MINIMUM REQUIREMENT FOR EACH SUBJECT

V. RESULT AND ANALYSIS The prototype was tested with 30 sample data. The

same data were also given to the admission officer to be evaluated. Each data contains information regarding the applicants’ household income, number of dependents and high school certificate result. The prototype will determine whether the applicant qualifies and if so, will suggest the course suitable for the student. A segment of the sample data is shown in Table 4.

The prototype system identified four applicants as ‘highly qualify’, twenty as ‘qualify’ and five applicants as ‘not qualify’.

Meanwhile, the admission officer identified four applicants as ‘highly qualify’, eighteen as ‘qualify’, and eight applicants as ‘not qualify’.

A. Analysis The sample data evaluated by the admission officer is

compared with the result obtained by the prototype. The comparisons between both results are shown in table 5.

From the table, the prototype system and the admission officer both selected the same twenty-five applicants out of thirty. Both came to different conclusions for the five remaining applicants. This means the system accuracy is 83%. The reason for the discrepancy is maybe because the admission officer did not take into consideration the number of dependents. The officer’s judgement is based on the cut off value of RM2000 for eligible income regardless of number of dependents, whereas the prototype system bases its judgement on income and number of dependents. Thus, in the prototype system, the applicants with high household income and have less number of dependents do not qualify for the MDAB Programme. This can be seen in sample data number ten. The applicant is not qualified because the household income is high which is RM1900 and has only one number of dependent. However, from the manual selection process by the admission officer, this applicant is qualified for the MDAB programme.

VI. CONCLUSION Based on the results achieved by the prototype, it can

be concluded that this system has achieved the objectives of selecting suitable students for the MDAB Programme from a large number of applicants. We found and justified the need to apply fuzzy inference in the selection process whereby the data is large and time consuming to select or suggest manually. The analysis of the result between the prototype system and the manual process indicates that this technique is applicable for the selection process.

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Admission Recommender System for Secondary School Students”. Paper presented at the The 6th International Conference on Information Technology and Applications, 2009.

TABLE IV. SAMPLE DATA

COURSE

THE MINIMUM REQUIREMENT SUBJECT FOR MDAB PROGRAMME

BM BI MATH /ADD

MATH

SC/ CHEM /PHY /BIO

HIS AC CM

Pre-Diploma Science

6 4 6 4 2 - -

Pre-Diploma Accountancy

6 4 4 - 2 4 -

Pre-Diploma Commerce

6 2 2 - 2 - 4

NO INCOME (RM) DEPENDENT

HIGH SCHOOL RESULT

BM BI MT/ ADD

SC/CHEM/PHY/ BIO HIS AC CM

1 500 1 C D C E C - -

2 900 2 C C D - B C E

3 1000 3 B A B+ - A A A

4 500 3 B C B+ B C A -

5 900 4 B A D A- C - -

6 1000 5 C D C C+ B - A

7 500 6 C D C C B A -

8 900 8 C D C - C - -

9 1000 1 A B B - B+ - -

10 1900 1 B+ D B+ D C - -

400

TABLE V. COMPARISON OF THE RESULT

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[19] Sharifah Nurulhikmah Syed Yasin, Noor Maizura Mohamad Noor,Mustafa Mamat. “Determining the Preferences among the High School Students Towards the Local Malaysian Public Universities :A case study” International Journal of Soft Computing, vol 4(5), 2009, pp215-222.

NO INCOME (RM)

DEPENDENT

HIGH SCHOOL RESULT QUALIFICATION (by system)

QUALIFICATION (by officer)

COURSE OFFER

COURSE OFFER

(by officer) BM BI MT/ ADD

SC/CHEM/PHY/

BIO

HIS AC CM (by system)

1 500 1 C D C E C - - QUALIFY QUALIFY P_COMMERCE P_COMMERCE 2 900 2 C C D - B C E QUALIFY QUALIFY P_ACCOUNT P_ACCOUNT 3 1000 3 B A B+ - A A A QUALIFY QUALIFY P_ACCOUNT P_COMMERCE 4 500 3 B C B+ B C A - QUALIFY QUALIFY P_ACCOUNT P_ACCOUNT 5 900 4 B A D A- C - - QUALIFY QUALIFY P_COMMERCE P_COMMERCE 6 1000 5 C D C C+ B - A QUALIFY QUALIFY P_COMMERCE PRE SCIENCE 7 500 6 C D C C B A - HIGHLY

QUALIFY HIGHLY

QUALIFY P_ACCOUNT PRE SCIENCE

8 900 8 C D C - C - - HIGHLY QUALIFY

HIGHLY QUALIFY

P_COMMERCE P_COMMERCE

9 1000 1 A B B - B+ - - QUALIFY QUALIFY P_COMMERCE P_COMMERCE 10 1900 1 B+ D B+ D C - - NOT QUALIFY QUALIFY - P_SCIENCE

401