Selection of Educational Software Tools based on AHP and TOPSIS

Selection of Educational Software Tools based on AHP and TOPSIS

Aliu Folasade Mercy Department of Computer Science Elizade University

Ilara-Mokin, Nigeria

Kehinde K. Agbele Department of Computer Science Elizade University

Ilara-Mokin, Nigeria

Abstract Several software packages are available for the smooth-running of an educational system. However, making the choice for most-appropriate software can be very tasking. This may require the meeting of the schools management team to take quality decisions based on some given criteria. As a result, conflicts may arise if standard multicriteria decision methods are not applied in the selection process. This current research focuses on the application of AHP and TOPSIS methods to select the most relevant software among three options for use in a high school. Five members of the management team evaluated the various criteria and conducted pair-wise comparisons to determine the weights using AHP. The choices were further ranked based on the TOPSIS method. The result showed SMS C as the best choice for the school, with a TOPSIS ranking score of 0.730044.

Keywords Analytic Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), School Management Software (SMS), Multicriteria Decision Making (MCDM)

1. INTRODUCTION

   School authorities are constantly involved in several activities to efficiently manage the educational processes within their domain. These activities range from admissions to class work and results-computation among others. The educational system is gradually transcending from paper works to digital processes. Therefore, to improve the performance of the school administration, many school authorities opt-out a school management software to smoothly automate some of the important educational processes. School management software is a collection of relevant application programs that organizes the school processes into a well-structured system to effectively give the staff and students an interesting educational experience. There are various educational software packages available nowadays, with each of them having its uniqueness. Therefore, selecting suitable software for a specific school can be quite demanding. The application of a multicriteria decision making (MCDM) method is a good way to make an optimal choice in the selection process. MCDM methods include but are not limited to Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), ELimination Et Choix Traduisant la REalitè (ELECTRE), Best-Worst Method (BWM) [1] [2], Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE). There are no specific rules to be followed

   when making a choice at MCDM [3]. This work applies AHP and TOPSIS in the selection of suitable school management software for an educational system.

2. A REVIEW OF RELATED WORKS

   AHP is MCDM tool that can be applied in a wide range of fields [4]. AHP was originally developed by Thomas L., Saaty in the 1970s and has been used in various fields to make decisions. A multicriteria analysis for supplier selection in a university using AHP was performed by [5]. The selection was conducted based on flexibility, delivery, variety, quality and cost. The developed model can evaluate and monitor the performances of various suppliers for the procurement department of the institution. In [4], the factors affecting the choice of retail pharmacies in Bangladesh while purchasing the drugs from various pharmaceutical companies were prioritized. Six factors were identified and then analyzed through analytic hierarchy process (AHP) to quantify the qualitative factors through a standard scale.

   AHP was also used by [6] to develop a framework that selects suppliers for qualitative dairy products in Indonesia. The criteria for the analysis were based on quality, quantity, delivery, warranty, and pricing. In 2019, [7] proposed a hybrid decision-making approach based on Analytic Hierarchy Process (AHP) and Dempster-Shafer Theory (DST) to evaluate and select a new product. AHP and DST were used in weight determination to improve accuracy and objectivity.

   Furthermore, AHP was applied by [8] in the selection of an optimal Electronic Toll Collection (ETC) method. Three ETC technologies were analyzed based on five criteria and their result provides an intelligent guide for toll selection in Nigeria. In addition, the application of AHP was used to establish a multicriteria-based equipment selection framework for sustainability in the context of the Malaysian construction industry. The resultant procurement index helps decision-makers in the process of the acquisition of sustainable construction equipment in Malaysia [9]. AHP and ELECTRE methods were used by [10] to evaluate and select a suitable hospital management software. Their result showed the cost criterion to be the most important criterion in the decision-making process.

   In [11], Fuzzy TOPSIS was used to quantify data and prioritize criteria for enterprise information security architecture. AHP and TOPSIS were integrated by [12] to determine the most appropriate tomography equipment.

   AHP was used to determine the weights while TOPSIS was applied to evaluate the purchase options.

   A multi-criteria decision approach was applied by [13] to

   while the negative ideal solution, , consists of all the

   worst values achieved for each attribute [15] [20].

   TOPSIS considers both the distance to an ideal best

   +

   select the best college coaches. In their work, the AHP was

   applied to find the best coaches from different sports and to rank these coaches while TOPSIS method was used to test the correctness and effectiveness of the model. The work of [14] AHP and TOPSIS independently to assess the performance of some financial institutions and to determine the best performing organization for a period of four years. In [15], an experiment was conducted on employee placement using several MCDM methods. Their results gave an accuracy of 95% using TOPSIS method. They concluded

   solution, , and the distance to an ideal worst solution, ,

   by taking the proximity relative to the positive ideal solution.

   Based on a comparison of the relative distance, an

   alternative priority arrangement, , can be achieved. This method is used to solve practical decision-making problems.

   Because the concept is simple and easy to understand, computing is efficient and has the ability to measure the relative performance of decision alternatives. The steps for TOPSIS are seen below.

   Step 1: Vector normalization is done using equation 3.

   that when there are many criteria, the accuracy is reduced

   using AHP.

   =

   (3)

   2

3. METHODOLOGY

   1. Analytic Hierarchy Process (AHP)

      AHP organizes criteria and alternatives into levels of hierarchy to enable experts to make easy comparisons among several variables [16]. In the AHP method, an important indicator is the number of criteria. This affects the consistency of the result because more than seven criteria lead to an increase in inconsistency [17]. The AHP

      where represents the performance values of each cell.

      Step 2: Multiply the corresponding weights by the

      normalized values to obtain the weighted normalized

      decision values, .

      Step 3: Determine the values for the ideal best and ideal

      worst for each criterion. For non-beneficial factors, loer values are the ideal best, while the higher values are the ideal worst. For beneficial factors, the higher values are the ideal

      +

      best, while the lower values are the ideal worst. indicates

      mathematical methods are shown in equations (1) and (2).

      • Define the value of the criteria, that is, the judgment matrix, C, on the scale 19.

      • Calculate normalized matrix using Equation (1):

      the ideal best solution. indicates the ideal worst solution.

      Step 4: Calculate the Euclidean distance from the ideal best

      (+) and the ideal worst (). They are expressed in

      equations 4 and 5.

      , =

      (1)

      + + 2 0.5

      where Cij is the criteria value; Cij is the column sum.

      Calculate priority vector using Equation (2):

      = [=1( ) ]

      0.5

      (4)

      2

      , =

      , (2)

      = [=1( ) ]

      (5)

      where Xi,j is the normalized matrix column sum; and n is the number of criteria.

      When applying AHP method, it is important to involve

      Step 5: Calculate the performance score, . It is expressed

      as seen in equation 6.

      experts in the evaluation process so that the values obtained can be re-used in future [18]. The decision process is done

      =

      The performance score

      (6)

      ++

      b ween 0 and 1. The closer it is

      is et

      using Saatys scale of preference as seen in Table I.

      TABLE I. Scale of Comparison [19]

      Scale	Degree of Preference
      1	Equal significance
      3	Moderate significance of a factor over the other
      5	Strong significance
      7	Very strong significance
      9	Ultimate importance
      2, 4, 6, 8	Estimates for inverse comparison

   2. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)

      The principle used in TOPSIS is that the chosen alternative must have the closest distance from an ideal best solution and furthest from an ideal worst solution from a geometric point of view using the Euclidean distance to determine the relative proximity of an alternative with the

      optimal solution. The positive ideal solution, +, is defined

      as the sum of all the best attainable values for each attribute,

      to 1, the more optimal the solution is. Step 6: Rank the alternatives.

4. RESULTS AND DISCUSSION

   This research uses AHP and TOPSIS to select the appropriate software among three proposed school management software (SMS) based on the following factors.

   • Component: The software is expected to handle admissions, fees, class assessment, academic activities and relationship management for the school.

   • Cost: This factor is to be considered to fit in into the estimated budget of the school without negatively affecting the turnover. It includes the initial cost, running cost and maintenance cost.

   • Ease of use: The software is expected to be user- friendly, easy to navigate, have an attractive GUI, and have access to support.

   • Maturity: Software should be known for its capability to handle school management activities.

   SMS Software Selection

   Component

   Cost

   Ease of Use

   Maturity

   SMS A

   SMS B

   SMS C

   Fig. 1. Hierarchy Structure for the Software Selection

   The above factors are arranged into a hierarchical tree to determine their weights using AHP as shown in figure 1. Five members of the management team made the decision

   1

   1

   3

   using the scale of comparison shown in Table 1. The judgement matrix is shown.

   3 5

   3 1 5 7

   = 1

   3

   1 5

   1 3

   1 1

   ]

   [5 7 3 1

   Six comparisons were done with a consistency ratio of 4.6%.

   The resultant weights of the process can be seen in Table II.

   Criteria	Priority	Rank
   Cost	0.262	2
   Component	0.565	1
   Ease of Use	0.118	3
   Maturity	0.055	4

   Table II. AHP Weights for Selection Factors

   Based on the outcome of the AHP analysis, it can be seen

   that the Component criterion has the highest threshold, with a weight of 0.565. This implies that Component factor is most important when considering the selection of a school management software. The obtained weights are further applied to select the most suitable software for the school. The judgement is consistent because the value of the consistency ratio is less than 10%. Fig. 2 shows the graph of the criteria weights.

   Fig. 2. Weights of Selection Criteria

   Furthermore, TOPSIS is applied to rank the three SMS alternatives. The resultant matrices and ranking are shown in Tables III, IV and V.

   TABLE III. TOPSIS Weighted Normalized Decision Matrix

   Ideal Best	0.028619	0.374180391	0.080947	0.044610891
   Ideal Worst	0.250412	0.299344313	0.06071	0.017844356

   Options	+		+ +		Rank
   SMS A	0.222715	0.079479	0.302194	0.263006	3
   SMS B	0.0881	0.180228	0.268328	0.67167	2
   SMS C	0.082015	0.221793	0.303808	0.730044	1

   TABLE V. Summary of Final TOPSIS Analysis

   	COST	COMPONENT	EASE	MATURITY
   SMS A	0.250412	0.374180391	0.06071	0.044610891
   SMS B	0.071546	0.299344313	0.080947	0.026766534
   SMS C	0.028619	0.299344313	0.06071	0.017844356

   TABLE IV. Ideal Best and Ideal Worst Matrix

   As shown in Table V above, the best choice is SMS C with a score of 0.730044 because of its closeness to 1. The closer

   the performance score is to 1, the more excellent the outcome is.

5. CONCLUSION

   Fig. 3. TOPSIS Performance Score for the SMS

   management system, Technology and Health Care, 2018, DOI 10.3233/THC-170947 IOS Press

   Based on the result of this study, it can be seen that software selection process should not be handled casually but should be done using suitable MCDM methods such as AHP and TOPSIS. The component factor is of utmost importance when making decision for an ideal school management software. The weights for the criteria were obtained using AHP while the actual ranking of the software was done through TOPSIS. Therefore, the hybrid approach yields an excellent result with performance score of 0.730044.

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