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Optimizing Allocation and Distribution of Students’ Assistance Among SUCs Using Analytic Network Process (ANP)

Queeny Eliza D. Saludares*, Rhoda A. Namoco

University of Science and Technology of Southern Philippines, Cagayan de Oro City, Philippines

Abstract: This study proposed an ANP model as a decision-making tool to aid in allocating and distributing the students’ assistance among the respective colleges and the SHS department of the university. The criteria considered were (1) the total population of students for each college/department, (2) the availability of technology for online classes such as analog phones, smartphones, tablets, laptops, or desktop computers; and (3) the availability of internet connections such as broadband, direct subscription line (DSL), mobile data, piso-wifi/net or no internet connections. Consequently, priority proportions for each college/ department that would be a basis for allocating student assistance were obtained. Super Decisions software was utilized to generate the result of the ANP model. Lastly, to evaluate the robustness of the results, a sensitivity analysis was conducted.

Keywords: analytic network process, optimization, allocation, student assistance

Received March 22, 2023; Revised April 25, 2023; Accepted May 07, 2023

Citation: Queeny Eliza D. Saludares, Rhoda A. Namoco, Optimizing Allocation and Distribution of Students’ Assistance Among SUCs Using Analytic Network Process (ANP). Journal of Innovations in Teaching and Learning, 3 (1). 19-22. January 2023. doi:10.12691/jitl-3-1-4

1. Introduction

A good education is one of the most important things an individual can pursue. In the Philippines, the government actively seeks to expand access and participation in higher education by ratifying Republic Act 10931 or the "Universal Access to Quality Tertiary Education Act." This law has built-in mechanisms encouraging increased participation in the program from all socioeconomic classes, especially the poor [1]. Even with free tuition and miscellaneous expenses, it would still require a significant amount of money to take an education, and is strongly felt by low-income families [2]. To address this problem, government and private organizations assist students in different forms, such as scholarships, grants, financial aid, and loan programs. Different types of student assistance use varying indicators and priorities and the COVID-19 outbreak of 2020 caused significant disruption to the world. Schools worldwide shifted to online learning to continue education during the pandemic. Though the intention is good, like in other countries, the implementation of online learning poses some challenges and one of the most apparent issues is access to technology which puts the marginalized at a disadvantage [3].

One of the Higher Education Institutions in the Philippines is the University of Science and Technology of Southern Philippines (USTP) has implemented online learning through its Flexible Learning Program (FLP). To ensure students stay caught up while implementing the Flexible Learning Program, the University developed student assistance in communication/load allowance and tablets for qualified students. The program aims to prioritize students with no gadgets and no internet access due to limited resources at home. However, it is challenging for the administrator to allocate the resources to different colleges and the SHS department of the university due to the limited resources and varying needs of the colleges/department. The decision-making process for granting student assistance can be inaccurate and inefficient since it involves a lot of factors to consider, which can lead to undeserving students receiving assistance or denying assistance to deserving students. The problem that often occurs in decision-making is due to inaccuracies and ignorance of decision-makers [4].

Multi-Criteria Decision-Making problems are common in higher education, such as resource allocation, performance evaluation, budgeting, scheduling, and student selection. MCDM has attracted increasing attention over the past 20 years from both a conceptual and a practical perspective. There has been significant growth in the number of published applications that use a formal approach to problem structuring combined with an analytic method for multicriteria analysis [5]. The analytical hierarchy process (AHP) and analytic network process (ANP) are some of the multi-criteria decision-making methods widely used to solve various issues in the real world due to the consideration of complex and interrelated relationships between decision elements and the ability to apply quantitative and qualitative attributes simultaneously [6]. For instance, in the study of Lusdoc and Namoco [7] where they proposed a method using AHP to provide assistance to incoming first-year high school students in deciding what special program in arts major to take. The results of the study show that most of the students shift to another specialisation due to the late realisation of their field of interest. Hence, the proposed method may be used to provide assistance to incoming first-year high school students in deciding what SPA major to take to avoid shifting majors after their first year in high school.

While AHP and ANP have been combined with other methods in solving multicriteria problems, there are still some studies that utilize ANP method alone. In the study of Gashaw and Jilcha [8], they developed a risk assessment model to prioritise the risks of railway construction projects. A fuzzy analytic network process (FANP) is employed to rank project risks based on the likelihood of project risks and their impact on cost and time. The study results help practitioners and decision-makers understand the main risks of the Ethiopian railway construction projects and take proactive actions accordingly. In the study of Zarei, et. al [9], they developed an Analytic Network Process (ANP) model to assist policymakers in identifying and prioritizing allocation indicators, which are being used or should be used to distribute drugs in short supply among different provinces. The model encompasses the interactions between various indicators and efficiency, equity, and effectiveness paradigms. Accordingly, a set of clusters and elements, which were associated with the allocation of drugs in short supply in Iran’s pharmaceutical system, were detected to develop the model and were then compared in pairs in terms of a specified factor to show the priorities. In another study of Marttunen, et. al [5], they applied ANP to analyze the decision factors of relief allocation. The ANP is used to resolve priority weights of factors and order of importance. The result was applied to Taipei City Street Trees and Park Facilities Mobile Patrol System – Disaster Reporting and Decision Support Subsystem” to produce recommendations for allocation ratio of relief resources by district and road which provide decision support for disaster relief commander and unit heads. Lastly, the study of Tripathi and Vidyathi [10] employed ANP proposed a task allocation model using ANP for cloud resources where the resources were allocated to the task in efficient and justified proportions.

This study seeks to determine the criteria and priority weights that influence the allocations of the student and eventually generate the priority proportion allocation of student assistance for each college and the SHS department of USTP.

2. Methodology

The problem of allocating student assistance among colleges/departments of the university involves a group of decision-makers composed of focal persons from the administration, the deans/heads of the colleges, and the technical team of the implementation of the online platform.

2.1. Identification of the Criteria and Alternatives

The allocation of student assistance will be distributed to the six colleges and the senior high school of the university which are the alternatives of the ANP model. The main criteria in the allocation of student assistance were determined based on the consolidated survey data conducted by the university as agreed upon by three selected decision-makers. The survey results are shown in Table 1. The first criteria are the population of students in each college/department. Second is the technology available to students for online learning such as analog phones, smartphones, tablets, laptops, desktop computers, or no gadgets at all. Lastly, internet access available to students for online learning such as broadband, DSL, mobile data, pisonet/pisowifi, or no internet access.

Table 1. Survey Results

2.2. Structuring the Problem

In this study, the ANP model was constructed using the SuperDecisions software. The five colleges and the senior high school department constituted the alternatives; the population of students for each college, the type of technology and the internet connection available to students formed the criteria.

Figure 1. Cluster and Elements in Super Decisions (R)

2.3. Pairwise Comparison of the Criteria

The pairwise comparison questionnaire was formulated on the basis of Saaty’s scale (1-9) as shown in Table 2.

Table 2. Fundamental scale of absolute numbers

The set of responses provided by each decision-maker was fed into the ANP model. The consistency property of the comparison matrix should be less than 0.1 and the comparisons are acceptable, otherwise, the comparisons need to be revised. The decision-makers' individual priorities were aggregated using geometric means to determine their collective opinion on allocating student assistance.

The group comparisons were synthesized to generate the collective priority scores for allocating student assistance to the respective colleges/department. After all the comparisons were done, sensitivity analysis was conducted on the collective opinion to investigate the most sensitive parameters in the consensus model. By testing the model's output under various scenarios and assumptions, we have identified the critical inputs and understand how changes in these variables impact the overall results.

3. Results and Discussions

The problem in the allocation of student assistance was constructed in the Super Decision software using the ANP method where aggregated pairwise comparisons of the three experts were used as inputs.

Table 3. Aggregated Cluster Comparisons with Respect to Alternatives

The priority function in the Super Decisions can also generate weights of assessment criteria as shown in Figure 2.

Figure 2. Summary of Priorities in Super Decisions

Table 3 shows that DM has given technology the highest priority (0.47423), closely followed by the population (0.37640) and lastly, internet connection (0.14937). Among the subcriteria of Technology, no gadgets have the highest priority (0.42158) followed by analog phones (0.37536) as shown in Figure 2.

This implies that the students’ situation with regard to the availability of gadgets were given more importance, especially among those with no gadgets or those using analog phones only.

Table 4. Synthesized Result of Allocations of Student Assistance

Consequently, Table 4 shows the synthesized result of the allocation proportions of student assistance to the respective colleges/department where CITC has the highest priority allocation (30.17%), followed by CEA (26.48%), CSTE (14.75%), CSM (13.98%), COT (10.40%), and SHS (4.20%).

Furthermore, Sensitivity analysis helps identify the most critical criteria that need to be carefully considered.

Figure 3 shows node sensitivity with respect to the element analog. In the graph, the point at p = 0.5 is the original value by default. The values of p above 0.5, the importance of the node goes up and below it, the importance goes down. This implies that once analog is important enough, it causes a rise in CSM overtaking CSTE, CITC, and CEA. The change in node Analog importance will bring up CSM to number 1 in the ranking which would change the rankings of the other alternatives since CSM has the highest number of Analog users. This means that one must carefully think when giving priorities to this node as it has a big influence on the results.

Figure 3. Node Sensitivity for Analog

4. Conclusion and Recommendations

Allocation is a difficult decision problem. It becomes even more complex when it involves intangible factors. With student assistance, it is usually directed to those in need however, it is difficult to judge between what factors influence each other. The different decision-makers inevitably have subjective judgments about the problems, leading to inconsistency in allocation. ANP is very suitable to analyze the interdependence of decision factors of student assistance allocation. Furthermore, integrating the opinions of a group to determine the importance of different factors leads to a standardized weighting system that would make the allocation of student assistance more consistent and fairer while taking into account all important factors.

In this study, the ANP method was employed to address decision problems in allocation on a specific objective. Nonetheless, this tool is applicable to any community decision-making problem where expert judgment is required and accessible.

Finally, the techniques used in this study are subjective by nature and rely on subjective judgments and opinions. Thus, the final decision represents the personal opinions of the experts/decision-makers as shaped by their knowledge, skills, and experiences. Therefore, it is important to be transparent about the subjective nature of techniques and mitigate potential errors by ensuring that the experts/decision-makers have a diverse range of perspectives and expertise in providing clear criteria for decision-making.

References

[1]   | Nur, A.M., Wazdi, M.F., Harianto, B. and Zaini, M.F., “Implementation of Naive Bayes Algorithm in Analyzing Acceptance of Poor Student Assistance.,” Journal of Physics: Conference Series, pp. 1539, 012018., 2020.
[2]   | Simbulan, N., “The Philippines – COVID-19 and its Impact on Higher Education in the Philippines.,” The HEAD Foundation, 2020.
[3]   | Cuisia-Villanueva, M., and Nuñez, J. “A Study on the Impact of Socioeconomic Status on Emergency Electronic Learning during Coronavirus Lockdown.,” Institute of Education Sciences, 2020.
[4]   | David Shou, T. and Lin, M.-C., “PROCESS (ANP) TO ANALYZE THE DECISION FACTORS OF RELIEF RESOURCES ALLOCATION: A CASE STUDY IN TAIPEI STREET TREES RECOVERY IN POST-DISASTER,” Technology (An International Bi-Annual Journal, pp. 1-8 4(1), 2018.
[5]   | Marttunen, M., Lienert, J., and Belton, V., “Structuring problems for Multi-Criteria Decision Analysis in practice: A literature review of method combinations.,” European Journal of Operational Research, pp. 263(1), 2017.
[6]   | Kheybari, S., Rezaie, F. M., and Farazmand, H. “ Analytic network process: An overview of applications.,” Applied Mathematics and Computation, pp. 367, 124780, 2020.
[7]   | Lusdoc, C. S., and Namoco, R. A., Selecting an SPA (special program in the arts) major for high school students using AHP combined with interest inventory, International Journal of Innovative Research in Education, volume 6 (1), pages 1-11, 2019.
[8]   | Gashaw, T. and Jilcha, K., Risk prioritisation using fuzzy analytic network process: A case of Addis–Djibouti railway construction project, Journal of Multi-Criteria Decision Analysis, Volume 29 (3-4) pp313-334, 2021.
[9]   | Zarei, l., Moradi, N., Peiravian, F., and Mehralian, G.,”An application of analytic network process model in supporting decision making to address pharmaceutical shortage,” BMC Health Services Research, p. 20(1), 2020.
[10]   | Tripathi, A., and Vidyathi, D.P., “Task allocation on cloud resources using analytic network process,” in International Conference on Advances in Computer Engineering and Applications, 2015.