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Volume 15, Issue 02 (February 2026)

Assessment of Students’ Qualification using Integrated Analytical Hierarchy Process

DOI : https://doi.org/10.5281/zenodo.18533140
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Han Ju Gwon, Ri Jin Su, Kim Chol Ryong, Han Yong Gil, Hong In Song, Choe Dae Hyok, 2026, Assessment of Students’ Qualification using Integrated Analytical Hierarchy Process, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 15, Issue 02 , February – 2026

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Assessment of Students Qualification using Integrated Analytical Hierarchy Process

Han Ju Gwon

Faculty of Mathematics, Kim Il Sung University, Pyongyang, DPR Korea

Ri Jin Su, Han Yong Gil, Hong In Song, Choe Dae Hyok

Faculty of Mechanical Engineering, Kim Chaek University of Technology, Pyongyang, DPR Korea

Kim Chol Ryong

Faculty of Quality Management, Pyongyang University of Publication and Printing, Pyongyang, DPR Korea

Abstract – At present, it is very important for universities to improve the comprehensive qualifications of students and social competitiveness. As society develops and the recruitment of students continues to expand, the challenges and difficulties faced by students graduating are also growing. It is a core requirement of the development of university education to evaluate students qualifications objectively and accurately and to give appropriate education accordingly.

Establishing an objective and comprehensive student qualification assessment system to bring education closer to more realistic needs and provide more comprehensive and accurate data when organizations choose students is an important task in university work.

Although several universities have already had considerable success in terms of qualification management, teaching management, and so on, further improvements in the assessment system of students qualification are a real challenge for change and development. On the other hand, when assessing students qualifications in many universities, such an approach often lacks scientific and objective qualities to assess students overall performance using a series of qualitative methods.

In paper, we have implemented an assessment system of students qualification that combines qualitative and quantitative factors using an integrated hierarchical process (IAHP) tool developed in the Net-oriented System Description Language (NSDL) environment, which combines the advantages of Petri nets and object-oriented programming languages to provide scientific, objective, intuitive and flexible evaluation systems.

Keywords – IAHP, Qualification Assessment, Petri Net, Object-oriented Programming Language, Net-oriented System Description Language,

  1. INTRODUCTIONAnalytical Hierarchy Process (AHP) is a comparative assessment method using human perception, which is a decision- making method that selects the best of the various alternatives selected by modeling the actions of the decision-making factors in a hierarchical structure.

    AHP is widely used in various fields of economy, military, society, management, education, medicine, etc due to its ability to deal with qualitative (fuzzy) or quantitative data, logical, systematic, simple and effective analysis.

    Developing a convenient and reliable decision-making tool has great practical significance.

    In the past, AHP tools have been developed and used mainly in a graphical user interface, with a strong spreadsheet and a table-based Execl program, such as XLSTAT, AHP Decision, AHP Solver, AHPcalc, AHP-jar, FuzzyAHP, and Ahp_Calculator[1-8].

    These tools have already been used for hierarchical analysis by using the appropriate input to users based on a hierarchical model built with image or graphical modeling tools. In other words, there was a lack of automatic informational links between the hierarchical model building and the hierarchical analysis module. Finally, when the number of layers, criteria, and alternatives are large and the interaction is complex, the overall analysis is time consuming and laborious and the complexity of users use is unavoidable.

    The characteristics of the proposed IAHP and other AHP tools are shown in Table 1.

    In addition, there are many software development tools that combine the advantages of Petri nets and object-oriented programming languages with good intuition and convenience in modeling in the world [9-16].

    Table 1. AHP tool comparison

    AHP tools Modeling Language AHP Structure Diagram Model Qualitative Criteria Quantitative Criteria
    XLSTAT Excel
    AHP Decision Java
    AHPcalc Excel
    AHP-jar Java
    FuzzyAHP R
    Ahp_Calculator Python
    IAHP NSDL
    • This means supported, no support

    Hence, we developed IAHP in NSDL environment that was developed by combining the advantages of Petri nets and object-oriented programming languages. In IAHP, the hierarchical structure model is described by Petri net diagrams to enhance the intuition and convenience of the AHP tool.

  2. INTEGRATED ANALYTICAL HIERARCHY PROCESS TOOL
    1. Net-oriented System Description Language- NSDLThe NSDL (Net-oriented System Description Language) is an independent software development tool that uses the Petri nets and object-oriented programming language VB based on Microsoft.NET Framework 4.0 libraries.

      The formal representation of NSDL is as follows.

      Where,

      • P is the finite set of places,
      • T is the finite set of transitions,
      • A is the finite set of arcs,NSDL [P, T , A, M , F , , O]

        (1)

      • M is the finite set of markings (includes the object tokens),
      • F is the finite set of functional code,
      • is the finite set of attributes (delay time after firing, firing rate, priority, weight, capacity, type of elements, competition extraction setting and color, etc.) and
      • O is the finite set of user-defined objects modeled with NSDLs functional codes.

      In NSDL, systems are modeled as follows.

      • In the upper level of system,
        • Diagram models are constructed according to the mutuality and the logical action sequence of system components.
        • Graphical User Interface models can be configured according to the demands of the user. (optional)
        • Setting properties of diagram and interface elements
      • In the lower level of system,
        • Standard and user defined functional code model of upper level diagram elements are edited.
        • Dynamic setting properties of diagram and interface elements by functional codes during the simulation.

      In NSDL, we introduced elements such as in/out place terminal, equal place and subsystem, functions such as model library management, code debugging and model compiling to further improve its flexibility, convenience, extensibility and productivity.

    2. Integrated Analytical Hierarchy Process (IAHP) Method
        1. li data-list-text=”2.2.1.”>

      Algorithm diagram of IAHP

      The algorithm diagram of IAHP is shown in Fig. 1.

      Figure 1. Algorithm diagram of IAHP.

      The process of making AHP structure model in tool is as follows.

      Hierarchical Structure Modeling Module

          • User can used one goal layer element (A1), 17 criteria layer elements (B1- K1), one alternative layer element (S1) and one reference arc element to model hierarchical structure, also one sub-criteria and sub- alternative element by subsystem element. These elements are created and stored in the Hierarchical Analytical Library file ahp.ndt.
          • Insert the model library file ahp.ndt into the toolbox.
          • Using the modeling elements in the toolbox, create an AHP structural model that the user needs and add the attributes for the example (Fig. 8-9).
          • For convenience, If there are so many criteria (or alternatives) in one layer can be used as sub-criteria (alternatives).Generation of incidence table, checking and revising for hierarchical structure model
          • Generates incidence table according to the relationship of each element in the hierarchical structure model (Table 1).
          • The user checks the consistency of the built-in hierarchical structure model with the generated incidence table and performs modifications and storage of the built-in model.AHP module
          • Based on the incidence table obtained from the created AHP structural model, we implement AHP algorithm using the VB script language of NSDL.
          • The user can directly input qualitative and quantitative criteria. Also, the NSDLs script language and the best GIS analysis

      tool ArcGIS were combined to input the analysis results for the relevant criteria.

    3. Evaluation of Qualitative CriteriaBased on incidence table, the following AHP algorithm is constructed by VB language of NSDL. Step 1: Judgment Matrix Construction

      When AHP diagram is constructed, judgment matrix to reflect experts subjective assessment is made. The judgment matrix may be made by comparison table.

      Table 2. Meaning of Comparison Values.

      Comparison value Meaning
      1 Equally importance
      3 Moderately importance
      5 Strongly importance
      7 Very strongly importance
      9 Extreme importance
      2, 4, 6, 8 Intermediate values

      Step 2: Calculation of the weight by geometric average.

      Table 3. Random Consistency Index.

      n 2 3 4 5 6 7 8 9 10 11 12
      R.I 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49 1.51 1.53
    4. Evaluation of Quantitative CriteriaThe quantitative criteria may be normalized by simple, max/min and sigmoid method according to the users requirement. The sigmoid transformation method uses the following expression to estimate the importance:
    5.  

      Here, quantitative criteria are inputted by analysis results and normalized by simple method.

  • ASSESSMENT OF STUDENTS QUALIFICATION USING IAHP
      1. AHP Structural ModelingThe AHP structure model for students qualification assessment written in diagram of IAHP is as follows.

        Figure 2. AHP Structure Model.

        C1-What do I now know?

        C2-How can I use this information to meet the problem goal?

        C3-How am I doing as a learner for PBL?

        C4-What are my strengths and weaknesses?

        1. Self-Examination Sub-CriteriaC5-Time Management

          C6-Resources Management

        2. Learning Skills Sub-Criteria
          C7-Consensual Decision Marking
          C8-Conversation and Discussion
          C9-Conflict Resolution and Maintenance
          C10-Team Leadership
        3. Cooperative Skills Sub-CriteriaC11- Learning Attitude

          C12-Turning up for all meetings and being punctual

          C13-Assigned Tasks are completed

        4. Sense of Responsibility Sub-CriteriaC14-Know when there is a need for information

          C15-Identify the information needed to solve a problem

          C16-Be able to locate the needed information

          C17-Use the information to solve a problem effectively

        5. Information Skills Sub-CriteriaC18-Problem Structured Design

          C19-Data Gathering

          C20-Concurrent Thinking

          C21-Idea Generation

        6. Problem Solving Skills Sub-CriteriaFigure 3. Sub-Criteria Element.
      2. Evaluation of Qualitative and Quantitative CriteriaThe incidence table corresponding to the AHP Structural model is shown in Table 4.

        Table 4. Incidence table corresponding to the AHP structural model.

        A1 B1 B2 B3 B4 B5 B6 C1 C2 C3 C20 C21
        1 B1 <>C1 C5 C7 C11 C14 C18 S1 S1 S1 S1 S1
        2 B2 C2 C6 C8 C12 C15 C19 S2 S2 S2 S2 S2
        3 B3 C3 C9 C13 C16 C20 S3 S3 S3 S3 S3
        4 B4 C4 C10 C17 C21 S4 S4 S4 S4 S4
        5 B5 S5 S5 S5 S5 S5
        6 B6 S6 S6 S6 S6 S6
        7 S7 S7 S7 S7 S7
        8 S8 S8 S8 S8 S8
        35 S35 S35 S35 S35 S35
        36 S36 S36 S36 S36 S36
        37 S37 S37 S37 S37 S37

        The judgment matrix of qualitative criteria are shown as tables.

        Table 5. Judgment matrix for goal A1.

        A1 B1 B2 B3 B4 B5 B6 Weight
        B1 1 0.3333333 0.1428571 0.1111111 0.125 0.2 0.0251954
        B2 3 1 0.2 0.1428571 0.1666667 0.3333333 0.045783
        B3 7 5 1 0.3333333 0.5 3 0.1798516
        B4 9 7 3 1 2 5 0.3924806
        B5 8 6 2 0.5 1 4 0.2680967
        B6 5 3 0.3333333 0.2 0.25 1 0.0885927

        Table 6. Judgment matrix for B1- Self-Examination.

        B1 C1 C2 C3 C4 Weight
        C1 1 0.3333333 0.2 0.1428571 0.0550225
        C2 3 1 0.3333333 0.2 0.1177864
        C3 5 3 1 0.3333333 0.2633784
        C4 7 5 3 1 0.5638128

        Table 7. Judgment matrix for B2- Learning Skills.

        B2 C5 C6 Weight
        C5 1 3 0.75
        C6 0.3333333 1 0.25

        Table 8. Judgment matrix for B3- Cooperative Skills.

        B3 C7 C8 C9 C10 Weight
        C7 1 0.2 0.1428571 0.1111111 0.0393486
        C8 5 1 0.3333333 0.2 0.1259583
        C9 7 3 1 0.3333333 0.2696383
        C10 9 5 3 1 0.5650548

        Table 9. Judgment matrix for B4- Sense of Responsibility.

        B4 C11 C12 C13 Weight
        C11 1 3 0.3333333 0.258285
        C12 0.3333333 1 0.2 0.1047294
        C13 3 5 1 0.6369856
        B4 C11 C12 C13 Weight

        Table 10. Judgment matrix for B5- Information Skills.

        B5 C14 C15 C16 C17 Weight
        C14 1 0.3333333 0.2 0.1428571 0.0550225
        C15 3 1 0.3333333 0.2 0.1177864
        C16 5 3 1 0.3333333 0.2633784
        C17 7 5 3 1 0.5638128

        Table 11. Judgment matrix for B6- Problem Solving Skills.

        B6 C18 C19 C20 C21 Weight
        C18 1 3 5 0.3333333 0.2633784
        C19 0.3333333 1 3 0.2 0.1177864
        C20 0.2 0.3333333 1 0.1428571 0.0550225
        C21 3 5 7 1 0.5638128

        The Excel data file for 37 students indicated in SFile is shown in Table 12.

        Table 12. Public assessment marks for 37 students.

        Assessment method Very High: 30, High: 20, Middle: 10, Low: 5must be chosen only one mark for all students.
        p>Name C1 C2 C3 C4 C5 C6 C7 C8 C9 C17 C18 C19 C20 C21
        1 Cha Chol Ho 30 20 30 30 20 20 30 20 30 30 30 30 20 30
        2 William 20 30 30 20 20 30 20 30 20 20 30 20 30 30
        36 Ri Kum

        Son g

        		 10 10 10 10 10 10 10 20 10 20 5 10 5 10
        37 Marie 10 10 10 10 10 10 10 10 10 20 5 10 5 10
      3. Evaluation Total Weight

    The results are analyzed from the overall data. The results are as follows.

    Table 13. Summary of Results.

    Goal B- Criteria Layer C- Criteria Layer S Alternative Layer Ranking
    Criteria Weight Criteria Weight Alternatives Weight
    A1-

    Assessment of Students

    		 B1-Self- Examination 0.0252 C1-What do I now know? 0.0014 S13- David 0.0367 1
    C2-How can I use this 0.0030 S7- Köster 0.0345 2
    Qualification information to meet the problem goal? S8- K. J. Jon 0.0380 3
    C3-How am I doing as a learner for PBL? 0.0066 S16- Vörös 0.0404 4
    S5- U. I. Ri 0.0364 5
    C4-What are my strengths and weaknesses? 0.0142 S1- C. H. Cha 0.0352 6
    S6- Y. M. Pak 0.0372 7
    B2-Learning Skills 0.0458 C5-Time Management 0.0343 S26- S. K. Jo 0.0371 8
    S2- William 0.0274 9
    C6-Resources Management 0.0114 S32- J. H. Kim 0.0236 10
    S15- J. Y. Ra 0.0208 11
    B3-

    Cooperative Skills

    		 0.1799 C7-Consensual Decision Making 0.0071 S33- Henry 0.0363 12
    S9- D. H. Ryu1 0.0373 13
    C8-Conversation and Discussion 0.0227 S10- Kare 0.0212 14
    S11- Marcio 0.0242 15
    C9-Conflict Resolution and Maintenance 0.0485 S14- Cha Ming 0.0360 16
    S18- Dzakiyah 0.0172 17
    B4-Sense of Responsib

    ility

    		 0.3925 C10-Team Leadership 0.1016 S17- Karolina 0.0217 18
    S22- S. M. Ju 0.0175 19
    C11- Learning Attitude 0.1014 S25- U. C. Cheo 0.0202 20
    C12-Turning up for all meetings and being punctual 0.0411 S27- Reisig 0.0267 21
    S21- O. C. Choe 0.0207 22
    C13-Assigned Tasks Are Completed 0.2500 S19- Dawid 0.0181 23
    S31-H. S. Kim 0.0308 24
    B5-

    Information Skills

    		 0.2681 C14-Know when there is a need for information 0.0148 S20- D. H. Ryu 0.0255 25
    S24- John 0.0304 26
    C15-Identify the information needed to solve a given problem 0.0316 S23- J. S. Ri 0.0243 27
    S28- R. H. Kim 0.0363 28
    S4- K. C. Jong 0.0230 29
    C16-Be able to locate the needed

    information

    		 0.0706 S29- M. S. Kang 0.0186 30
    B6-Problem Solving

    Skills

    		 0.0886 C17-Use the information to solve the given problem

    effectively

    		 0.1512 S30- David 0.0218 31
    S3- S. H. Han 0.0362 32
    S12- K. H. Choe 0.0244 33
    C18-Problem Structured Design 0.0233 S35- K. H. Pak 0.0169 34
    C19-Data Gathering 0.0104 S36- Ri Song 0.0169 35
    C20-Concurrent Thinking 0.0049 S34- C. Han 0.0151 36
    C21-Idea Generation 0.0499 S37- Marie 0.0152 37

    As shown in the Table 13, IAHP can be solved for AHP problem having so many alternatives.

  • CONCLUSION

In this paper, we have considered about method to assess the students qualification intuitively and coveniently by

using an integrated hierarchical analysis tool (IAHP) developed in the network-oriented system description language NSDL environment, which is developed by combining the advantages of Petri nets and object-oriented programming languages.

With the introduction of File Alternative Element (SFile), AHP structural model can be constructed more conveniently, simply and effectively in the case of so many alternatives

Abbreviations

IAHP: Integrated Analytical Hierarchy Process
NSDL: Net-oriented System Description Language
VB: Visual Basic
PBL: Problem-Based Learning

Acknowledgment

The authors would like to thanks Prof. Dr. Kim Kwan Sik who is a boss in the development of NSDL, Won Chang Son, Choe Yong Su and Kim Song Hyok teachers who gave valuable guidance to the writing of the paper.

Funding

This work was partially supported by University of National Economy

Conflicts of Interest

The authors declare no conflicts of interest.

REFERENCES

  1. Meryem Tahri ect.6, New Fuzzy-AHP Matlab based graphical user interface (GUI) for a broad range of users: Sample applications in the environmental field, Computers and Geosciences, 2021, https://doi.org/10.1016/j.cageo.2021.104951
  2. Manoj Mathew ect.2, A novel approach integrating AHP and TOPSIS under spherical fuzzy sets for advanced manufacturing system selection, Engineering Applications of Artificial Intelligence, 2020, https://doi.org/10.1016/j.engappai.2020. 103988
  3. Mohamed Hanine, Omar Boutkhoum, Abdessadek Tikniouine1 and Tarik Agouti, Application of an integrated multicriteria decision making AHPTOPSIS methodology for ETL software selection, SpringerPlus (2016) 5:263, DOI 10.1186/s400640161888z
  4. Marcio Pereira Basílio etc.4, A Systematic Review of the Applications of Multi-Criteria Decision Aid Methods (19772022),Electronics 2022, 11, 1720, https://doi.org/10.3390 /electronics 11111720
  5. Ngoc-Ai-Thy Nguyen etc.4, Selection of Cold Chain Logistics Service Providers Based on a Grey AHP and Grey COPRAS Framework: A Case Study in Vietnam, Axioms 2022, 11, 154, https://doi.org/10.3390/axioms11040154
  6. Wolfgang Reisig, Understanding Petri Nets, Springer-Verlag Berlin Heidelberg, 2013, DOI 10.1007/978-3-642-33278-4
  7. Fabrice Kordon, Daniel Moldt, Introduction to the special issue from Petri Nets 2016, Science of Computer Programming, 2018, https://doi.org/10.1016/j.scico.2018.01.007
  8. Rene David, Hassane Alla, Discrete, Continuous and Hybrid Petri Nets, Springer-Verlag Berlin Heidelberg, 2010, DOI 10.1007/978-3-642-10669-9
  9. Reggie Davidrajuh, Modeling Discrete-Event Systems with GPenSIM, SpringerBriefs in Applied Sciences and Technology, 2018, https://doi.org/10.1007/978-3-319-73102-5
  10. Lawrence Cabac, Michael Haustermann, David Mosteller, Software Development with Petri Nets and Agents: Approach, Frameworks and Tool Set, Preprint submitted to Science of Computer Programming, December 12, 2017, 27 pages, https://doi.org/10.1016/j.scico. 2017.12.003
  11. András Vörös, Dániel Darvas, Ákos Hajdu, Industrial applications of the PetriDotNet modeling and analysis tool, Preprint submitted to Science of Computer Programming, September 11, 2017, 24 pages, http://dx.doi.org/10.1016/j.scico.2017.09. 003