Enhanced FMEA Methodology for Evaluating Mobile Learning Platforms Using Grey Relational Analysis and Fuzzy AHP
Source abstract
Abstract
This study addresses a significant problem: it is difficult to choose a suitable mobile learning platform effectively since many learning platforms are readily available for users. For this purpose, the study proposes an efficient way to rank and choose the most suitable mobile learning platform by integrating risk analysis and multi-criteria decision-making methods. The selection of a suitable mobile learning platform is challenging due to the vast collection of available platforms. Traditional decision-making approaches often struggle to manage the inherent uncertainty and subjectivity in platform evaluation. To address this, we propose an enhanced methodology that combines grey relational analysis (GRA) and fuzzy analytic hierarchy process (FAHP), leveraging their complementary strengths to provide a robust and adaptive solution. The study employs ISO/IEC 9126 software quality standards to locate the most suitable mobile learning platform. FMEA is based on three risk factors: occurrence, severity, and detection. The fuzzy analytical hierarchy process (FAHP) is used to determine the relative weight of each risk factor to identify the grey risk priority number that can be calculated for each criterion. Mobile learning platforms are then ranked based on their grey risk priority number. The method was applied to five widely used mobile learning platforms with three decision-makers. In addition, the multi-criteria decision-making software was developed to aid users, educators, and administrators in their decision-making processes. The integrated FMEA-GRA-FAHP technique, using ISO/IEC 9126 standards, provides an effective way of locating the most suitable mobile learning platform and ranking them according to their reliability. This research is believed to be the only study applying an integrated FMEA-GRA-FAHP approach to evaluate the risks and quality of mobile learning platforms. The unique approach overcomes certain limitations of the standalone methods such as FMEA and FAHP, making it a valuable tool for identifying the suitability of mobile learning platforms. In addition, the study underscores the importance of inclusivity and equity in ensuring high-quality education and creating an environment conducive to lifelong learning for all.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Multi-Criteria Decision Making
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Information Systems, Near East University, Lefkoşa 98010, Turkey
Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK
References (55)
Show source-provided references
1. E-and M-Learning: A comparative study
2. Chandran, S. (2010, January 6–8). Application and Impacts of Mobile Learning: A Case Study in Technology for Sustainable Distance Learning. Proceedings of the Eighth International Network Conference (INC 2010), Heidelberg, Germany.
3. Determining e-learning success factor in higher education based on user perspective using Fuzzy AHP10.1051/matecconf/201815403011 ↗
4. A Fuzzy AHP Approach to Select Learning Management System10.7763/IJCTE.2015.V7.1009 ↗
5. A Review of Software Quality Models for the Evaluation of Software Products
6. Quality characteristics and metrics related to m-learning process
7. The role of failure mode and effects analysis in improving the quality performance of dairy laboratories10.1111/jfs.12364 ↗
8. Pomerol, J.-C., and Barba-Romero, S. (2000). Multicriterion Decision in Management, Springer.10.1007/978-1-4615-4459-3 ↗
9. The analytic hierarchy process (AHP)
10. Maintenance policy selection using fuzzy failure modes and effective analysis and key performance indicators10.1504/IJPQM.2018.094760 ↗
11. Applications of the extent analysis method on fuzzy AHP10.1016/0377-2217(95)00300-2 ↗
12. A Study on Fuzzy AHP Method and Its Applications in a Tie Breaking Procedure
13. Software Failure Modes and Effects Analysis10.1109/TR.1979.5220578 ↗
14. Identifying and evaluating enterprise architecture risks using FMEA and fuzzy VIKOR10.1007/s10845-014-0880-0 ↗
15. Failure mode and effect analysis using fuzzy analytic hierarchy process10.1504/IJPQM.2014.064807 ↗
16. Risk evaluation of green components to hazardous substance using FMEA and FAHP10.1016/j.eswa.2008.08.031 ↗
17. Multi-Criteria Decision Analysis Approaches for Selecting and Evaluating Digital Learning Objects10.1016/j.procs.2016.09.398 ↗
18. Prioritizing the CSFS of Cloud-Based M-Learning Analytic Hiearchy Process and Blackboard Case Study10.21608/ijlms.2023.299475 ↗
19. Naveed, Q.N., Qahmash, A.I., Qureshi, M.R.N., Ahmad, N., Abdul Rasheed, M.A., and Akhtaruzzaman, M. (2023). Analyzing Critical Success Factors for Sustainable Cloud-Based Mobile Learning (CBML) in Crisp and Fuzzy Environment. Sustainability, 15.10.3390/su15021017 ↗
20. Meng, X., Li, S., Malik, M.M., and Umer, Q. (2022). Machine-Learning-Based Suitability Prediction for Mobile Applications for Kids. Sustainability, 14.10.3390/su141912400 ↗
21. Selection of Learning Apps to Promote Critical Thinking in Programming Students using Fuzzy TOPSIS
22. Alonso-Martínez, D., Jiménez-Parra, B., González-Álvarez, N., Godos-Díez, J.-L., and Cabeza-García, L. (2019). Taking Advantage of Students’ Passion for Apps in Sustainability and CSR Teaching. Sustainability, 11.10.3390/su11030779 ↗
23. Parsons, D., and Hokyoung, R. (2006, January 12–14). A framework for assessing the quality of mobile learning. Proceedings of the International Conference for Process Improvement, Research and Education, Amsterdam, The Netherlands.
24. A Multi-Criteria Decision Making to Rank Android based Mobile Applications for Mathematics
25. Mobile learning technologies and educational applications
26. An ISO 9126 Based Quality Model for the e-Learning Systems
27. Idri, A., Moumane, K., and Abran, A. (2013, January 2–5). On the Use of Software Quality Standard ISO/IEC9126 in Mobile Environments. Proceedings of the 2013 20th Asia-Pacific Software Engineering Conference (APSEC), Bangkok, Thailand.10.1109/APSEC.2013.12 ↗
28. A Comprehensive Usability Measurement Tool for m-Learning Applications10.1109/TE.2023.3347191 ↗
29. A Framework for Evaluating the Usability of Mobile Learning Applications in Universities10.46243/jst.2022.v7.i05.pp42-59 ↗
30. A design requirements framework for mobile learning environments10.4304/jcp.2.4.1-8 ↗
31. Computer supported collaborative learning using wirelessly interconnected handheld computers10.1016/j.compedu.2003.08.005 ↗
32. MLearning scaffolding model for undergraduate English language learning: Bridging formal and informal learning
33. Ubiquitous knowledge construction: Mobile learning re-defined and a conceptual framework10.1080/14703290902843828 ↗
34. Elucidating user behavior of mobile learning: A perspective of the extended technology acceptance model10.1108/02640470710829569 ↗
35. Proposal of a Mobile Learning Preferences Model10.3991/ijim.v4i4.1445 ↗
36. A review of models and frameworks for designing mobile learning experiences and environments10.21432/T2V616 ↗
37. Evaluating the quality of software in e-book using the ISO 9126 model
38. Selecting the optimal ERP software by combining the ISO 9126 standard and fuzzy AHP approach10.7903/cmr.10 ↗
39. Integrating FAHP and TOPSIS to evaluate mobile learning applications for mathematics10.1016/j.procs.2017.11.214 ↗
40. A new software quality model for academic information systems ‘case study e-learning system’
41. García Aguirre, P.A., Pérez-Domínguez, L., Luviano-Cruz, D., Solano Noriega, J.J., Martínez Gómez, E., and Callejas-Cuervo, M. (2021). PFDA-FMEA, an Integrated Method Improving FMEA Assessment in Product Design. Appl. Sci., 11.10.3390/app11041406 ↗
42. Personal Information Security Risk Assessment for E-Waste Recycling Based on Fuzzy–GRA–TOPSIS and FMEA10.1109/TEM.2024.3395649 ↗
43. Altubaishe, B., and Desai, S. (2023). Multicriteria Decision Making in Supply Chain Management Using FMEA and Hybrid AHP-PROMETHEE Algorithms. Sensors, 23.10.3390/s23084041 ↗
44. Vijayaraghavan, G. (2003). A Taxonomy of the E-Commerce Risks and Failures. [Master’s Thesis, Florida Institute of Technology].
45. Botella, P., Burgués, X., Carvallo, J.P., Franch, X., Grau, G., Marco, J., and Quer, C. (2004). ISO/IEC 9126 in practice: What do we need to know. Software Measurement European Forum, Data Processing Organization S.r.l., Via Valentino Mazzola.
46. Multicriteria analysis with fuzzy pairwise comparison10.1016/S0888-613X(99)00025-0 ↗
47. Application of grey relational analysis with fuzzy AHP to FMEA method
48. Chua, B.B., and Dyson, L.E. (2004, January 5–8). Applying the ISO 9126 model to the evaluation of an e-learning system. Proceedings of the ASCILITE, Perth, Australia.
49. Tedarik Zinciri Yönetiminde Bulanık AHP yöntemi kullanılarak tedarikçilerin performansının ölçülmesi: Otomotiv Yan Sanayiinde bir uygulama
50. Using the fuzzy analytic hierarchy process for selecting wastewater facilities at prefecture level
51. Analysis of the Educational Application Users Satisfaction Levels by Using the Integrated Entropy Weight VIKOR Method10.14429/djlit.43.02.18383 ↗
52. Measuring User Satisfaction of Educational Service Applications using Text Mining and Multicriteria Decision-Making Approach10.3991/ijet.v16i17.22939 ↗
53. A Decision Tool for Selecting a Sustainable Learning Technology Intervention
54. Toward Selection of Trustworthy and Efficient E-Learning Platform10.1109/ACCESS.2021.3114150 ↗
55. Hsu, C.W., Hu, A.H., and Wu, W.C. (2008, January 19–22). Using FMEA and FAHP to risk evaluation of green components. Proceedings of the 2008 IEEE International Symposium on Electronics and the Environment, San Francisco, CA, USA.