## 9 Conclusions  
The combination of FMEA-GRA-FAHP methods proved to be an effective technique in evaluating the quality of a mobile learning platform as well as ranking mobile learning platforms in order of their reliability. Multi-criteria decision-making techniques play a vital role in the quality evaluation and testing of mobile learning platforms. The last COVID-19 pandemic situation has triggered rapid growth in the use of mobile learning platforms, especially in the educational sector. Evaluating such platforms by employing robust and reliable multi-criteria decision-making techniques plays a vital role in selecting proper mobile learning platforms and improving the vulnerabilities, hence providing better management of online learning. This study utilizes the FMEA-GRA-FAHP technique in prioritizing risk factors. It is one of the most efficient weight-assigning MCDM techniques. FMEA is used to evaluate the potential risk associated with each platform based on the adopted framework which is coined from the ISO/IEC 9126 standard. The results of the FMEA are prioritized using the FAHP weights to assign risk priority numbers and, in turn, rank mobile learning platforms in their order of reliability from a quality perspective. Other multi-criteria decision-making techniques, such as AHP, ANP, PROMETHEE, etc., can be integrated to further the selection and assessment of MLPs. Also, additional MLP samples can be added, and increasing the number of experts could be recommended for future work. FMEA-GRA-FAHP is an efficient tool for evaluating the potential risk of a system considering the fact that the FAHP is able to compensate for the drawbacks of the traditional FMEA technique. This makes it recommendable in various industries, including the aerospace industries, electronic industries, and safety management. These are a few of the areas where the risk of failure is highly prioritized to maintain safety. Educators can leverage the use of the FMEA-GRA-FAHP approach for MLP ranking to ease the learning process and use the most efficient MLPs from a pool of available options. Institutions can also leverage the use of this technique in the teaching process to enable easier and more efficient means of learning for students and a more efficient means of teaching for lecturers. Some potential limitations of the study could be listed as follows:  
This study is delimited to enhancing FMEA methodology through the use of GRA-FAHP methods by assigning different weights to risk factors and ranking mobile learning platforms. The traditional FMEA, GRA-FMEA and the proposed methodology are also compared and contrasted in terms of robustness. However, in the future, different risk assessments and multi-criteria methods could be incorporated to perform fine-tuning of the results. In this study, the same importance was assigned to each expert judgment, as for future work, different weights could also be assigned to expert judgments. The evaluation framework emphasizes technical aspects of mobile learning platforms; however, researchers acknowledge the importance of non-technical aspects such as user engagement, accessibility, and equity for the pedagogical effectiveness of mobile learning platforms to enhance user experiences and outcomes in the educational milieu. Therefore, the crucial attributes should be considered for further evaluation. Another limitation of the study is that the number of experts is limited to three. For future work, the inclusion of more experts will yield a more representative sample of judgments. The same issue is also valid with the number of alternatives, including more mobile learning platforms for evaluation, which will improve the representability of the sample. Corresponding author, S.B., contributed to the manuscript's theoretical development, analysis, interpretation, and writing. Conceptualization, S.B. ; Data curation, S.B. ; Formal analysis, O.A.I. ; Investigation, S.B. and O.A.I. ; Methodology, S.B. ; Project administration, O.A.I. ; Resources, S.B. and O.A.I. ; Software use, O.A.I.