## 1 Introduction
In this research, we focused on the quality-in-use characteristics of the ISO/IEC 9126 standards with the consideration that the evaluated metrics should also be accessible for evaluation by the experts. The scope of the evaluation is limited to the following characteristics. The linkage between metrics and MLPs is given as follows:  
Suitability under functionality is the main characteristic of which MLP performs it is required tasks, such as meeting the specified educational needs, such as course delivery, user interaction, and content management. Understandability, learnability, operability, and attractiveness under the main metric usability indicate the extent to which learners interact with MLP, encompassing these attributes, such as whether users understand MLP effortlessly and how users learn to use MLP easily. This metric focuses on user-centered aspects of the MLPs. This constitutes another rationale for selecting ISO/IEC 9126 standards for the evaluation framework of the study. Since this aspect is believed to have a critical impact on the learning performances. Usability metrics according to ISO 9126 are used for predicting the extent to which the mobile learning platforms in this study can be understood, learned, operated, and attractive. Fault tolerance under reliability's main characteristics shows to what extent MLP could perform correctly and consistently with minimized failures and handle the errors well. Time behavior under efficiency's main characteristic is to what extent MLP performs smoothly with minimal delay. For the reasons mentioned above, MLPs can be evaluated using the ISO/IEC 9126 software quality characteristics [6]. Risk management and quality reliability in laboratories require the use of failure modes and effects analysis (FMEA) in its dynamic procedure [7]. FMEA was applicable in two different stages; in the first phase, the potential failure mode was selected, and assessment and analysis were carried out on the probable risks. In order to identify underlying failures and their effects as risks, risk priority numbers (RPNs) were employed. In the later stage, plans are proposed to implement and quantify those failures' impact. While in the series of making choices out of varieties of decision-making, the popular AHP approach is very well known and is also, in fact, the most utilized approach for these types of decision-making processes in which application rankings are based on certain attributes that are expressed with the help of the utilization of distinctive scales. In this study, we aimed to improve the FMEA risk assessment methodology by combining it with GRA (Grey relational analysis) for making decisions based on incomplete information and FAHP (fuzzy analytic hierarchy process) to assign varying weights to risk factors as occurrence, severity, and detection, which was not possible with conventional FMEA and ranking alternatives. GRA is highly helpful when making decisions in situations when there is uncertainty and inadequate information. It is expected that some platform performance measures will have incomplete or unclear data when evaluating MLPs. Since GRA compares alternatives' relative performance across multiple factors, it effectively handles alternatives' similarity in such settings, making it a great fit for our study. In GRA, results do not need to be normalized. The FAHP technique is used when dealing with language issues and subjective evaluations. When expert reviews of MLPs incorporate subjective evaluations of usability, usefulness, dependability, and efficiency, this becomes significant. FAHP allows us to generate a more realistic and trustworthy evaluation by capturing the inherent ambiguity and imprecision in these expert opinions. FAHP is designed to handle complex decision-making scenarios where a large number of factors are graded using expert opinion. Using fuzzy scales and pairwise comparisons, professionals can systematically create weights for each criterion using FAHP, which represents the relative relevance.