## 2 Review Methodology
### Research Question (RQ) Identification  
The framed SLR aimed to provide and assess the empirical evidence from the studies that deployed ML or AI models in predicting the student academic performance. The  
Figure 1: Implemented systematic literature review protocol. motivation behind developing these RQs relied on the real focus of this systematic review. The initial process of SLR and the perfect base to perform the SLR was formed with the exact definition of the RQs. Five principal RQs were framed to explicate the exact idea of this systematic review. The research questions were framed such that the articles responding either partially or perfectly alone stands in the filter of articles. These sustained articles proceeded further for evaluation to describe the concepts of machine learning application in the field of educational mining. RQ1: What are the different ML models/techniques used for student academic performance prediction? The aim of this research question is to understand the models that have been implemented for predicting student academic performance. The models/techniques used are analyzed to obtain an insight of the most frequently used methods, new proposed methods, and methods that provide better results or performance metrics. The reader of the article will be able to find a list of such methods that are utilized and proved by various researchers so that the budding researchers can adopt new ideas from existing works. RQ2: What are the various estimation methods/metrics used? What is the performance measure used to appraise the performance of the models in the described problem area? This research question has been framed with the target to identify the metrics that have been used to measure the precision of the developed model. Furthermore, it aims to assess the way the referred articles speak of their credibility and accuracy in proving the purpose of the developed model. Even though the metrics used for analyzing the machine learning models are standardized, the values obtained by various methods on different databases speak to the importance of each feature and its contribution towards the performance metrics discussed in the relevant articles. RQ3: Are there any datasets and collection methods; if they exist, is their usage specified? RQ3 responds with the quality of analysis made, so that the size of datasets reveals their proportion of reliability. The datasets used in the referred articles are considered as a research question to show the features taken under consideration and the importance level of each feature towards arriving at the best model and better performance measure. RQ4: Are there any guidelines on the number of features considered or the features used? This research question aims at finding the data collected and the source and identifying the effective features in the dataset. The guidelines regarding the features used provide an idea during the feature extraction process of developing an ML-based model or a prototype. These guidelines discussed in different articles of multiple features show the insights that each author has attained during their research. The readers will be able to identify the importance of choosing the features or the justification provided by the authors in eliminating the consideration of certain features. RQ5: Are enough comparisons made to prove the reliability of the proposed model? The models that are proposed in the cluster of articles are to be segregated and selected for a further examination based on the comparative measures that were taken to validate the proposed works as adequate and substantial and that they surpass the previously presented works in the pertinent literature. Even though every model when proposed seems to prove its innovation, solid proof is needed to say that the proposed model is genuine. Hence, a considerable number of methods that were existing should be taken into consideration and analyzed for improvement in the performance measure of the proposed system.