## 3 Results and Discussions
### Feature Description and Usage  
Even though RQs 4 and 5 speak of the feature mentions and their usage in the research articles under study, they play a central role in deciding the features to be concentrated and the way data collection can take place in future in order to proceed with effective research on academic performance prediction.  
Some of the features mentioned as a group in the research articles are social, demographic, personal, academic, extracurricular, and previous academic record. Even though the categorization was made in this common aspect, the individual components contributing to the research conducted varied in accordance with the educational institution, their geography, and their previous experience with students. The data do not only limit themselves to category of education institution or mode of study; it varies from online education university, online education courses, regular academic universities, colleges, schools, and others.  
Figure 8: Box and whisker plots—performance metrics.  
The category of features to their accuracy and their importance is shown in Figures 9 and 10. Figure 9 includes the details of the importance given to each feature category. Out of the scored articles, around 39% of articles contributed with three-feature set importance, dual-feature set importance is given by 30% of articles, and one-feature set is given importance by 32% of articles. Additionally, it is noted that minimal accuracy stays when considering only the behavioral features. However, the accuracy stays equal with demographic-academic dual feature and with academic features contributing to an average accuracy when all the three features are considered.  
Based on the features described in the articles, they are broadly classified based on demographic, academic, and behavioral features. Out of the selected 56 articles of study, only 34 articles sustained delivering their features and their nature of importance. Table 9 specifies the detailed list of parameters that have been adopted in the studies. The quality assessment score is contributed as a response received from RQ 6 as shown in Table 4. The number of comparisons made in the proposed model to prove that its excellence against other models was considered. The taxonomy broadly explains the models used in each category of ML algorithms.  
Even though each ML-based algorithm works on the same principle in its own way, it behaves differently for the data used. The ML algorithm must be trained and used for the specific data to be fit enough for classification or prediction. Hence, the models that were used are also considered for evaluation, as this can potentially provide a fair idea to the future researchers on the process of proceeding with their research in the domain of academic assessment and prediction.  
This systematic review was constrained to ML algorithms used in the domain of academic performance prediction. It was found that various algorithms have been used; some algorithms that were not used and applied were not considered for testing in this domain of research. Hence, the future researchers can consider those algorithms used in the previous studies as benchmarks and proceed with the unused ML models to showcase the results. Additionally, a bird's-eye view on the related disciplines of ML, namely AI, DL, statistics, and data mining with respect to this domain of research, can be done in order to devise new valuable ideas and provide the attempts to implement them.