# Predicting Academic Performance Using an Efficient Model Based on Fusion of Classifiers
## 2 Literature Review  
EDM is burgeoning due to the massive growth of educational resources, the internet, and the usage of online tools to impart education [12]. Consistent research efforts are being made to improve the quality of educational tools. This section provides an overview of the factors that may affect students' academic progress and technologies that are helpful in making predictions regarding students' academic progress. A systematic review has been conducted to determine factors that affect student performance through information mining procedures [13]. The study handled numerous subjects, one of which was to recognize the significant characteristics which can be utilized in anticipating student performance. The results indicated that the internal assessments and aggregate evaluation points are the most incessant qualities utilized for predicting academic performance. Additionally, other significant properties were identified including personal and inner appraisal, previous record, extra-curricular activities, and social attributes. The decision tree and neural networks were found as the most regularly utilized information-digging strategies [14]. Another research study [15] considered the cumulative grade point average as a prominent factor for measuring student performances in each semester. Additionally, the study also interprets normal class tests and assignments, previous academic failure, and study duration as the appreciable factors for predicting student performances. In [16] author stated that academic attainments are somehow linked with students' extracurricular activities and presented the presence of the student in a class as the strongest predictor for academic performance. The study [17] found that family attributes and academic attributes were the deciding factors for prediction. The cumulative grade point average of students and their external and internal assessments marks were also the most frequently used attributes by the researchers. Another research [18] also showed the performance prediction of fourth-year undergrad students using pre-university marks and considering marks obtained in courses of the second year. They only considered grades for performance prediction and ignored the family and socio-economic attributes of the students. A comprehensive analysis of supervised machine learning techniques was conducted and applied to predict students' performance in the examination. They considered different factors including demographics and social interest to predict students' expected score in the final term as well as students at risk [19]. A survey study was conducted using a sample of 1500 United State students to identify the impact of the COVID-19 pandemic on higher education. It has been revealed through analysis and a classification model that the shocks related to health and economics brought about by COVID-19 varied by socioeconomic factors [20]. Another study gathered students' pre-university marks, first and second-year marks through a large-sized sample and applied a predictive model on it to envisage students' CGPA at the final semester [21]. Table 1 shows the set of attributes used at the secondary level to predict students' academic performance. Another research study [22] proposed a predictive investigation framework to gauge the satisfaction level of university students regarding an online summer program. The students were given a questionnaire, consisting of questions related to socioeconomic, demographics, and some other indicators. Regression and ANOVA analysis were applied in that prediction model to interpret learners' interaction. Data mining approaches are being widely used by academic analysts to assess the effectiveness of education by processing and interpreting the huge volumes of data [23]. For the timely completion of students' degree requirements, their future performance was predicted based on their academic record. A novel machine learning method was used to predict the students' performance in a degree program [24].