# A Fuzzy Model for Reasoning and Predicting Student's Academic Performance
## 5 Conclusions  
This paper contributes in four aspects:  
1. In the first aspect, this paper provided an investigative study that mostly covers the most recent research works on providing approaches, models, application, and/or evaluating studies for one or more of students' academic performance issues. The investigative study reviewed 32 studies and concluded that these works provided 15 prediction models or approaches, 9 evaluation or analyses studies or models, 4 decision-making systems, and 3 classification models or approaches. Most of the fuzzy methods and techniques have been implemented over the investigated research works, with neuro fuzzy representing the most used method, where it has been used in nine works. 2. In the second aspect, this paper proposed a novel fuzzy model for dealing with students' academic performance behaviors. The proposed model integrated the concept of fuzzy sets with the concept of propositional logic. Although the proposed model uses propositional logic concepts, it differs from the fuzzy logic approach that has been used in pervious works such as [15; 16; 17; 22; 30; 32], as it relies mainly on the fuzzy set concepts, where all data are presented in fuzzy sets, and all the operations are performed based on fuzzy set operations. The role of propositional logic in the approach is transforming the problem into an if-then rule and representing this rule propositionally to obtain [PERSON]'s formula (Equation (4)). This way of incorporating the \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Models** & **MBE** & **NSE** & **RMSE** \\ \hline Our proposed model (FPM)—absenteeism data (First scenario) & \(0.115\) & \(-1.648\) & \(0.249\) \\ \hline Linear regression—absenteeism data (First scenario) & \(0.211\) & \(-2.24\) & \(0.275\) \\ \hline Our proposed model (FPM)—lab exam data (Second scenario) & \(-2.174\) & \(0.812\) & \(2.651\) \\ \hline Linear regression—lab exam data (Second scenario) & \(-1.807\) & \(0.883\) & \(2.331\) \\ \hline The fuzzy logic system of [30] & & & \(7.4833\) \\ \hline The neuro fuzzy model of [31] & & & \(9.287\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: FPM evaluation and comparison. propositional logic with the fuzzy sets concept allows the model to provide a better representation of uncertainty and imprecision in the data with the capability of our model in providing interpretability by generating linguistic rules that can be easily understood by domain experts. * In the third aspect, FPM was designed and built efficiently with low overhead for massively parallel computations. * Finally, in the fourth aspect, the experiment and FPM evaluation were presented using a real data set. In this aspect, this paper achieved three aims: the first achieved aim is in applying FPM on absenteeism, which can be categorized as one of the critical issues in students' academic performance, because there is no clear impact of it to students' academic performance, especially in our case of study where the university has strong regulation against student absenteeism. The second achieved aim is in applying the linear regression model on students' academic performance and comparing the results with FPM results. Finally, for the last achieved aim, this paper proves that the proposed model can work well even if there is no clear uncertainty behavior on the data, i.e., although FPM is a fuzzy model, it can also work on certain data. As a final conclusion, FPM may provide advantages for all those concerned with students' academic performance, such as the students, the teachers, and decision makers, where the model results can be useful for students in forecasting their performances based on what they got, can help teachers in evaluating their assessments methods or teaching methodology, and can help decision makers in making a decision based on examining new scenarios or the factors that may affect the students' performance. Conceptualization, M.O.H. and B.A. ; methodology M.O.H. ; software, B.A. ; validation, M.O.H., B.A. and K.S. ; formal analysis, K.S.