## 6 Discussion and Conclusions  
This study applied AI techniques, including the Bayesian network and fuzzy logic, to create a novel hybrid student model, in order to offer adaptive instruction and personalized support in a web-based intelligent tutoring system. A web-based intelligent tutoring system called FB-ITS was designed and implemented in the present study. According to the findings, the evaluation of the proposed system showed significantly satisfactory results and positive effects in terms of the students' academic performances, where the results revealed that the students who used FB-ITS to learn Excel had higher academic performances than students who studied Excel with the ITS using the Bayesian network and fuzzy logic separately. By comparing the performance of the presented system with the traditional e-learning, it was concluded that the students \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Dependent Variable: Post-Test**} \\ \hline
**Group** & **Mean** & **Std. Deviation** & **N** \\ \hline A3 & 82.95 & 18.587 & 20 \\ B & 64.33 & 26.256 & 60 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Descriptive Statistics of groups A3 and B. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Dependent Variable: Post-Test**} \\ \hline
**Source** & **Type III Sum of Squares** & **df** & **Mean Square** & **F** & \(p\) & **Partial Eta Squared** \\ \hline Corrected Model & 14,834.302a & 2 & 7417.151 & 15.188 & 0.000 & 0.283 \\ Intercept & 74,228.653 & 1 & 74,228.653 & 151.992 & 0.000 & 0.664 \\ Pre-test & 9633.084 & 1 & 9633.084 & 19.725 & 0.000 & 0.204 \\ Group & 10,300.490 & 1 & 10,300.490 & 21.092 & 0.000 & 0.215 \\ Error & 37,604.599 & 77 & 488.371 & & & \\ Total & 433,193.331 & 80 & & & \\ Corrected Total & 52,438.900 & 79 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Tests of Between-Subjects Effects. who studied with FB-ITS had a higher mean value on academic performance than the students who studied using the traditional e-learning system. The results of the present work support the results of other studies that used AI techniques to develop intelligent educational systems [1; 14; 38; 45]. Moreover, in terms of the level of time taken to perform the post-test, FB-ITS recorded the lowest mean time value compared with the other two versions. The students who used the FB-ITS needed less time on average to perform the post-test than the students who used the traditional e-learning system. In a similar study [36], the adaptive intelligent system has a good performance compared with another educational system in terms of the time needed to read each domain concept. The major contribution of the presented study is the development of a new model. It is a novel hybrid student model that combines Bayesian networks and fuzzy logic techniques, and which is designed to promote adaptation and personalization in educational systems. In this manner, the presented student model helps the students who already have prior knowledge about the domain to save time and effort during the learning process. Moreover, it tracks the changes of the knowledge level of the students and dynamically adapts the learning material accordingly. The other contribution comes from the development of a unique online intelligent tutoring system named FB-ITS by the researcher using Microsoft Visual Studio and SQL Server Management Studio. FB-ITS can provide adaptation based on a student's knowledge level using the adaptive navigation support, including the adaptive learning techniques, such as link annotation and link hiding, where the link annotation is used to highlight each topic with an appropriate color. Link hiding is used to hide any links for topics that are not yet ready to be learned. In addition, the proposed ITS framework can be used as a reference model to develop instances of intelligent tutoring systems by focusing on different views of the domain model, as well as the adaptation model.