## 5 Results and Discussions
The model differentiates the trainable and non-trainable parameters after word embeddings, as specified in Table 5. As a training result, GRU gave 87% accuracy when the core course contents were tested for each technical term along with the set target domain followed by LSTM with 80% accuracy and RNN with 42% accuracy. According to [PERSON] et al., both GRU and LSTM perform similar with respect to accuracy [44]. Since GRU performed better with maximum accuracy for the considered training of the data set, every elective course in \(e\in E\) is predicted for the target domain \(d\in D\) using GRU. The algorithm used by [PERSON] et al. produced 86% accuracy in recommending elective courses [8] and hence the proposed elective course recommendation using the GRU model has outperformed with better accuracy of 93% with the computational complexity of \(O(2^{n-1})\). After the prediction of domains for each elective course, it is necessary to identify the domain expertise of a student \(s\) such that elective courses under the domain expertise could be recommended. Every student is identified with the CGPA of the courses completed for the quantified domains. The domain with a maximum grade is considered as the domain expertise of the student. For a student \(s\), the domain expertise is considered as \(d_{1}\) since the student has scored maximum CGPA in it as specified in Figure 5. Once the domain expertise is identified, elective courses under the domain \(LE_{1}\) are recommended to student \(s\). The recommendations of elective courses are validated by comparing the grades of the student with and without recommendation. Table 6 depicts the comparative analysis of the elective course selection patterns of the students with recommendation, and students without recommendations. It also specifies the representation of the elective course completion statistics of a student \(s\) with and without recommendation. Figure 6 illustrates the grades a student \(s\) has scored in the respective domains with the CGPA scored per each domain. The number of elective course credits registered and the corresponding grades scored in each domain is analysed with and without elective course recommendation, and the experimental results shows that the student s has scored higher grades after a recommendation on his expertise domain, as specified in Figure 6. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Domain ID**} & \multicolumn{3}{c}{**Number of Effective Courses Completed**} \\ \cline{2-3}  & **Without Recommendation** & **With Recommendation** \\ \hline \(d_{1}\) & 3 & 29 \\ \(d_{2}\) & 3 & 4 \\ \(d_{3}\) & 4 & \\ \(d_{4}\) & 2 & \\ \(d_{5}\) & 3 & 3 \\ \(d_{6}\) & 14 & \\ \(d_{7}\) & 3 & 4 \\ \(d_{8}\) & 8 & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Effective course selection patterns and the CGPA scored. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{1}{c}{**Total**} & \multicolumn{1}{c}{**Trainable**} & \multicolumn{1}{c}{**Non-Trainable**} & \multicolumn{1}{c}{**Training**} & \multicolumn{1}{c}{**Test**} \\  & **Parameters** & **Parameters** & **Parameters** & **Accuracy** & **Accuracy** \\ \hline RNN & 180,902 & 180,902 & 0 & 0.42 & 0.50 \\ LSTM & 721,802 & 721,802 & 0 & 0.8034 & 0.85 \\ GRU & 541,502 & 541,502 & 0 & 0.8712 & 0.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the data models during training. After a recommendation, student s have registered for 29 credits of elective courses under the domain \(d_{1}\) which makes the student expertise more on a particular domain. Based on the comparative analysis of the elective course selection patterns, only 13% of students without recommendation have selected about 11 to 25 credits of elective courses from the same domain.