## 4 Recommendation of elective Courses  
Once the domain of each elective course is predicted, it has to be recommended to each student by identifying their domain expertise, as specified in Algorithm 2. Let \(AH_{s}\) denote the academic history of a student \(s\) which has the list of completed courses of the student from the university curriculum. Completed courses that falls under the domain \(d\) in the subgroup \(SG\) are considered for evaluating the domain expertise of a student \(s\). ```
Input: Set of students \(S=\{s_{1},\ s_{21},\ldots,s_{k}\}\) Set of Subgroups \(G=\{SG_{1},\ SG_{2},\ldots,SG_{t}\}\)\(AH_{s}\) is the academic history of student \(s\) with the list of completed courses. 1. for each \(s\in S\) do get academic history \(AH_{s}\)
2. for all completed course in subgroup \(SG\) do
3. calculate \(CGPA\)
4. end for
5. subgroup \(SG\) with maximum \(CGPA\) specified as \(C\_CGPA_{s}\) is considered as the domain expertise \(DE_{s}\) of a student \(s\)
6. end for Output:\(C\_CGPA_{s}\) -the core course \(CGPA\) of the student \(s\)\(DE_{s}\) -domain expertise of student \(s\)
```  
**Algorithm 2**Identification of domain expertise of a student  
For all the completed courses in the subgroup \(SG\), CGPA of student \(s\) is calculated. The completed course of the subgroup \(SG\) under domain \(d\) in which a student \(s\) has scored maximum CGPA is specified as \(C\_CGPA_{s}\) is considered as the domain expertise \(DE_{s}\) of the student. As depicted in Figure 5, domain \(d_{1}\) is identified as the domain expertise of student \(s\) and hence based on the number of elective course requirements for the semester, he/she will be recommended with the elective courses from \(\text{LE}_{1}\) under domain \(d_{1}\). A student has to register for 40 credits of elective courses as per the curriculum mandate. Elective courses are recommended for each semester based on the semester's elective course credits requirements. Once approximately 20 credits of elective courses are recommended to a student s, the CGPA scored by student s for the elective courses \(E\_CGPA\) is compared with the \(C\_CGPA\) scored for the expertise domain. If the difference is less than the threshold, the elective courses from the same domain expertise are recommended. Otherwise, the new domain expertize of a student is identified and recommended. The threshold is set to 1.5 as anything greater than 1.5 affects the overall CGPA of the student. The recommendation of elective courses based on the CGPA in the expertise domain of the student is as specified in Algorithm 3. ```
Input: Set of students \(S=\{s_{1},\;s_{2},\ldots,s_{k}\}\) Set of target domains \(D=\{d_{1},\;d_{2},\ldots,d_{l}\}\) Set of Subgroups \(SG=\{SG_{1},\;SG_{2},\ldots,SG_{l}\}\)\(SG\) is the set of all core courses of domain \(d\in D\)\(AH_{s}\) is the academic history of student \(s\) with the list of completed courses \(C\_CGPA_{s}\) is the core course \(CGPA\) of the student \(s\)\(DE_{s}\) is the domain expertise of student \(s\)\(LE\) is the set of all elective courses of domain \(d\in D\) Threshold = 1. for\((s=0;\;s!=NULL;s++)\) 2 Recommend approximately 20 credits of \(LE\) from his/her \(DE_{s}\) 3 Calculate CGPA and specify it as \(E\_CGPA\) 4 If (\(|C\_CGPA-E\_CGPA|>\) Threshold) then
5 Repeat Algorithm 2 and identify new domain expertise of student \(s\) and then repeat Algorithm 3. 6 else continue recommending all possible \(LE\) to student \(s\) endfor Output: \(E\_CGPA\) - the elective course \(CGPA\) of the student \(s\) The elective course recommendations
```  
**Algorithm 3**Elective Course Recommendation  
After the recommendation of elective courses to a student s, it is mandatory to validate the impact of the recommended elective courses. For this, the elective course selection patterns and results of the previous students and the current students are compared. It is  
Figure 5: Identification of domain expertise of a student \(s\).