###### Abstract  
The selection of elective courses based on an individual's domain interest is a challenging and critical activity for students at the start of their curriculum. Effective and proper recommendation may result in building a strong expertise in the domain of interest, which in turn improves the outcomes of the students getting better placements, and enrolling into higher studies of their interest, etc. In this paper, an effective course recommendation system is proposed to help the students in facilitating proper course selection based on an individual's domain interest. To achieve this, the core courses in the curriculum are mapped with the predefined domain suggested by the domain experts. These core course contents mapped with the domain are trained semantically using deep learning models to classify the elective courses into domains, and the same are recommended based on the student's domain expertise. The recommendation is validated by analyzing the number of elective course credits completed and the grades scored by a student who utilized the elective course recommendation system, with the grades scored by the student who was subjected to the assessment without elective course recommendations. It was also observed that after the recommendation, the students have registered for a greater number of credits for elective courses on their domain of expertise, which in-turn enables them to have a better learning experience and improved course completion probability.  
e 20221210792103390/app122110792107  
relatively small number of optional courses, and 30% of students drop these optional courses within a few weeks of the corresponding semester due to difficulty in getting higher scores. The improper selection of elective courses seriously affects the optional course achievements of students, and enforces them to drop these optional courses midway.  
The selection of elective is greatly influenced by a student's friends' or instructors' recommendations, due to a lack of information about the description of the course contents and limited guidance during the selection process of the elective courses [5]. To make this point clear, a survey was conducted among 110 random students of our institution for analyzing the selection of elective courses. From Figure 1, we can see that most of the students (i.e., 59%) are selecting courses based on their domain interest; students selecting courses based on their friends' suggestions (16%); suggestions from faculty (15%); and favorite faculty's course (5%).  
The survey results gave some interesting insights as to how the students select their elective courses. Gen-z students prefers elective courses based on their domain of interest. Despite this, out of 110 students who took the survey on the need for recommendation, 56 students have given their choice as \"maybe\", as depicted in Figure 2.  
The survey results show that students are clear about which type of elective courses to select, but they do not have a clear view on how to effectively select the same through recommendation for enabling their performance, and hence gain a better Cumulative Grade Point Average (CGPA). It is imperative that the students are guided properly during these course selections that would enable them to achieve better results.  
Figure 1: A survey on the selection of elective courses.  
Figure 2: Students’ insight on course recommendation.  
This paper analyses the various methodologies related to elective course recommendation and text processing in Section 2, the proposed elective course recommendation methodology in Sections 3 and 4 discusses the results in Section 5 and concludes the proposed work with future scope for research proceedings in Section 6.