- Teghdeep Kapoor
- Vardhika Jain
- Tanya Pandhi
COURSE RECOMMENDATION SYSTEM TO IMPROVE LEARNING RATE OF STUDENTS IN THE COVID SCENARIO
In higher education, Courses ought to provide a deeper insight of the trending advancements in the field of specialization for undergraduate students. Making choice of elective courses during the pre-final or final year of the undergraduates play a crucial role as it helps in shaping their career or area of specialization for the better learning. However, as per the current educational scenarios, the undergraduates remain mostly confused on what to choose as they either lack in having the sufficient initial knowledge of the elective subjects or are having knowledge overflow of all subjects and so are unable to decide which one to choose. In such scenarios, they often seek the advice of their instructors or friends and mostly go with the cohort choice. However, going with the flow often creates a gap between their actual skills set and the required skills set for the elective subject that they have preferred as their choice. In later stages, this results in loss of interest of the students in the enrolled elective subject and hence a degraded academic performance is encountered by the institution. Similarly, as a result of this, there can be numerous limitations, gaps or concerns arising either in case of students or institutions in real world educational scenarios. A personalized recommender system recommends efficient course subjects to the students that indirectly predicts the academic success of different courses beforehand and along with this also preserves the student subject interests.