Abstract
The goal of this system is to empower first-year students to make well-informed decisions about their university programs by providing tailored recommendations based on their individual profiles. Selecting the right university program can be a daunting task for first-year students. In response to this challenge, we have developed Betr Selektr, an all-encompassing program recommender system. This innovative system considers both the student's personality type and academic achievements, utilizing the Myers-Briggs Type Indicator (MBTI) framework. By merging this information with the student's high school results, we create a personalized index figure that reflects their unique personality type. This index figure acts as the foundation for recommending degree programs that align with the student's interests, strengths, and educational background. This system has been designed using the waterfall development methodology, employing tools such as Visual Studio Code, SQLAlchemy, Flask, and SQLite. Through various stages, including systems analysis and design, implementation and testing, and the utilization of research methodologies, we have created a comprehensive solution. Betr Selektr offers a user-friendly interface, swift data processing, and precise program recommendations, making it an invaluable asset in the university application process.
Author Information
Belinda Ndlovu, National University of Science and Technology, Zimbabwe
Zirah Takunda Migioni, National University of Science and Technology, Zimbabwe
Sibusisiwe Dube, National University of Science and Technology, Zimbabwe
Phillip Nyoni, National University of Science and Technology, Zimbabwe
Paper Information
Conference: BCE2023
Stream: Professional Training
This paper is part of the BCE2023 Conference Proceedings (View)
Full Paper
View / Download the full paper in a new tab/window
To cite this article:
Ndlovu B., Migioni Z., Dube S., & Nyoni P. (2023) Betr Selektr: A University Program Recommender System Utilising Personality Type and Academic Results ISSN: 2435-9467 – The Barcelona Conference on Education 2023: Official Conference Proceedings https://doi.org/10.22492/issn.2435-9467.2023.55
To link to this article: https://doi.org/10.22492/issn.2435-9467.2023.55
Comments
Powered by WP LinkPress