An automated framework to recommend a suitable academic program, course and instructor

Ahmad Slim, Don Hush, Tushar Ojha, Chaouki Abdallah, Gregory Heileman, Georges El-Howayek

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

In this paper we propose a recommendation tool framework to help a student pick the right program, right course and right instructor that fit his/her skills and characteristics. We apply collaborative filtering (CK), using a non-negative matrix factorization (NMF) method, to extract latent features corresponding to the student academic skills and to the program required skills: when augmented with a student's competence level, the required skills of programs can be exploited to produce a program recommender system. Then using a multi-objective optimization method, we propose a degree plan recommender system that is able to design a customized/personalized degree plan that will better fit to real life situations by moving the courses with relatively higher crucial values and higher predicted letter grades to closest possible terms while meeting all the constraints. We extend our framework to include instructor recommender system. The intuition is to recommend an instructor that jointly fits the required skills of the course and the academic skills of the student. The experimental results conducted using real data from the University of New Mexico (UNM) show that our proposed framework can accurately extract features related to students and courses. The results are validated using k-means clustering technique. Ultimately, using this framework, we will be able improve enrollment, help students graduate in a timely fashion and incentive their ability to persist.

Original languageEnglish (US)
Title of host publicationProceedings - 5th IEEE International Conference on Big Data Service and Applications, BigDataService 2019, Workshop on Big Data in Water Resources, Environment, and Hydraulic Engineering and Workshop on Medical, Healthcare, Using Big Data Technologies
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages145-150
Number of pages6
ISBN (Electronic)9781728100593
DOIs
StatePublished - Apr 2019
Externally publishedYes
Event5th IEEE International Conference on Big Data Service and Applications, BigDataService 2019 - Newark, United States
Duration: Apr 4 2019Apr 9 2019

Publication series

NameProceedings - 5th IEEE International Conference on Big Data Service and Applications, BigDataService 2019, Workshop on Big Data in Water Resources, Environment, and Hydraulic Engineering and Workshop on Medical, Healthcare, Using Big Data Technologies

Conference

Conference5th IEEE International Conference on Big Data Service and Applications, BigDataService 2019
Country/TerritoryUnited States
CityNewark
Period4/4/194/9/19

Keywords

  • Collaborative filtering
  • Education and learning
  • Learning analytics
  • Recommender system

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems
  • Environmental Engineering
  • Water Science and Technology
  • Medicine (miscellaneous)
  • Health Informatics
  • Information Systems and Management
  • Health(social science)

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