Modern Artificial Intelligence Model Development for Undergraduate Student Performance Prediction: An Investigation on Engineering Mathematics Courses - B-AIM PICK SELECTS

 

 

Abstract:

A computationally efficient artificial intelligence (AI) model called Extreme Learning Machines (ELM) is adopted to analyze patterns embedded in continuous assessment to model the weighted score (WS) and the examination (EX) score in engineering mathematics courses at an Australian regional university. The student performance data taken over a six-year period in multiple courses ranging from the mid- to the advanced level and a diverse course offering mode (i.e., on-campus, ONC, and online, ONL) are modelled by ELM and further benchmarked against competing models: random forest (RF) and Volterra. With the assessments and examination marks as key predictors of WS (leading to a grade in the mid-level course), ELM (with respect to RF and Volterra) outperformed its counterpart models both for the ONC and the ONL offer. This generated relative prediction error in the testing phase, of only 0.74%, compared to about 3.12% and 1.06%, respectively, while for the ONL offer, the prediction errors were only 0.51% compared to about 3.05% and 0.70%. In modelling the student performance in advanced engineering mathematics course, ELM registered slightly larger errors: 0.77% (vs. 22.23% and 1.87%) for ONC and 0.54% (vs. 4.08% and 1.31%) for the ONL offer. This study advocates a pioneer implementation of a robust AI methodology to uncover relationships among student learning variables, developing teaching and learning intervention and course health checks to address issues related to graduate outcomes, and student learning attributes in the higher education sector.

 

click here to see the content - pdf

 

Published in: IEEE Access

 

 

 

click here to watch making of B-AIM

 

 

Please reload

Our Recent Posts

Why shrimp producers must learn from salmon sector- B-aim pick selects

December 1, 2020

Fishing sector offered chance to move into seaweed, shellfish culture-B-AIM PICK SELECTS

December 1, 2020

Steering the States towards sustainable offshore aquaculture -B-aim pick selects

December 1, 2020

1/1
Please reload

Tags

 
  • LinkedIn
  • Facebook

©2018 by B-AIM