A personalised approach to engineering education is needed, researchers say

Researchers are finding that a ‘one-size-fits-all’ approach to teaching engineering may not effectively meet the diverse needs of every student.

The researchers are testing the use of an adaptive learning platform (ALP) that tailors resources to each student's needs, using machine learning algorithms
The researchers are testing the use of an adaptive learning platform (ALP) that tailors resources to each student's needs, using machine learning algorithms - AdobeStock

In response, the US National Academy of Engineering listed personalised learning – instruction being tailored to the student’s individual needs – as one of its main challenges, encouraging engineering professors to develop new methods for their classrooms.

Associate professors Renee Clark and Ahmed Dallal at the University of Pittsburgh Swanson School of Engineering, in collaboration with the University of Central Florida and the University of South Florida, received a $254,966 grant from the US National Science Foundation to advance research on personalising STEM education, utilising their sophomore-level statistics and circuit classes.

“We’re aiming to promote better in-class engagement by reviewing pre-requisite content for students before class in a personalised, multiple-resources fashion,” Clark, principal investigator and associate professor of industrial engineering, said in a statement.

“To truly support student success, we need to embrace a more personalised approach that adapts to their diverse needs, rather than treating every student the same.”

The researcher’s experiment will involve two groups. The control group will access class materials, such as quizzes, videos and textbook content through Canvas, an online learning management system that Pitt students already use.

The experimental group, however, will use the Realizeit adaptive learning platform (ALP) that tailors resources to each student's needs using machine learning algorithms. The ALP adjusts content based on student performance on online assessment quizzes. The researchers will compare the two groups and investigate differences in students’ exam scores, motivation and cognitive engagement levels.

“Ensuring students are prepared before they enter the classroom fosters a more active learning environment,” said Dallal. “This approach allows faculty to focus on deeper engagement during lectures, helping students get more out of the experience and enhancing their overall educational mission.”

Clark and Dallal recently completed development of the pre-class preparation materials for the control group and will begin their classroom research in the spring 2025 semester for their project, ‘Collaborative Research: Using Adaptive Lessons to Enhance Motivation, Cognitive Engagement, And Achievement Through Equitable Classroom Preparation.’