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Related Experiment Video

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Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

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Published on: June 10, 2021

Clustering students into groups according to their learning style.

Irene Pasina¹, Goze Bayram¹, Wafa Labib¹

¹College of Engineering, Prince Sultan University, P.O. Box 66833, Riyadh 11586, Saudi Arabia.

|November 1, 2019

Summary

This study classifies engineering students by learning styles using group technology. This helps instructors tailor teaching and group students effectively for better learning outcomes.

Keywords:

Felder and Silverman Group technology Hierarchal clustering algorithms Learning style Teaching style

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Area of Science:

Educational Technology
Engineering Education

Background:

Limited research exists on learning style preferences in Saudi Arabian engineering education.
Understanding student learning styles is crucial for effective pedagogy.

Purpose of the Study:

To classify engineering students into clusters based on learning style preferences.
To apply group technology and clustering algorithms for student classification.

Main Methods:

Utilized Felder and Silverman's Index Learning Style (ILS) to assess preferences.
Employed clustering algorithms, including complete clustering, to group students.

Main Results:

Successfully clustered students based on shared learning style preferences.
Identified outlier students with distinct learning styles.

Conclusions:

Student clustering enables instructors to adopt tailored teaching approaches.
Grouping facilitates collaborative assignments for students with similar learning styles.
Identifying outliers allows for individualized instructional support.