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Addressing the Needs of Slow Learners in Engineering Programs: Effective Identification and Improvement Strategies.

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Bibliographic Details
Title:	Addressing the Needs of Slow Learners in Engineering Programs: Effective Identification and Improvement Strategies.
Authors:	Beldar, Pankaj¹ prbeldar@kkwagh.edu.in, Kadbhane, Snehal¹ svkadbhane@kkwagh.edu.in, Patil, Atulkumar¹ a.s.patil@kkwagh.edu.in
Source:	Journal of Engineering Education Transformations. Oct2025, Vol. 39 Issue 2, p111-123. 13p.
Subject Terms:	Academic achievement, Peer teaching, Students, Technical education, Educational planning, Individualized instruction, Prediction models, Detection algorithms
Abstract:	This paper addresses the needs of slow learners in engineering programs by exploring effective identification and improvement strategies. We employ a range of statistical methods, including descriptive statistics, regression analysis, and clustering, to identify slow learners. Predictive modelling techniques, such as decision trees and support vector machines, are utilized to classify students based on their learning patterns. Our analysis with Python Programming reveals a noticeable improvement in academic performance from Semester 1 to Semester 2. Specifically, there is an increase in average CGPA, a decrease in the number of backlogs, and an improvement in the passing rate. These results demonstrate the effectiveness of the implemented strategies. To support these learners, we propose several strategies: pairing slow learners with advanced peers, promoting peer teaching, developing individualized learning plans, and utilizing technology-enhanced resources. Feedback from students indicates high satisfaction with these strategies, reflecting their positive impact on engagement, understanding, and academic performance. These approaches collectively aim to foster better learning outcomes and overall improvement in engineering education. [ABSTRACT FROM AUTHOR]
	Copyright of Journal of Engineering Education Transformations is the property of Rajarambapu Institute of Technology and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database:	Education Research Complete

Description
Abstract:	This paper addresses the needs of slow learners in engineering programs by exploring effective identification and improvement strategies. We employ a range of statistical methods, including descriptive statistics, regression analysis, and clustering, to identify slow learners. Predictive modelling techniques, such as decision trees and support vector machines, are utilized to classify students based on their learning patterns. Our analysis with Python Programming reveals a noticeable improvement in academic performance from Semester 1 to Semester 2. Specifically, there is an increase in average CGPA, a decrease in the number of backlogs, and an improvement in the passing rate. These results demonstrate the effectiveness of the implemented strategies. To support these learners, we propose several strategies: pairing slow learners with advanced peers, promoting peer teaching, developing individualized learning plans, and utilizing technology-enhanced resources. Feedback from students indicates high satisfaction with these strategies, reflecting their positive impact on engagement, understanding, and academic performance. These approaches collectively aim to foster better learning outcomes and overall improvement in engineering education. [ABSTRACT FROM AUTHOR]
ISSN:	23492473