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Evaluation of the Effect of Learning Disabilities and Accommodations on the Prediction of the Stability of Academic Behaviour of Undergraduate Engineering Students Using Decision Trees

Title: Evaluation of the Effect of Learning Disabilities and Accommodations on the Prediction of the Stability of Academic Behaviour of Undergraduate Engineering Students Using Decision Trees
Language: English
Authors: Singer, Gonen; Golan, Maya; Rabin, Neta; Kleper, Dvir
Source: European Journal of Engineering Education. 2020 45(4):614-630.
Availability: Taylor & Francis. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 17
Publication Date: 2020
Document Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Descriptors: Learning Disabilities; Academic Accommodations (Disabilities); Undergraduate Students; Engineering Education; Early Intervention; Decision Making; Prediction; Academic Achievement; Student Behavior; Evaluation Methods; Classification; Data Analysis
DOI: 10.1080/03043797.2019.1677560
ISSN: 0304-3797
Abstract: The purpose of this study is to evaluate how learning disabilities (LDs), in combination with accommodations, affect the performance of a decision-tree to predict the stability of academic behaviour of undergraduate engineering students. Additionally, this study presents several examples to illustrate how a college could use the resultant model to choose the appropriate accommodation to give a student with a learning disability, from among a set of possible accommodations. The findings show that: (1) The models yield superior performance in predicting the stability category for a given student when the LD and accommodation factors are included; (2) Different types of accommodation action have different effects on the stability of academic behaviour, depending on the student pattern. Such a model could be useful for engineering faculties, as it would allow them to predict the stability of academic behaviour and to provide early intervention for students who are likely to need additional support.
Abstractor: As Provided
Entry Date: 2020
Accession Number: EJ1260455
Database: ERIC