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.
- Learning disabilities
- decision trees
- educational data mining
All Science Journal Classification (ASJC) codes