TY - JOUR
T1 - 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
AU - Singer, Gonen
AU - Golan, Maya
AU - Rabin, Neta
AU - Kleper, Dvir
N1 - Publisher Copyright:
© 2019, © 2019 SEFI.
PY - 2020/7/3
Y1 - 2020/7/3
N2 - 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.
AB - 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.
KW - Learning disabilities
KW - accommodations
KW - decision trees
KW - educational data mining
UR - http://www.scopus.com/inward/record.url?scp=85074357216&partnerID=8YFLogxK
U2 - 10.1080/03043797.2019.1677560
DO - 10.1080/03043797.2019.1677560
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AN - SCOPUS:85074357216
SN - 0304-3797
VL - 45
SP - 614
EP - 630
JO - European Journal of Engineering Education
JF - European Journal of Engineering Education
IS - 4
ER -