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A Conceptual Framework to Aid Attribute Selection in Machine Learning Student Performance Prediction Models

One of the key applications of Learning Analytics is offering an opportunity to the institutions to track the students' academic activities and provide them with real-time adaptive consultations regarding the students' academic progression. However, numerous barriers exist while developing...

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Main Authors: Khan I., Ahmad A.R., Jabeur N., Mahdi M.N.
Other Authors: 58061521900
Format: Article
Published: International Association of Online Engineering 2023
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spelling my.uniten.dspace-265062023-05-29T17:11:18Z A Conceptual Framework to Aid Attribute Selection in Machine Learning Student Performance Prediction Models Khan I. Ahmad A.R. Jabeur N. Mahdi M.N. 58061521900 35589598800 6505727698 56727803900 One of the key applications of Learning Analytics is offering an opportunity to the institutions to track the students' academic activities and provide them with real-time adaptive consultations regarding the students' academic progression. However, numerous barriers exist while developing and implementing such kind of learning analytics applications. Machine learning algorithms emerge as useful tools to endorse learning analytics by building models capable of forecasting the final outcome of students based on their available attributes. Machine learning algorithm's performance demotes with using the entire attributes and thus a vigilant selection of predicting attributes boosts the performance of the produced model. Though, several constructive techniques facilitate to identify the subset of significant attributes, however, the challenging task is to evaluate if the prediction attributes are meaningful, explicit, and controllable by the students. This paper reviews the existing literature to come up with an exhausting list of attributes used in developing student performance prediction models. We propose a conceptual framework which identifies the nature of attributes and classify them as either latent or dynamic. The latent attributes may appear significant, but the student is not able to control these attributes, on the other hand, the student has command to restrain the dynamic attributes. The framework presents an opportunity to the researchers to pick constructive attributes for model development. We apply artificial neural network, a supervised learner, over a dataset to compare the performance of prediction models with distinct classes of attributes. It confirms the significance of dynamic attributes for student performance prediction models. � 2021. All Rights Reserved. Final 2023-05-29T09:11:18Z 2023-05-29T09:11:18Z 2021 Article 10.3991/ijim.v15i15.20019 2-s2.0-85112851766 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112851766&doi=10.3991%2fijim.v15i15.20019&partnerID=40&md5=5b04cefdb0ce48a27d9b8df41cc51a93 https://irepository.uniten.edu.my/handle/123456789/26506 15 15 4 19 All Open Access, Gold International Association of Online Engineering Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description One of the key applications of Learning Analytics is offering an opportunity to the institutions to track the students' academic activities and provide them with real-time adaptive consultations regarding the students' academic progression. However, numerous barriers exist while developing and implementing such kind of learning analytics applications. Machine learning algorithms emerge as useful tools to endorse learning analytics by building models capable of forecasting the final outcome of students based on their available attributes. Machine learning algorithm's performance demotes with using the entire attributes and thus a vigilant selection of predicting attributes boosts the performance of the produced model. Though, several constructive techniques facilitate to identify the subset of significant attributes, however, the challenging task is to evaluate if the prediction attributes are meaningful, explicit, and controllable by the students. This paper reviews the existing literature to come up with an exhausting list of attributes used in developing student performance prediction models. We propose a conceptual framework which identifies the nature of attributes and classify them as either latent or dynamic. The latent attributes may appear significant, but the student is not able to control these attributes, on the other hand, the student has command to restrain the dynamic attributes. The framework presents an opportunity to the researchers to pick constructive attributes for model development. We apply artificial neural network, a supervised learner, over a dataset to compare the performance of prediction models with distinct classes of attributes. It confirms the significance of dynamic attributes for student performance prediction models. � 2021. All Rights Reserved.
author2 58061521900
author_facet 58061521900
Khan I.
Ahmad A.R.
Jabeur N.
Mahdi M.N.
format Article
author Khan I.
Ahmad A.R.
Jabeur N.
Mahdi M.N.
spellingShingle Khan I.
Ahmad A.R.
Jabeur N.
Mahdi M.N.
A Conceptual Framework to Aid Attribute Selection in Machine Learning Student Performance Prediction Models
author_sort Khan I.
title A Conceptual Framework to Aid Attribute Selection in Machine Learning Student Performance Prediction Models
title_short A Conceptual Framework to Aid Attribute Selection in Machine Learning Student Performance Prediction Models
title_full A Conceptual Framework to Aid Attribute Selection in Machine Learning Student Performance Prediction Models
title_fullStr A Conceptual Framework to Aid Attribute Selection in Machine Learning Student Performance Prediction Models
title_full_unstemmed A Conceptual Framework to Aid Attribute Selection in Machine Learning Student Performance Prediction Models
title_sort conceptual framework to aid attribute selection in machine learning student performance prediction models
publisher International Association of Online Engineering
publishDate 2023
_version_ 1806425550660567040
score 13.211869

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