Predicting and Interpreting Student Performance Using Ensemble Models and Shapley Additive Explanations

In several areas, including education, the use of machine learning, such as artificial neural networks, has resulted in significant improvements in predicting tasks. The opacity of these models is one of the problems with their use. Prediction models that may offer valuable insights while still bein...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Hayat Sahlaoui, El Arbi Abdellaoui Alaoui, Anand Nayyar, Said Agoujil, Mustafa Musa Jaber
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Ensemble methods game theory machine learning student performance prediction SHAP values Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/a80a718b693744d9973e56ed63b82f0f
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:a80a718b693744d9973e56ed63b82f0f
record_format	dspace
spelling	oai:doaj.org-article:a80a718b693744d9973e56ed63b82f0f2021-11-23T00:01:59ZPredicting and Interpreting Student Performance Using Ensemble Models and Shapley Additive Explanations2169-353610.1109/ACCESS.2021.3124270https://doaj.org/article/a80a718b693744d9973e56ed63b82f0f2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9594825/https://doaj.org/toc/2169-3536In several areas, including education, the use of machine learning, such as artificial neural networks, has resulted in significant improvements in predicting tasks. The opacity of these models is one of the problems with their use. Prediction models that may offer valuable insights while still being simple to comprehend are preferred by decision-makers in education. Hence, this study suggests an approach that improves the previous student performance prediction by enhancing performance and explaining why a student’s performance is attaining a certain score. A prediction model was proposed and tested using machine learning models. Our models outperform previous work models developed on the same dataset. Using a combined framework of data level and algorithm approaches, the proposed model achieves an accuracy of over 98%, inplying a 20.3% improvement compared with previous work models. As a balancing technique for upsampling data, we use the default strategy of synthetic minority oversampling technique (SMOTE) to oversample all classes to the number of examples in the majority class. We also use ensemble methods. For tuning the parameters, we use a simple grid search algorithm provided by scikit to estimate the optimal parameters of our model. This hyperparameter optimization along with a ten-fold cross-validation process demonstrates the dependability of the novel model. In addition, a novel visual and intuitive technique is used to help determine which factors most influence the score which helps to interpret and understand the entire model and visualizes feature attributions at the observation level for the machine learning model. Therefore, SHAP values are a powerful tool that should be incorporated within the student performance prediction framework by obtaining the prediction and explanation created through the experiment, educators can recognize students at risk early and provide suitable exhortation in an auspicious manner.Hayat SahlaouiEl Arbi Abdellaoui AlaouiAnand NayyarSaid AgoujilMustafa Musa JaberIEEEarticleEnsemble methodsgame theorymachine learningstudent performance predictionSHAP valuesElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 152688-152703 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Ensemble methods game theory machine learning student performance prediction SHAP values Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Ensemble methods game theory machine learning student performance prediction SHAP values Electrical engineering. Electronics. Nuclear engineering TK1-9971 Hayat Sahlaoui El Arbi Abdellaoui Alaoui Anand Nayyar Said Agoujil Mustafa Musa Jaber Predicting and Interpreting Student Performance Using Ensemble Models and Shapley Additive Explanations
description	In several areas, including education, the use of machine learning, such as artificial neural networks, has resulted in significant improvements in predicting tasks. The opacity of these models is one of the problems with their use. Prediction models that may offer valuable insights while still being simple to comprehend are preferred by decision-makers in education. Hence, this study suggests an approach that improves the previous student performance prediction by enhancing performance and explaining why a student’s performance is attaining a certain score. A prediction model was proposed and tested using machine learning models. Our models outperform previous work models developed on the same dataset. Using a combined framework of data level and algorithm approaches, the proposed model achieves an accuracy of over 98%, inplying a 20.3% improvement compared with previous work models. As a balancing technique for upsampling data, we use the default strategy of synthetic minority oversampling technique (SMOTE) to oversample all classes to the number of examples in the majority class. We also use ensemble methods. For tuning the parameters, we use a simple grid search algorithm provided by scikit to estimate the optimal parameters of our model. This hyperparameter optimization along with a ten-fold cross-validation process demonstrates the dependability of the novel model. In addition, a novel visual and intuitive technique is used to help determine which factors most influence the score which helps to interpret and understand the entire model and visualizes feature attributions at the observation level for the machine learning model. Therefore, SHAP values are a powerful tool that should be incorporated within the student performance prediction framework by obtaining the prediction and explanation created through the experiment, educators can recognize students at risk early and provide suitable exhortation in an auspicious manner.
format	article
author	Hayat Sahlaoui El Arbi Abdellaoui Alaoui Anand Nayyar Said Agoujil Mustafa Musa Jaber
author_facet	Hayat Sahlaoui El Arbi Abdellaoui Alaoui Anand Nayyar Said Agoujil Mustafa Musa Jaber
author_sort	Hayat Sahlaoui
title	Predicting and Interpreting Student Performance Using Ensemble Models and Shapley Additive Explanations
title_short	Predicting and Interpreting Student Performance Using Ensemble Models and Shapley Additive Explanations
title_full	Predicting and Interpreting Student Performance Using Ensemble Models and Shapley Additive Explanations
title_fullStr	Predicting and Interpreting Student Performance Using Ensemble Models and Shapley Additive Explanations
title_full_unstemmed	Predicting and Interpreting Student Performance Using Ensemble Models and Shapley Additive Explanations
title_sort	predicting and interpreting student performance using ensemble models and shapley additive explanations
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/a80a718b693744d9973e56ed63b82f0f
work_keys_str_mv	AT hayatsahlaoui predictingandinterpretingstudentperformanceusingensemblemodelsandshapleyadditiveexplanations AT elarbiabdellaouialaoui predictingandinterpretingstudentperformanceusingensemblemodelsandshapleyadditiveexplanations AT anandnayyar predictingandinterpretingstudentperformanceusingensemblemodelsandshapleyadditiveexplanations AT saidagoujil predictingandinterpretingstudentperformanceusingensemblemodelsandshapleyadditiveexplanations AT mustafamusajaber predictingandinterpretingstudentperformanceusingensemblemodelsandshapleyadditiveexplanations
_version_	1718417394149359616

Predicting and Interpreting Student Performance Using Ensemble Models and Shapley Additive Explanations

Ejemplares similares