Instructor-assisted question classification system using machine learning algorithms with N-gram and weighting schemes

Abstract One aspect of natural language processing, text classification, has become necessary in the educational domain due to the increasing number of students and the COVID-19 outbreak. The advent of the devastating pandemic and the need to remain safe have surged the discussions around online lea...
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Autor*in:	Dake, Delali Kwasi [verfasserIn] Nwiah, Edward Klogo, Griffith Selorm Ativi, Wisdom Xornam

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Supervised algorithms Ensemble algorithms Natural language processing Text classification

Anmerkung:	© The Author(s) 2023

Übergeordnetes Werk:	Enthalten in: Discover artificial intelligence - [Cham] : Springer International Publishing, 2021, 3(2023), 1 vom: 08. Aug.
Übergeordnetes Werk:	volume:3 ; year:2023 ; number:1 ; day:08 ; month:08

Links:	Volltext

DOI / URN:	10.1007/s44163-023-00073-5

Katalog-ID:	SPR052679713

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allfields	10.1007/s44163-023-00073-5 doi (DE-627)SPR052679713 (SPR)s44163-023-00073-5-e DE-627 ger DE-627 rakwb eng Dake, Delali Kwasi verfasserin (orcid)0000-0002-6067-6980 aut Instructor-assisted question classification system using machine learning algorithms with N-gram and weighting schemes 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract One aspect of natural language processing, text classification, has become necessary in the educational domain due to the increasing number of students and the COVID-19 outbreak. The advent of the devastating pandemic and the need to remain safe have surged the discussions around online learning and integrated modules in teaching and learning. In this study, we employed machine learning to develop an automatic instructor-assisted question classification module for learning management systems. In selecting the best classifier, the conventional and the ensemble machine learning algorithms were compared using the tenfold and the fivefold cross-validation techniques. In addition, the N-gram feature selection mechanism and three weighting schemes were evaluated for performance enhancement. The detailed analysis indicates that the ensemble algorithms outperform the conventional ones with decreasing accuracy as the N-gram size increases. For all compared algorithms, the AdaBoost (SVM) ensemble algorithm has the highest accuracy of 78.55% for Unigram (TP, TF, TF-IDF). In addition, the AdaBoost (SVM) emerged with the highest F1-score of 0.782, whiles the ensemble Bagging (RF) algorithm had the highest ROC value of 0.955 for Unigram (TP). Supervised algorithms (dpeaa)DE-He213 Ensemble algorithms (dpeaa)DE-He213 Natural language processing (dpeaa)DE-He213 Text classification (dpeaa)DE-He213 Nwiah, Edward aut Klogo, Griffith Selorm aut Ativi, Wisdom Xornam aut Enthalten in Discover artificial intelligence [Cham] : Springer International Publishing, 2021 3(2023), 1 vom: 08. Aug. (DE-627)1774283107 (DE-600)3097625-X 2731-0809 nnns volume:3 year:2023 number:1 day:08 month:08 https://dx.doi.org/10.1007/s44163-023-00073-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2023 1 08 08
spelling	10.1007/s44163-023-00073-5 doi (DE-627)SPR052679713 (SPR)s44163-023-00073-5-e DE-627 ger DE-627 rakwb eng Dake, Delali Kwasi verfasserin (orcid)0000-0002-6067-6980 aut Instructor-assisted question classification system using machine learning algorithms with N-gram and weighting schemes 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract One aspect of natural language processing, text classification, has become necessary in the educational domain due to the increasing number of students and the COVID-19 outbreak. The advent of the devastating pandemic and the need to remain safe have surged the discussions around online learning and integrated modules in teaching and learning. In this study, we employed machine learning to develop an automatic instructor-assisted question classification module for learning management systems. In selecting the best classifier, the conventional and the ensemble machine learning algorithms were compared using the tenfold and the fivefold cross-validation techniques. In addition, the N-gram feature selection mechanism and three weighting schemes were evaluated for performance enhancement. The detailed analysis indicates that the ensemble algorithms outperform the conventional ones with decreasing accuracy as the N-gram size increases. For all compared algorithms, the AdaBoost (SVM) ensemble algorithm has the highest accuracy of 78.55% for Unigram (TP, TF, TF-IDF). In addition, the AdaBoost (SVM) emerged with the highest F1-score of 0.782, whiles the ensemble Bagging (RF) algorithm had the highest ROC value of 0.955 for Unigram (TP). Supervised algorithms (dpeaa)DE-He213 Ensemble algorithms (dpeaa)DE-He213 Natural language processing (dpeaa)DE-He213 Text classification (dpeaa)DE-He213 Nwiah, Edward aut Klogo, Griffith Selorm aut Ativi, Wisdom Xornam aut Enthalten in Discover artificial intelligence [Cham] : Springer International Publishing, 2021 3(2023), 1 vom: 08. Aug. (DE-627)1774283107 (DE-600)3097625-X 2731-0809 nnns volume:3 year:2023 number:1 day:08 month:08 https://dx.doi.org/10.1007/s44163-023-00073-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2023 1 08 08
allfields_unstemmed	10.1007/s44163-023-00073-5 doi (DE-627)SPR052679713 (SPR)s44163-023-00073-5-e DE-627 ger DE-627 rakwb eng Dake, Delali Kwasi verfasserin (orcid)0000-0002-6067-6980 aut Instructor-assisted question classification system using machine learning algorithms with N-gram and weighting schemes 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract One aspect of natural language processing, text classification, has become necessary in the educational domain due to the increasing number of students and the COVID-19 outbreak. The advent of the devastating pandemic and the need to remain safe have surged the discussions around online learning and integrated modules in teaching and learning. In this study, we employed machine learning to develop an automatic instructor-assisted question classification module for learning management systems. In selecting the best classifier, the conventional and the ensemble machine learning algorithms were compared using the tenfold and the fivefold cross-validation techniques. In addition, the N-gram feature selection mechanism and three weighting schemes were evaluated for performance enhancement. The detailed analysis indicates that the ensemble algorithms outperform the conventional ones with decreasing accuracy as the N-gram size increases. For all compared algorithms, the AdaBoost (SVM) ensemble algorithm has the highest accuracy of 78.55% for Unigram (TP, TF, TF-IDF). In addition, the AdaBoost (SVM) emerged with the highest F1-score of 0.782, whiles the ensemble Bagging (RF) algorithm had the highest ROC value of 0.955 for Unigram (TP). Supervised algorithms (dpeaa)DE-He213 Ensemble algorithms (dpeaa)DE-He213 Natural language processing (dpeaa)DE-He213 Text classification (dpeaa)DE-He213 Nwiah, Edward aut Klogo, Griffith Selorm aut Ativi, Wisdom Xornam aut Enthalten in Discover artificial intelligence [Cham] : Springer International Publishing, 2021 3(2023), 1 vom: 08. Aug. (DE-627)1774283107 (DE-600)3097625-X 2731-0809 nnns volume:3 year:2023 number:1 day:08 month:08 https://dx.doi.org/10.1007/s44163-023-00073-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2023 1 08 08
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source	Enthalten in Discover artificial intelligence 3(2023), 1 vom: 08. Aug. volume:3 year:2023 number:1 day:08 month:08
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author	Dake, Delali Kwasi
spellingShingle	Dake, Delali Kwasi misc Supervised algorithms misc Ensemble algorithms misc Natural language processing misc Text classification Instructor-assisted question classification system using machine learning algorithms with N-gram and weighting schemes
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topic_title	Instructor-assisted question classification system using machine learning algorithms with N-gram and weighting schemes Supervised algorithms (dpeaa)DE-He213 Ensemble algorithms (dpeaa)DE-He213 Natural language processing (dpeaa)DE-He213 Text classification (dpeaa)DE-He213
topic	misc Supervised algorithms misc Ensemble algorithms misc Natural language processing misc Text classification
topic_unstemmed	misc Supervised algorithms misc Ensemble algorithms misc Natural language processing misc Text classification
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format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
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title	Instructor-assisted question classification system using machine learning algorithms with N-gram and weighting schemes
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title_full	Instructor-assisted question classification system using machine learning algorithms with N-gram and weighting schemes
author_sort	Dake, Delali Kwasi
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author_browse	Dake, Delali Kwasi Nwiah, Edward Klogo, Griffith Selorm Ativi, Wisdom Xornam
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title_sort	instructor-assisted question classification system using machine learning algorithms with n-gram and weighting schemes
title_auth	Instructor-assisted question classification system using machine learning algorithms with N-gram and weighting schemes
abstract	Abstract One aspect of natural language processing, text classification, has become necessary in the educational domain due to the increasing number of students and the COVID-19 outbreak. The advent of the devastating pandemic and the need to remain safe have surged the discussions around online learning and integrated modules in teaching and learning. In this study, we employed machine learning to develop an automatic instructor-assisted question classification module for learning management systems. In selecting the best classifier, the conventional and the ensemble machine learning algorithms were compared using the tenfold and the fivefold cross-validation techniques. In addition, the N-gram feature selection mechanism and three weighting schemes were evaluated for performance enhancement. The detailed analysis indicates that the ensemble algorithms outperform the conventional ones with decreasing accuracy as the N-gram size increases. For all compared algorithms, the AdaBoost (SVM) ensemble algorithm has the highest accuracy of 78.55% for Unigram (TP, TF, TF-IDF). In addition, the AdaBoost (SVM) emerged with the highest F1-score of 0.782, whiles the ensemble Bagging (RF) algorithm had the highest ROC value of 0.955 for Unigram (TP). © The Author(s) 2023
abstractGer	Abstract One aspect of natural language processing, text classification, has become necessary in the educational domain due to the increasing number of students and the COVID-19 outbreak. The advent of the devastating pandemic and the need to remain safe have surged the discussions around online learning and integrated modules in teaching and learning. In this study, we employed machine learning to develop an automatic instructor-assisted question classification module for learning management systems. In selecting the best classifier, the conventional and the ensemble machine learning algorithms were compared using the tenfold and the fivefold cross-validation techniques. In addition, the N-gram feature selection mechanism and three weighting schemes were evaluated for performance enhancement. The detailed analysis indicates that the ensemble algorithms outperform the conventional ones with decreasing accuracy as the N-gram size increases. For all compared algorithms, the AdaBoost (SVM) ensemble algorithm has the highest accuracy of 78.55% for Unigram (TP, TF, TF-IDF). In addition, the AdaBoost (SVM) emerged with the highest F1-score of 0.782, whiles the ensemble Bagging (RF) algorithm had the highest ROC value of 0.955 for Unigram (TP). © The Author(s) 2023
abstract_unstemmed	Abstract One aspect of natural language processing, text classification, has become necessary in the educational domain due to the increasing number of students and the COVID-19 outbreak. The advent of the devastating pandemic and the need to remain safe have surged the discussions around online learning and integrated modules in teaching and learning. In this study, we employed machine learning to develop an automatic instructor-assisted question classification module for learning management systems. In selecting the best classifier, the conventional and the ensemble machine learning algorithms were compared using the tenfold and the fivefold cross-validation techniques. In addition, the N-gram feature selection mechanism and three weighting schemes were evaluated for performance enhancement. The detailed analysis indicates that the ensemble algorithms outperform the conventional ones with decreasing accuracy as the N-gram size increases. For all compared algorithms, the AdaBoost (SVM) ensemble algorithm has the highest accuracy of 78.55% for Unigram (TP, TF, TF-IDF). In addition, the AdaBoost (SVM) emerged with the highest F1-score of 0.782, whiles the ensemble Bagging (RF) algorithm had the highest ROC value of 0.955 for Unigram (TP). © The Author(s) 2023
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title_short	Instructor-assisted question classification system using machine learning algorithms with N-gram and weighting schemes
url	https://dx.doi.org/10.1007/s44163-023-00073-5
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author2	Nwiah, Edward Klogo, Griffith Selorm Ativi, Wisdom Xornam
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up_date	2024-07-03T14:00:48.094Z
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