Feature engineering for predicting MOOC performance
Increasing data recorded in massive open online course (MOOC) requires more automated analysis. The analysis, which includes making student's prediction requires better strategy to produce good features and reduces prediction error. This paper presents the process of feature engineering for pre...
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my.utm.940772022-02-28T13:24:11Z http://eprints.utm.my/id/eprint/94077/ Feature engineering for predicting MOOC performance Mohamad, Nadirah Ahmad, Nor Bahiah Abang Jawawi, Dayang Norhayati Mohd. Hashim, Siti Zaiton QA75 Electronic computers. Computer science Increasing data recorded in massive open online course (MOOC) requires more automated analysis. The analysis, which includes making student's prediction requires better strategy to produce good features and reduces prediction error. This paper presents the process of feature engineering for predicting MOOC student's performance utilizing deep feature synthesis (DFS) method. The experiment produces features which all the top features selected using principal component analysis (PCA) are the features that are generated from method. In terms of prediction comparing both based features and generated features, the result shows better accuracy for generated features proposed using k-nearest neighbours technique which shows the method potential to be used for future prediction model. 2020 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/94077/1/NorBahiahAhmad2020_FeatureEngineeringforPredictingMOOCPerformance.pdf Mohamad, Nadirah and Ahmad, Nor Bahiah and Abang Jawawi, Dayang Norhayati and Mohd. Hashim, Siti Zaiton (2020) Feature engineering for predicting MOOC performance. In: Sustainable and Integrated Engineering International Conference 2019, 8 - 9 December 2019, Putrajaya, Malaysia. http://dx.doi.org/10.1088/1757-899X/884/1/012070 |
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QA75 Electronic computers. Computer science Mohamad, Nadirah Ahmad, Nor Bahiah Abang Jawawi, Dayang Norhayati Mohd. Hashim, Siti Zaiton Feature engineering for predicting MOOC performance |
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Increasing data recorded in massive open online course (MOOC) requires more automated analysis. The analysis, which includes making student's prediction requires better strategy to produce good features and reduces prediction error. This paper presents the process of feature engineering for predicting MOOC student's performance utilizing deep feature synthesis (DFS) method. The experiment produces features which all the top features selected using principal component analysis (PCA) are the features that are generated from method. In terms of prediction comparing both based features and generated features, the result shows better accuracy for generated features proposed using k-nearest neighbours technique which shows the method potential to be used for future prediction model. |
format |
Conference or Workshop Item |
author |
Mohamad, Nadirah Ahmad, Nor Bahiah Abang Jawawi, Dayang Norhayati Mohd. Hashim, Siti Zaiton |
author_facet |
Mohamad, Nadirah Ahmad, Nor Bahiah Abang Jawawi, Dayang Norhayati Mohd. Hashim, Siti Zaiton |
author_sort |
Mohamad, Nadirah |
title |
Feature engineering for predicting MOOC performance |
title_short |
Feature engineering for predicting MOOC performance |
title_full |
Feature engineering for predicting MOOC performance |
title_fullStr |
Feature engineering for predicting MOOC performance |
title_full_unstemmed |
Feature engineering for predicting MOOC performance |
title_sort |
feature engineering for predicting mooc performance |
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2020 |
url |
http://eprints.utm.my/id/eprint/94077/1/NorBahiahAhmad2020_FeatureEngineeringforPredictingMOOCPerformance.pdf http://eprints.utm.my/id/eprint/94077/ http://dx.doi.org/10.1088/1757-899X/884/1/012070 |
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1726791477502148608 |
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13.18916 |