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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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | 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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| Summary: | 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. |
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