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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Bibliographic Details
Published inIOP conference series. Materials Science and Engineering Vol. 884; no. 1; pp. 12070 - 12076
Main Authors Mohamad, Nadirah, Ahmad, Nor Bahiah, Jawawi, Dayang Norhayati Abang, Hashim, Siti Zaiton Mohd
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.07.2020
Subjects
Engineering education
Error reduction
Performance prediction
Prediction models
Principal components analysis
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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.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/884/1/012070