An Early Prediction of Dropouts for At-risk Scholars in MOOCs using Deep Learning

Anjali, C A

doi:10.1109/ic3sis54991.2022.9885328

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…Therefore, in this paper, we design a set of multivariate time series features to represent students' online learning status. On the one hand, multivariate time series features can be used together with other structured statistical features (e.g., [10], [11], [12]) to form new features whose expressive power is stronger. On the other hand, multivariate time series features can express more information about students' online learning status than univariate series features (e.g., [13], [14], [15]) and can capture the spatial-temporal correlation, providing a more comprehensive response to learners' learning status.…”

Section: Related Workmentioning

confidence: 99%

A Study of Online Academic Risk Prediction Based on Multivariate Time Series Features

Yu,

Hou,

Huang

et al. 2023

Preprint

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Academic risk prediction is a hot topic in the field of big data in education that aims to identify and help students who experience great academic difficulties. In recent years, the use of machine learning algorithms to achieve academic risk prediction has garnered more attention and development. However, most of these studies use static statistics as features for prediction, which are slightly insufficient in terms of timeliness. To be able to capture students who have difficulties in course learning in a timely manner and to improve the academic performance of school students, this paper proposes a method based on multivariate time series features to predict academic risk. The method includes three steps: first, the multivariate time series feature is extracted from the interaction records of the students' online learning platforms; second, the multivariate time series feature transformation model ROCKET is applied to convert the multivariate time series feature into a new feature; third the new feature is converted into a final prediction result. Comparative tests show that the proposed method has high effectiveness.

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Section: Related Workmentioning

confidence: 99%

A Study of Online Academic Risk Prediction Based on Multivariate Time Series Features

Yu,

Hou,

Huang

et al. 2023

Preprint

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A study of online academic risk prediction based on neural network multivariate time series features

Wu,

Yu,

Huang

et al. 2024

Concurrency and Computation

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SummaryNeural networks are becoming increasingly widely used in various fields, especially for academic risk forecasts. Academic risk prediction is a hot topic in the field of big data in education that aims to identify and help students who experience great academic difficulties. In recent years, the use of machine learning algorithms and deep learning algorithms to achieve academic risk prediction has garnered increased attention and development. However, most of these studies use nontime series data as features for prediction, which are slightly insufficient in terms of timeliness. Therefore, this article focuses on time series data features that are more expressive of changes in students' learning status and uses multivariate time series data as predictive features. This article proposes a method based on multivariate time series features and a neural network to predict academic risk. The method includes three steps: first, the multivariate time series feature is extracted from the interaction records of the students' online learning platforms; second, the multivariate time series feature transformation model ROCKET is applied to convert the multivariate time series feature into a new feature; third, the new feature is converted into a final prediction result using a linear classification model. Comparative tests show that the proposed method has high effectiveness.

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Educational Data Mining: A Systematic Review on the Applications of Classical Methods and Deep Learning Until 2022

Trung,

Son,

Tung

et al. 2023

2023 IEEE Symposium on Industrial Electronics &Amp; Applications (ISIEA)

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An Early Prediction of Dropouts for At-risk Scholars in MOOCs using Deep Learning

Cited by 3 publications

References 6 publications

A Study of Online Academic Risk Prediction Based on Multivariate Time Series Features

A Study of Online Academic Risk Prediction Based on Multivariate Time Series Features

A study of online academic risk prediction based on neural network multivariate time series features

Educational Data Mining: A Systematic Review on the Applications of Classical Methods and Deep Learning Until 2022

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