Grade prediction from multi-valued click-stream traces via Bayesian-regularized deep neural networks

Learning activities that have been designed linearly in small private online courses and thematic massive open online courses often result in learners undergoing similar learning journeys. While most learners perform activities leading to similar click-stream sequences, they achieve contrasting grad...

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Main Authors: Ng, Kelvin Hongrui, Tatinati, Sivanagaraja, Khong, Andy Wai Hoong
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/159723
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1597232022-06-30T02:41:46Z Grade prediction from multi-valued click-stream traces via Bayesian-regularized deep neural networks Ng, Kelvin Hongrui Tatinati, Sivanagaraja Khong, Andy Wai Hoong School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Grade Prediction Deep Neural Network Learning activities that have been designed linearly in small private online courses and thematic massive open online courses often result in learners undergoing similar learning journeys. While most learners perform activities leading to similar click-stream sequences, they achieve contrasting grades. This phenomenon of similar online learning behavior resulting in different learning outcomes is termed as the multi-valued (MV) problem. Existing grade prediction models are not designed to handle MV inputs and this problem is further aggravated because the MV characteristic cannot be expressed analytically due to the complexity of these sequential inputs. In this work, the detrimental impact of MV inputs on grade prediction is highlighted. A new MV-sensitive grade prediction model is proposed to quantify the MV characteristic of the dataset. As opposed to existing deep neural network based grade prediction models, the proposed algorithm is able to capture the uncertainty associated with each click-stream sequence. This results in the model organizing its feature space to discriminate the MV inputs. With the newly formulated surrogate measures, and by removing these identified MV inputs, experiment results show that the proposed models achieve lower prediction error compared to existing algorithms when evaluated on two online-learning datasets. Nanyang Technological University National Research Foundation (NRF) This work was supported in part by the Project SLE-RP2 within the DeltaNTU Corporate Lab for Cyber-Physical Systems with funding support from Delta Electronics Inc., and in part by the National Research Foundation (NRF) Singapore under the Corp Lab@University Scheme. 2022-06-30T02:41:46Z 2022-06-30T02:41:46Z 2021 Journal Article Ng, K. H., Tatinati, S. & Khong, A. W. H. (2021). Grade prediction from multi-valued click-stream traces via Bayesian-regularized deep neural networks. IEEE Transactions On Signal Processing, 69, 1477-1491. https://dx.doi.org/10.1109/TSP.2021.3057691 1053-587X https://hdl.handle.net/10356/159723 10.1109/TSP.2021.3057691 2-s2.0-85101477784 69 1477 1491 en SLE-RP2 IEEE Transactions on Signal Processing © 2021 IEEE. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Grade Prediction
Deep Neural Network
spellingShingle Engineering::Electrical and electronic engineering
Grade Prediction
Deep Neural Network
Ng, Kelvin Hongrui
Tatinati, Sivanagaraja
Khong, Andy Wai Hoong
Grade prediction from multi-valued click-stream traces via Bayesian-regularized deep neural networks
description Learning activities that have been designed linearly in small private online courses and thematic massive open online courses often result in learners undergoing similar learning journeys. While most learners perform activities leading to similar click-stream sequences, they achieve contrasting grades. This phenomenon of similar online learning behavior resulting in different learning outcomes is termed as the multi-valued (MV) problem. Existing grade prediction models are not designed to handle MV inputs and this problem is further aggravated because the MV characteristic cannot be expressed analytically due to the complexity of these sequential inputs. In this work, the detrimental impact of MV inputs on grade prediction is highlighted. A new MV-sensitive grade prediction model is proposed to quantify the MV characteristic of the dataset. As opposed to existing deep neural network based grade prediction models, the proposed algorithm is able to capture the uncertainty associated with each click-stream sequence. This results in the model organizing its feature space to discriminate the MV inputs. With the newly formulated surrogate measures, and by removing these identified MV inputs, experiment results show that the proposed models achieve lower prediction error compared to existing algorithms when evaluated on two online-learning datasets.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Ng, Kelvin Hongrui
Tatinati, Sivanagaraja
Khong, Andy Wai Hoong
format Article
author Ng, Kelvin Hongrui
Tatinati, Sivanagaraja
Khong, Andy Wai Hoong
author_sort Ng, Kelvin Hongrui
title Grade prediction from multi-valued click-stream traces via Bayesian-regularized deep neural networks
title_short Grade prediction from multi-valued click-stream traces via Bayesian-regularized deep neural networks
title_full Grade prediction from multi-valued click-stream traces via Bayesian-regularized deep neural networks
title_fullStr Grade prediction from multi-valued click-stream traces via Bayesian-regularized deep neural networks
title_full_unstemmed Grade prediction from multi-valued click-stream traces via Bayesian-regularized deep neural networks
title_sort grade prediction from multi-valued click-stream traces via bayesian-regularized deep neural networks
publishDate 2022
url https://hdl.handle.net/10356/159723
_version_ 1738844805136384000