Predicting at-risk university students in a virtual learning environment via a machine learning algorithm

Chui, Kwok Tai; Fung, Dennis Chun Lok; Lytras, Miltiadis D.; Lam, Tin Miu

doi:10.1016/j.chb.2018.06.032

Cited by 136 publications

(79 citation statements)

References 31 publications

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“…Additionally, in a study, at-risk students were predicted by deploying various data mining techniques, including Support Vector Machines (SVM), Naïve Bayes Classifier, Decision Tree, K-Nearest Neighbor and Multi-Layer Perceptron, to identify the best prediction modeling method. Logistic Regression was employed as the baseline model (Chui et al, 2018;Marbouti et al, 2016). Assessment engagement pattern is considered to be another parameter that effectively captures the behavior of students and induces a positive impact on their performance (Hussain et al, 2018;Jung & Lee, 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting academic performance of students from VLE big data using deep learning models

Waheed

Hassan

Aljohani

et al. 2020

Computers in Human Behavior

352

145

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Section: Literature Reviewmentioning

confidence: 99%

Predicting academic performance of students from VLE big data using deep learning models

Waheed

Hassan

Aljohani

et al. 2020

Computers in Human Behavior

352

145

View full text Add to dashboard Cite

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“…We performed 5-fold cross-validation. In 5-fold cross validation, each fold was iteratively selected to test the model, and the remaining four folds were used to train the model [ 59 , 60 ]. For group normalization, we fix the number of groups to 8.…”

Section: Resultsmentioning

confidence: 99%

Fusing Visual Attention CNN and Bag of Visual Words for Cross-Corpus Speech Emotion Recognition

Seo

2020

Sensors

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Speech emotion recognition (SER) classifies emotions using low-level features or a spectrogram of an utterance. When SER methods are trained and tested using different datasets, they have shown performance reduction. Cross-corpus SER research identifies speech emotion using different corpora and languages. Recent cross-corpus SER research has been conducted to improve generalization. To improve the cross-corpus SER performance, we pretrained the log-mel spectrograms of the source dataset using our designed visual attention convolutional neural network (VACNN), which has a 2D CNN base model with channel- and spatial-wise visual attention modules. To train the target dataset, we extracted the feature vector using a bag of visual words (BOVW) to assist the fine-tuned model. Because visual words represent local features in the image, the BOVW helps VACNN to learn global and local features in the log-mel spectrogram by constructing a frequency histogram of visual words. The proposed method shows an overall accuracy of 83.33%, 86.92%, and 75.00% in the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), the Berlin Database of Emotional Speech (EmoDB), and Surrey Audio-Visual Expressed Emotion (SAVEE), respectively. Experimental results on RAVDESS, EmoDB, SAVEE demonstrate improvements of 7.73%, 15.12%, and 2.34% compared to existing state-of-the-art cross-corpus SER approaches.

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“…This improves the feature selection efficiency. Afterward, the features are implemented to the designed optimized ANN for training …”

Section: Simulation Resultsmentioning

confidence: 99%

“…Afterward, the features are implemented to the designed optimized ANN for training. 57 In this study, 85% of the data have been adopted for the training and the remained 15% have been adopted for testing. Simulation results have been compared with different state-of-the-art classification methods such as SVM-based classifier, 58 Genetic-based method, 31 particle swarm optimization algorithm, 59 particle swarm optimization-based ANN (NNPSO), 59 optimized ANN based on hybrid particle swarm optimization algorithm and biogeography-based optimization (MLPPSOBBO), 60 and ANN based on deep learning (CNN).…”

Section: Simulation Resultsmentioning

confidence: 99%

Brain tumor diagnosis based on artificial neural network and a chaos whale optimization algorithm

Gong

Gao

Abza

2019

Computational Intelligence

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Accurate and early detection of the brain tumor region has a great impact on the choice of treatment, its success rate, and the follow‐up of the disease process over time. This study presents a new bioinspired technique for the early detection of the brain tumor area to improve the chance of completely healing. The study presents a multistep technique to detect the brain tumor area. Herein, after image preprocessing and image feature extraction, an artificial neural network is used to determine the tumor area in the image. The method is based on using an improved version of the whale optimization algorithm for optimal selection of the features and optimizing the artificial neural network weights for classification. Simulation results of the proposed method are applied to FLAIR, T1, and T2 datasets and are compared with different algorithms. Three performance indexes including correct detection rate, false acceptance rate, and false rejection rate are selected for the system performance analysis. Final results showed the superiority of the proposed method toward the other similar methods.

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Predicting at-risk university students in a virtual learning environment via a machine learning algorithm

Cited by 136 publications

References 31 publications

Predicting academic performance of students from VLE big data using deep learning models

Predicting academic performance of students from VLE big data using deep learning models

Fusing Visual Attention CNN and Bag of Visual Words for Cross-Corpus Speech Emotion Recognition

Brain tumor diagnosis based on artificial neural network and a chaos whale optimization algorithm

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