A new ML-based approach to enhance student engagement in online environment

Ayouni, Sarra; Hajjej, Fahima; Maddeh, Mohamed; Al-Otaibi, Shaha

doi:10.1371/journal.pone.0258788

Cited by 17 publications

(5 citation statements)

References 34 publications

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“…For instance, Gitinabard et al [ 3 ] use decision trees to select features and use logistic regression to predict dropouts. Ayouni et al [ 4 ] trained Decision Tree (DT) and Support Vector Machines (SVM) to classify a student into different engagement levels. However, these methods remain a need for meticulously designed feature engineering, limiting their applicability.…”

Section: Related Workmentioning

confidence: 99%

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A survival analysis based volatility and sparsity modeling network for student dropout prediction

et al. 2022

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Student Dropout Prediction (SDP) is pivotal in mitigating withdrawals in Massive Open Online Courses. Previous studies generally modeled the SDP problem as a binary classification task, providing a single prediction outcome. Accordingly, some attempts introduce survival analysis methods to achieve continuous and consistent predictions over time. However, the volatility and sparsity of data always weaken the models’ performance. Prevailing solutions rely heavily on data pre-processing independent of predictive models, which are labor-intensive and may contaminate authentic data. This paper proposes a Survival Analysis based Volatility and Sparsity Modeling Network (SAVSNet) to address these issues in an end-to-end deep learning framework. Specifically, SAVSNet smooths the volatile time series by convolution network while preserving the original data information using Long-Short Term Memory Network (LSTM). Furthermore, we propose a Time-Missing-Aware LSTM unit to mitigate the impact of data sparsity by integrating informative missingness patterns into the model. A survival analysis loss function is adopted for parameter estimation, and the model outputs monotonically decreasing survival probabilities. In the experiments, we compare the proposed method with state-of-the-art methods in two real-world MOOC datasets, and the experiment results show the effectiveness of our proposed model.

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Section: Related Workmentioning

confidence: 99%

“…Traditional approaches for modeling SDP are generally based on machine learning algorithms such as Logistic Regression(LR) [ 3 ] and Support Vector Machines (SVM) [ 4 ] to fit time-independent features. Unfortunately, these methods often lose the ability to capture complex temporal dependency between students’ interactive activities.…”

Section: Introductionmentioning

confidence: 99%

A survival analysis based volatility and sparsity modeling network for student dropout prediction

et al. 2022

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“…where 𝑟 defines the size of the tree, which is fixed, the optimal weights 𝑥 𝑘 * of leaf 𝑗 is obtained through the (14).…”

Section: Xgboost Prediction Algorithmmentioning

confidence: 99%

“…Here different ML models and an ensemble learning mechanism are constructed for predicting student performance during the course. The outcome shows ensemble model outperforms another model in terms of prediction accuracy [14]- [16]. However, when data is imbalanced these model fails to establish feature affecting the predictive model; thus, providing poor classification accuracies.…”

Section: Introductionmentioning

confidence: 97%

Machine learning based education data mining through student session streams

Hanumanthappa,

Prakash

2024

IJRES

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Recently, significant growth in using online-based learning stream (i.e., elearning systems) have been seen due to pandemic such as COVID-19. Forecasting student performance has become a major task as an institution is focusing on improving the quality of education and students' performance. Data mining (DM) employing machine learning (ML) techniques have been employed in the e-learning platform for analyzing student session streams and predicting academic performance with good effects. A recent, study shows ML-based methodologies exhibit when data is imbalanced. In addressing ensemble learning by combining multiple ML algorithms for choosing the best model according to data. However, the existing ensemblebased model does not incorporate feature importance into the student performance prediction model. Thus, exhibits poor performance, especially for multi-label classification. In addressing this, this paper presents an improved ensemble learning mechanism by modifying the XGBoost algorithm, namely modified XGBoost (MXGB). The MXGB incorporates an effective cross-validation scheme that learns correlation among features more efficiently. The experiment outcome shows the proposed MXGBabased student performance prediction model achieves much better prediction accuracy contrary to the state-of-art ensemble-based student performance prediction model.

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“…In addition to the above studies, there are numerous studies conducted on automatically detecting student engagement and participation in class by utilizing image processing techniques based on the gaze direction determined the student engagement and participation based on their facial expressions [45][46][47][48][49][50][51]. In China, a system determining the students' attentions based on their facial expressions was developed and this system was tested as a pilot application in some schools [52].…”

Section: Studies On Determining Distraction and Gaze Directionmentioning

confidence: 99%

Recognizing Students and Detecting Student Engagement with Real-Time Image Processing

Ucar¹,

Özdemir

2022

Electronics

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With COVID-19, formal education was interrupted in all countries and the importance of distance learning has increased. It is possible to teach any lesson with various communication tools but it is difficult to know how far this lesson reaches to the students. In this study, it is aimed to monitor the students in a classroom or in front of the computer with a camera in real time, recognizing their faces, their head poses, and scoring their distraction to detect student engagement based on their head poses and Eye Aspect Ratios. Distraction was determined by associating the students’ attention with looking at the teacher or the camera in the right direction. The success of the face recognition and head pose estimation was tested by using the UPNA Head Pose Database and, as a result of the conducted tests, the most successful result in face recognition was obtained with the Local Binary Patterns method with a 98.95% recognition rate. In the classification of student engagement as Engaged and Not Engaged, support vector machine gave results with 72.4% accuracy. The developed system will be used to recognize and monitor students in the classroom or in front of the computer, and to determine the course flow autonomously.

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A new ML-based approach to enhance student engagement in online environment

Cited by 17 publications

References 34 publications

A survival analysis based volatility and sparsity modeling network for student dropout prediction

A survival analysis based volatility and sparsity modeling network for student dropout prediction

Machine learning based education data mining through student session streams

Recognizing Students and Detecting Student Engagement with Real-Time Image Processing

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