Student Performance Prediction via Attention-Based Multi-Layer Long-Short Term Memory

Xie, Yanqing

doi:10.4236/jcc.2021.98005

Cited by 14 publications

(6 citation statements)

References 33 publications

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“…Xie et al proposed a model for predicting grades through an attentive multilayer LSTM that combined students' demographic and clickstream datasets for integrated analysis. The results showed higher prediction accuracy and could provide timely interventions [14].…”

Section: Related Workmentioning

confidence: 94%

A Study on the Prediction Method of English Performance in Universities based on the Stacking Integrated Model

2024

SCPE

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Students’ performance in higher education reflects their overall quality in higher education. By predicting the performance, students with greater learning problems can be screened out early and given appropriate guidance. To predict students’ performance in English, the knowledge information of courses, examination papers, and historical examination records are used to build a feature project of students’ examinations. Meanwhile, the features strongly correlated with their performance are filtered out. Then the next step of performance prediction is carried out. The results showed that a neural network long and short-term memory performance prediction model incorporating an attention mechanism was more effective than other models in predicting English performance in higher education. Further experiments found that the model reduced the error by 1.04% on the MAE metric, 0.53% on the RMSE metric, and increased its value by 4.12% on the R2 metric. Adding the new feature dataset led to better forecasting by the Att-LSTM model in all metrics. This indicated that the enhanced dataset temporality could improve the effectiveness of the Att-LSTM model in predicting English grades in higher education. The stacked integrated prediction model, by integrating multiple strong regressors, can avoid poor prediction and excessive overall bias due to one regressor and increase the soundness and prediction precision of the mode.

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Section: Related Workmentioning

confidence: 94%

A Study on the Prediction Method of English Performance in Universities based on the Stacking Integrated Model

2024

SCPE

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“…The NAG and Adam algorithms were combined to create the Nadam algorithm [22], [29]. Nadam performs a momentum update for the value of 𝑚 ̂𝑡 [32].…”

Section: Nadam Optimizermentioning

confidence: 99%

“…To fine-tune the hyperparameters, we used the adaptive moment estimation (Adam) and Nesterov-accelerated adaptive moment estimation (Nadam) optimization algorithms. Adam and Nadam, are the two most effective gradient descent optimization algorithms [21], [22].…”

Section: Introductionmentioning

confidence: 99%

An LSTM-based prediction model for gradient-descending optimization in virtual learning environments

Ismanto,

Effendi

2024

Comput Sci Inf Technol

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A virtual learning environment (VLE) is an online learning platform that allows many students, even millions, to study according to their interests without being limited by space and time. Online learning environments have many benefits, but they also have some drawbacks, such as high dropout rates, low engagement, and students' self-regulated behavior. Evaluating and analyzing the students' data generated from online learning platforms can help instructors to understand and monitor students learning progress. In this study, we suggest a predictive model for assessing student success in online learning. We investigate the effect of hyperparameters on the prediction of student learning outcomes in VLEs by the long short-term memory (LSTM) model. A hyperparameter is a parameter that has an impact on prediction results. Two optimization algorithms, adaptive moment estimation (Adam) and Nesterov-accelerated adaptive moment estimation (Nadam), were used to modify the LSTM model's hyperparameters. Based on the findings of research done on the optimization of the LSTM model using the Adam and Nadam algorithm. The average accuracy of the LSTM model using Nadam optimization is 89%, with a maximum accuracy of 93%. The LSTM model with Nadam optimisation performs better than the model with Adam optimisation when predicting students in online learning.

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“…LSTM is utilized in predicting equity price with corporate action events, and student performance prediction. In addition, their application for mining public opinion forecasting, as well as in the public health field for State of health estimation of Lithium-Ion batteries [20][21][22][23]. In its term, the GRU is applied for several uses; including, the prediction of reservoir parameters through well-logging data [24].…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting material quantity using machine learning and times series techniques

Zermane,

Madjour,

Ziar

et al. 2024

Journal of Electrical Engineering

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The current research is dedicated to harnessing cutting-edge technologies within the paradigm of Industry 5.0. The objective is to capitalize on advancements in Machine and Deep Learning techniques. This research endeavors to construct robust predictive models, utilizing historical data, for precise real-time predictions in estimating material quantities within a cement workshop. Machine Learning regressors evaluated based on several metrics, SVR (R-squared 0.9739, MAE 0.0403), Random Forest (R-squared 0.9990, MAE 0.0026), MLP (R-squared 0.9890, MAE 0.0255), Gradient Boosting (R-squared 0.9989, MAE 0.0042). The time series models LSTM and GRU yielded R-squared 0.9978, MAE 0.0100, and R-squared 0.9980, MAE 0.0099, respectively. The ultimate outcomes include improved and efficient production, optimization of production processes, streamlined operations, reduced downtime, mitigation of potential disruptions, and the facilitation of the factory’s evolution towards intelligent manufacturing processes embedded within the framework of Industry 5.0. These achievements underscore the potential impact of leveraging advanced machine learning techniques for enhancing the operational dynamics and overall efficiency of manufacturing facilities

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Student Performance Prediction via Attention-Based Multi-Layer Long-Short Term Memory

Cited by 14 publications

References 33 publications

A Study on the Prediction Method of English Performance in Universities based on the Stacking Integrated Model

A Study on the Prediction Method of English Performance in Universities based on the Stacking Integrated Model

An LSTM-based prediction model for gradient-descending optimization in virtual learning environments

Forecasting material quantity using machine learning and times series techniques

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