Solar Wind Speed Time-Series Forecasting Based on Long Short-Term Memory (LSTM) Neural Network Model

Dani, Tiar; Priyatikanto, Rhorom; Winarko, Anton; Putri, Gerhana Puannandra

doi:10.1007/978-981-19-0308-3_15

Cited by 1 publication

(1 citation statement)

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“…Therefore, a more scientific and effective way is required to track students' status in the classroom, attract students' attention, achieve knowledge tracking, and improve the quality of distanced English learning [2][3]. In previous studies, some scholars have proposed to apply deep learning to the prediction of knowledge and to build Deep Knowledge Tracing (DKT) models by combining Long Short-Term Networks (LSTM) with history department sequences [4][5].…”

Section: Introductionmentioning

confidence: 99%

A Study on Distance Personalized English Teaching Based on Deep Directed Graph Knowledge Tracking Model

Deng¹

2023

IJACSA

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Despite the continuous development of online education models, the effectiveness of online distance education has never been able to meet people's expectations due to individual difference of learners. How to personalize teaching in a targeted manner and stimulate learners' independent learning ability has become a key issue. In this study, the multidimensional features of the learning process are mined with the help of the BORUTA feature selection model, and the DKVMN-BORUTA model incorporating multidimensional features is established. This optimized deep knowledge tracking method is combined with graph structure rules. Then, an intelligent knowledge recommendation algorithm based on reinforcement learning is used to construct a fusion approach-based model for distanced personalized teaching and learning of English. The results show that the research proposed fused deep-directed graph knowledge tracking with graph structure rules for remote personalized English teaching model has the lowest AUC value of 0.893 and the highest AUC value of 0.921 on each dataset. The prediction accuracy of the research model is 94.3% and the F1 score is 0.92, which is the highest among the studied models, indicating that the proposed model has a strong performance. The fusion model proposed in the study has a higher accuracy rate of knowledge personalization recommendation than the traditional deep knowledge tracking model, and it can help learners save revision time effectively and improve their overall English performance.

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Section: Introductionmentioning

confidence: 99%

A Study on Distance Personalized English Teaching Based on Deep Directed Graph Knowledge Tracking Model

Deng¹

2023

IJACSA

View full text Add to dashboard Cite

show abstract

Solar Wind Speed Time-Series Forecasting Based on Long Short-Term Memory (LSTM) Neural Network Model

Cited by 1 publication

References 11 publications

A Study on Distance Personalized English Teaching Based on Deep Directed Graph Knowledge Tracking Model

A Study on Distance Personalized English Teaching Based on Deep Directed Graph Knowledge Tracking Model

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