Predicting Academic Performance of Deaf Students Using Feed Forward Neural Network and An Improved PSO Algorithm

Durga, V. Sathya; Jeyaprakash, Thangakumar

doi:10.14704/web/v18si01/web18048

Cited by 8 publications

(8 citation statements)

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“…e findings reveal that the NN built with this approach performs better than the NN built with the basic PSO algorithm. is study's mean squared error is 0.0998, which is lower than several previous models [3]. When the allowable space of state for UAVs is severely limited, a method for picking space of state is more efficient than control space's sampling.…”

Section: Related Workmentioning

confidence: 80%

PSO Algorithm-Based Design of Intelligent Education Personalization System

Yang

2022

Computational Intelligence and Neuroscience

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The application of artificial intelligence in the field of education is becoming more and more extensive and in-depth. The intelligent education system can not only solve the limitations of location, time, and resources in the traditional learning field but it can also provide learners with a convenient, real-time, and interactive learning environment and has become one of the important applications in the Internet field. Particle swarm optimization (PSO) is a swarm intelligence-enabled stochastic optimization scheme. It is derived from a model of bird population foraging behavior. Because of its benefits of ease of implementation, high accuracy, and quick convergence, this algorithm has gained the attention of academics, and it has demonstrated its supremacy in addressing real issues. This paper aims to study the optimization of PSO in the field of computational intelligence, improve the matching degree of learning resource recommendation and learning path optimization, and improve the learning efficiency of online learners. This paper suggests intelligent education as the center, takes the PSO algorithm as the main research object, and expounds the related concepts of intelligent education and PSO algorithm. It uses swarm intelligence algorithms for intelligent education personalized services. He focuses on PSO algorithm and its work in intelligent education recommendation and learning path planning. Experiments show that the average value of the difference between the two obtained by the NBPSO algorithm is 1.13E + 02 and the variance 1.88E + 02 is the smallest. Therefore, PSO aids in improving the quality and consistency of online course resource development. In conclusion, the research results of this paper further demonstrate the advantages of PSO algorithm in solving the problem of personalized service in intelligent education. It can promote the in-depth application of swarm intelligence optimization algorithms in intelligent online learning systems. This effectively enhances the intelligent service level of the online learning system and increases the efficiency of online learning.

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Section: Related Workmentioning

confidence: 80%

PSO Algorithm-Based Design of Intelligent Education Personalization System

Yang

2022

Computational Intelligence and Neuroscience

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“…According to the research done [15], the MLP classifier with the SMOTE technique performs better than the ML algorithm used. A significant improvement in accuracy can be achieved by basing the model's development on the input of particular variables [8], [10], [18], [20]. LSTM is a subtype of recurrent neural networks (RNN) [33].…”

Section: 3mentioning

confidence: 99%

“…Unbalanced data can be handled in a variety of ways, including data level (preprocessing) and algorithm-based methods [8], [9]. The Synthetic Minority Over Sampling Technique (SMOTE), which creates synthetic samples between minority samples and their neighbors, is one of the most well-known preprocessing techniques [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Student Performance Prediction through LSTM-based Deep Learning Models with Unbalanced Data Handling using Oversampling Approach

Ismanto,

Ghani,

Aziz

et al. 2023

Advances in Social Science, Education and Humanities Research

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Accurate prediction of student performance is crucial in learning analytics to prevent course failures and improve academic outcomes. However, publicly accessible educational data often contains noise and imbalanced data distributions, requiring effective handling techniques. In this study, we propose a novel approach that combines the Synthetic Minority Over-sampling Technique (SMOTE) with Long Short-Term Memory (LSTM) and Feed-Forward Neural Network (FFNN) models for performance prediction in virtual learning environments (VLEs). Our experimental results show that utilizing the SMOTE technique significantly improves the accuracy of predicting student withdrawals, with the LSTM model achieving the highest accuracy of 94.90% in the 25th week of data testing. These findings indicate the effectiveness of the SMOTE technique in addressing data imbalance issues in VLE datasets and the potential of our proposed deep learning models in accurately predicting student performance. The implications of our study are significant for learning analytics and educational institutions, as accurate prediction of student performance can inform early interventions and personalized support. Future research could explore the generalizability of our approach in diverse educational contexts and the integration of additional features for further improving prediction accuracy. Hence, our study contributes to the field of learning analytics by proposing a novel approach that combines SMOTE with deep learning models for student performance prediction in VLEs. Our findings highlight the potential of our approach in addressing data imbalance challenges and accurately predicting student performance, with implications for enhancing student success in educational settings.

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“…Various deep learning, ensemble, hybrid models are used to predict the at-risk students [3134, 65,52,66]. The weight adjustment problems of neural networks are addressed using particle swarm optimization techniques [67]. To make the models transparent, they are made explainable in [38,40,42] which helps to explain the factors that positively and negatively affect the at risk student prediction.…”

Section: 12prediction Of At-risk Studentsmentioning

confidence: 99%

Closing the gap: exploring the untapped potential of machine learning in deaf students and hearing students’ academic performance

2023

IJATEE

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Modern universities use several methods to improve the learning environment. This includes analyzing the academic performance of students and planning various intervention programs to support the students to overcome the difficulties they face. The performance prediction at the time of admission and in the subsequent years helps universities effectively plan the intervention strategies which in turn can help both management and educators benefit from the students' performance prediction plans [1]. Educational data mining (EDM) and machine learning (ML) methods are gaining popularity in the educational domain.*Author for correspondence Many applications using these techniques are used to analyse student performance in academics.The EDM and ML-based applications help instructors to identify struggling students earlier and take action to improve success and retention. Researchers are using these techniques to accelerate their research to uncover discoveries and insights. Though the role of ML and EDM is explored considerably in the education of hearing students, its benefits are not used properly in deaf education. Research suggests that deaf young adults are less likely than their hearing peers to live independently[2], are underemployed and underpaid [3], and are less likely to obtain an undergraduatelevel degree [4]. The opportunities for higher

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Predicting Academic Performance of Deaf Students Using Feed Forward Neural Network and An Improved PSO Algorithm

Cited by 8 publications

References 0 publications

PSO Algorithm-Based Design of Intelligent Education Personalization System

PSO Algorithm-Based Design of Intelligent Education Personalization System

Enhancing Student Performance Prediction through LSTM-based Deep Learning Models with Unbalanced Data Handling using Oversampling Approach

Closing the gap: exploring the untapped potential of machine learning in deaf students and hearing students’ academic performance

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