A survey on big data-enabled innovative online education systems during the COVID-19 pandemic

Cui, Yuhuan; Ma, Zezhong; Wang, Liya; Yang, Aimin; Liu, Qiumei; Kong, Shanshan; Wang, Hong

doi:10.1016/j.jik.2022.100295

Cited by 56 publications

(36 citation statements)

References 51 publications

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“…This platform serves as a tool for facilitating the analysis and interpretation of data, fostering a more thorough evaluation of education and teaching practices through data utilization. According to existing research, using big data technology is a valuable strategy for improving the quality of teaching and tackling various educational obstacles ( Cui, Y., et. al., 2023).…”

Section: Implication Of Big Data In the Context Of The Education Syst...mentioning

confidence: 99%

Working and Impact of Parliamentary Committees in the UK and Bangladesh: A Theoretical Analysis

Chowdhury

2022

Dhaka Uni. Law. J.

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Section: Implication Of Big Data In the Context Of The Education Syst...mentioning

confidence: 99%

Working and Impact of Parliamentary Committees in the UK and Bangladesh: A Theoretical Analysis

Chowdhury

2022

Dhaka Uni. Law. J.

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“…Moreover, big data analytics empower educators to predict students' future learning needs based on their past performance and behavior. By analyzing historical data trends, educators can anticipate potential challenges or areas of growth for individual students or entire classes [11]. This proactive approach enables teachers to intervene early, providing targeted support and resources to prevent learning gaps from widening.…”

Section: Introductionmentioning

confidence: 99%

Quality Improvement Model of English Teaching in Universities Based on Big Data Mining

Lei Yu, Yunyun Zhu

2024

jes

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Big data mining in English teaching revolutionizes language instruction by leveraging vast amounts of data to personalize and optimize learning experiences. This approach utilizes sophisticated algorithms to analyze learners' language usage patterns, comprehension levels, and areas of difficulty. This paper presents a novel Quality Improvement Model of English Teaching (QIMET) tailored for universities, integrating big data mining techniques with a Stacked Hashing Edge Computing Model (SHECM). Recognizing the importance of enhancing English language proficiency among university students, particularly in an increasingly globalized educational landscape, this research aims to optimize English teaching methodologies through advanced computational approaches. The proposed QIMET framework leverages big data mining to analyze extensive datasets of student performance, language usage patterns, and instructional effectiveness. By extracting valuable insights from these datasets, educators can identify areas for improvement, tailor teaching materials, and personalize learning experiences to meet individual student needs. Simulation analysis of Across a dataset of 500 university students enrolled in English language courses, QIMET achieves an average improvement of 30% in language proficiency scores compared to traditional teaching methods. Moreover, specific language skills exhibit notable enhancements, with vocabulary acquisition increasing by 25%, grammar comprehension by 20%, and communication skills by 35%. The integration of the SHECM enhances the computational efficiency and scalability of the QIMET framework. Real-time data processing and analysis enable educators to make timely interventions and adjustments to teaching strategies, resulting in more responsive and effective English language instruction.

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“…The rapid development of information technology (IT) has facilitated this transition by allowing students to continue their education from a distance. Consequently, traditional classroom education has gradually integrated online and distance learning methods, with distance learning emerging as a new trend in education [ 3 , 4 ]. Distance learning offers learners the flexibility to create a learning environment that transcends spatial and temporal constraints.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Cognitive Load from Electroencephalography Signals Using Long Short-Term Memory Network

2023

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In recent years, the development of adaptive models to tailor instructional content to learners by measuring their cognitive load has become a topic of active research. Brain fog, also known as confusion, is a common cause of poor performance, and real-time detection of confusion is a challenging and important task for applications in online education and driver fatigue detection. In this study, we propose a deep learning method for cognitive load recognition based on electroencephalography (EEG) signals using a long short-term memory network (LSTM) with an attention mechanism. We obtained EEG signal data from a database of brainwave information and associated data on mental load. We evaluated the performance of the proposed LSTM technique in comparison with random forest, Adaptive Boosting (AdaBoost), support vector machine, eXtreme Gradient Boosting (XGBoost), and artificial neural network models. The experimental results demonstrated that the proposed approach had the highest accuracy of 87.1% compared to those of other algorithms, including random forest (64%), AdaBoost (64.31%), support vector machine (60.9%), XGBoost (67.3%), and artificial neural network models (71.4%). The results of this study support the development of a personalized adaptive learning system designed to measure and actively respond to learners’ cognitive load in real time using wireless portable EEG systems.

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A survey on big data-enabled innovative online education systems during the COVID-19 pandemic

Cited by 56 publications

References 51 publications

Working and Impact of Parliamentary Committees in the UK and Bangladesh: A Theoretical Analysis

Working and Impact of Parliamentary Committees in the UK and Bangladesh: A Theoretical Analysis

Quality Improvement Model of English Teaching in Universities Based on Big Data Mining

Prediction of Cognitive Load from Electroencephalography Signals Using Long Short-Term Memory Network

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