Big Data Application and its Impact on Education

Khan, Shakir; Alqahtani, Salihah

doi:10.3991/ijet.v15i17.14459

Cited by 35 publications

(33 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Big Data [17] refers to large data that requires complex processing and which also poses a problem in terms of storage. Faced with the problem of massive data storage and processing, new technologies are needed.…”

Section: Big Datamentioning

confidence: 99%

Predicting Student Success Using Big Data and Machine Learning Algorithms

Ouatik¹,

Erritali²,

Ouatik³

et al. 2022

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

The prediction of student performance, allows teachers to track student results to react and make decisions that affect their learning and performance, given the importance of monitoring students to fight against academic failure. We realized a system of the prediction of academic success and failure of the students, which is the overall result and the goal of the educational system. We used the personal information of the students, the academic evaluation, the activities of the students in VLE, Psychological, the student environment, and we added practical work and homework, mini projects, and the number of student absences which gives a vision of the quality of the student. Then we applied the methods of artificial intelligence and educational Data mining such as KNN, C4.5 and SVM for the prediction of the academic success of students, but these methods are not sufficient given the progressive number of students, specialties, learning methods and the diversity of data sources as well as student data processing time. To solve this problem, Big Data technology was used to distribute the processing in order to minimize the execution time without losing the efficiency of the algorithms used. In this system we cleaned the data and then applied the property selection algorithms to find the useful properties in order to improve the algorithm prediction rate and also to reduce the execution time. Finally, we stored the data in HDFS and we applied the classification algorithms for the prediction of student success using MAPREDUCE. We compared the results before and after the use of big data and we found that the results after the use of Big Data are very good at execution time and we arrived at a recognition rate of 87.32% by the SVM algorithm.

show abstract

Section: Big Datamentioning

confidence: 99%

Predicting Student Success Using Big Data and Machine Learning Algorithms

Ouatik¹,

Erritali²,

Ouatik³

et al. 2022

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

show abstract

“…It is critical to perceive that too many affected people may exhaust or cripple available medical resources, as happened in Wuhan, which was then saved by an injection of medical resources (medical staff and supplies) from the country's rest. Although the prediction indicates the continuation of the situation in a stable manner compared to the situation of other countries such as US and France [16], covid-19 is still an unclear contagious disease, which means that we can only get an accurate prediction for SEIR after the occurrence has ended. Nevertheless, even though the SEIR model is a numerical simulation, the numbers give us an insight into how high the number of covid-19 cases can arise.…”

Section: Evaluation Of Control Measuresmentioning

confidence: 94%

Visual Data Analysis and Simulation Prediction for COVID-19 in Saudi Arabia Using SEIR Prediction Model

Khan

2021

Int. J. Onl. Eng.

Self Cite

View full text Add to dashboard Cite

<p>The World Health Organization (WHO) reported the COVID-19 epidemic a global health emergency on January 30 and confirmed its transformation into a pandemic on March 11. China has been the hardest hit since the virus's outbreak, which may date back to late November. Saudi Arabia realized the danger of the Coronavirus in March 2020, took the initiative to take a set of pre-emptive decisions that preceded many countries of the world, and worked to harness all capabilities to confront the outbreak of the epidemic. Several researchers are currently using various mathematical and machine learning-based prediction models to estimate this pandemic's future trend. In this work, the SEIR model was applied to predict the epidemic situation in Saudi Arabia and evaluate the effectiveness of some epidemic control measures, and finally, providing some advice on preventive measures.</p>

show abstract

“…Big data is also recognized as a game-changer, capable of changing the way firms function in various organizations for long-term competitive advantage (Muhammad et al, 2020). Big data is used in a variety of sectors; in this paper, we look at how big data is used in education (Khan & Alqahtani, 2020). Universities must continue to play their role as proving grounds for educating the future generation and innovation, based on big data, in improving the education process, and outlines the challenges associated with big data mining, storage, and security in order to respond to the needs of digital transformation (Mkrttchian et al, 2021).…”

Section: Sustainability As a Purpose Of Educationmentioning

confidence: 99%

Adaptation of Big Data: an empirical investigation for sustainability of education

Alismaiel¹

2021

JESI

View full text Add to dashboard Cite

The current study aims at developing a framework to assess big data use for education and encompassing the theoretical background of knowledge sharing and diffusion of innovations in the educational environment. This study hypothesizes that age and cultural diversity, and motivators can influence knowledge sharing, whereas the constructs of relative advantage, trialability, complexity, observability would impact innovations. Thus, innovations influence knowledge sharing and would be positively associated with behavioural intention to use big data and sustainability for education. This study utilized a version of knowledge sharing model and Diffusion of Innovations (DOI) theory as the study framework and implemented quantitative approach for data analysis by collecting 494 responses from university students who were elected using stratified random sampling technique. The data were processed using eleven factors to unveil and understand the predictors of big data use for education sustainability. The study adopts the quantitative approach and employs structure equation modelling (SEM) to data analysis. According to the study's findings, age and cultural diversity and motivators significantly determine knowledge management sharing, while relative advantage, trialability, complexity, and observability have a positive impact on innovations. The adoption of innovations, knowledge sharing, and big data are able to capture 78.9% of sustainability phenomenon on education. Further, the study concludes by reporting findings and implications for research and practitioners.

show abstract

Big Data Application and its Impact on Education

Cited by 35 publications

References 32 publications

Predicting Student Success Using Big Data and Machine Learning Algorithms

Predicting Student Success Using Big Data and Machine Learning Algorithms

Visual Data Analysis and Simulation Prediction for COVID-19 in Saudi Arabia Using SEIR Prediction Model

Adaptation of Big Data: an empirical investigation for sustainability of education

Contact Info

Product

Resources

About