Big Data Technology-Enabled Analytical Solution for Quality Assessment of Higher Education Systems

Khan, Samiya; Alam, Mansaf; Liu, Xiufeng; Shakil, Kashish Ara

doi:10.14569/ijacsa.2019.0100640

Cited by 6 publications

(2 citation statements)

References 28 publications

(36 reference statements)

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“…On the one hand, utilising information technology to enhance the process of evaluating educational quality can lighten the workload. On the other hand, implementing universal criteria for evaluation can increase evaluation accuracy to some extent [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Higher Education Environment Monitoring and Quality Assessment Model Using Big Data Analysis and Deep Learning

2022

Journal of Environmental and Public Health

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A university exists to help students develop their skills. The goal of university development is to raise the standard of personnel training, and the core component of university development is teaching quality. Whether the level of practical training can as soon meet the requirements of enterprise employment. For responding to the demands of professions and occupations, it is essential. Teaching quality evaluation is a crucial cornerstone for ensuring the quality of instruction. Universities and colleges should therefore concentrate on evaluating instruction. Schools and colleges can more rapidly and thoroughly comprehend how their off-campus cooperative adult education programmes are running while also enhancing the efficacy and impartiality of their quality assessment by utilising educational data mining and learning analysis technology. Currently, issues with backward evaluation instruments, a single evaluation topic, and easy evaluation methods exist when evaluating the quality of schooling. Big data technology is used to create a higher vocational education environment monitoring and quality evaluation system that offers new and varied approaches to evaluate teaching quality. The technique for evaluating the quality of schooling is expanded upon in this research using various big data mining technologies. The improved collaborative filtering algorithm’s mean absolute difference is approximately 18.23% when the data set is larger. In conclusion, when applied to big data sets, the technique in this work performs with greater accuracy than the conventional collaborative filtering algorithm. The scoring matrix becomes denser as there are more scoring items in the model. In turn, this results in a more accurate similarity calculation at the beginning of the programme, albeit the similarity calculation error increases as the scoring matrix becomes denser.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Higher Education Environment Monitoring and Quality Assessment Model Using Big Data Analysis and Deep Learning

2022

Journal of Environmental and Public Health

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“…Cloud computing technologies and extensive data analysis are necessary for many platforms, as noted in Alam and Shakil (2016) [2] . (Khan et al 2016(Khan et al , 2018(Khan et al , 2019a, b, c, d; Shakil et al 2017) [17][18][19] . The recent increase in real-time applications that require low latency has necessitated a transition to processing architectures that utilize fog or edge computing.…”

Section: An Introduction To Iotmentioning

confidence: 99%

Development of machine language for internet of things

Oluwatoyin¹,

Dauda²,

Enem³

2021

Int. J. Comput. Artif. Intell.

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The fast growth in the number of smart devices capable of running complex apps significantly impacts the information communication technology industry's landscape. The Internet of Things (IoT) continues to grow in popularity and relevance in man's daily existence. However, as the Internet of Things evolves, so do the associated problems. Thus, the need for IoT development and ongoing upgrading becomes stronger. To maximize the potential of IoT systems, machine learning technologies have recently been used. The implementation of machine learning algorithms in IoT systems is examined in detail in this paper. Two categories of machine learning-based IoT algorithms deal with fundamental IoT challenges like localization, clustering, routing, and data aggregation. Additional machine learning-based IoT algorithms deal with performance challenges like congestion control, fault detection, resource management, and security.

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Surveillance of Type –I & II Diabetic Subjects on Physical Characteristics: IoT and Big Data Perspective in Healthcare @NCR, India

Rastogi

Chaturvedi

Satya

et al. 2020

Internet of Things (IoT)

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Big Data Technology-Enabled Analytical Solution for Quality Assessment of Higher Education Systems

Cited by 6 publications

References 28 publications

[Retracted] Higher Education Environment Monitoring and Quality Assessment Model Using Big Data Analysis and Deep Learning

[Retracted] Higher Education Environment Monitoring and Quality Assessment Model Using Big Data Analysis and Deep Learning

Development of machine language for internet of things

Surveillance of Type –I & II Diabetic Subjects on Physical Characteristics: IoT and Big Data Perspective in Healthcare @NCR, India

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