The Application Trend and Advantages of Mobile Technology in Higher Education

Li, Bo

doi:10.1155/2022/3874673

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…This will significantly boost the use of wireless technology for mobility in colleges and universities, and the overall performance has increased to 67%. Figure 4 illustrates how the number of signal nodes impacts the accuracy of the positioning algorithm, irrespective of whether the website is asymmetrical or a sinusoidal network [17]. The positioning accuracy increases with the number of signal nodes.…”

Section: Results Analysis and Discussionmentioning

confidence: 99%

Learning Management System in Education via Mobile App: Trends and Patterns in Mobile Learning

Zhang,

Hai,

2024

Int. J. Interact. Mob. Technol.

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The mobile revolution has influenced students’ preferences for various educational platforms in the new digital era, especially regarding young learners’ utilization of mobile devices such as smartphones, iPads, and other gadgets for mobile learning (m-learning). Because of this, universities that implement learning management systems (LMS) through standard web-based platforms should explore the potential for integrating mobile devices and technologies into m-learning platforms. Through the utilization of this technology, LMS can facilitate continuous user interaction and enhance user awareness of any revisions made to the material. To reach the majority of LMS users, mobile applications must be developed for all major mobile platforms. By utilizing the Web View API, this research combined native mobile and web technologies to develop the mobile application. This strategy was adopted in anticipation of the requirement to create and maintain the application across multiple mobile platforms. It was anticipated that this approach would reduce the time needed for creation, maintain a consistent interface, and enable the use of platform-specific features. It also makes sense to provide mobile device access to some of the LMS virtual classroom’s functions. The K-means algorithm is used for analyzing course material and learning. Nevertheless, achieving this goal might not be an easy process. To enable this form of connection between the LMS and the m-learning applications, this chapter assesses the challenges involved in achieving that goal and presents various common interchange designs and related research and development efforts.

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Section: Results Analysis and Discussionmentioning

confidence: 99%

Learning Management System in Education via Mobile App: Trends and Patterns in Mobile Learning

Zhang,

Hai,

2024

Int. J. Interact. Mob. Technol.

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Innovative Application of Data Mining Technology in College Information System Based on Informatized Teaching Environment

Chen

2024

Applied Mathematics and Nonlinear Sciences

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With the rapid development of information technology, the development of information technology has become the main focus of national development, and the informationization of education in colleges and universities has also been mentioned as a strategic height, and various fields such as student management and teaching methods are also being continuously intelligentized and informative. In this paper, we obtain relevant data from the information systems of colleges and universities and use the K-Means clustering algorithm based on information entropy and density optimization to cluster and analyze educational data. The optimal clustering coefficient is used to determine major school behavioral characteristics, such as students’ academic performance, consumption amount, access control records, and so forth. It can be analyzed. Are respectively used as the clustering dimensions of K-Means, and the information entropy is used to determine the attribute weight values to classify these dimensions, and to find out the common prominent characteristics in their same class. Then analyze the laws of student behavior and cluster them into categories. The academic early warning model evaluation index is used to verify the model’s accuracy. Among them, the academic early warning model proposed in this paper has an accuracy rate of 95.2%, while the precision rate and recall rate at different warning levels reach more than 90%. The validity of the model has been verified. The study shows that the introduction of educational data mining technology can solve the pain points for modeling and analyzing diverse data and accurately predict student behavior.

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The Application Trend and Advantages of Mobile Technology in Higher Education

Cited by 2 publications

References 26 publications

Learning Management System in Education via Mobile App: Trends and Patterns in Mobile Learning

Learning Management System in Education via Mobile App: Trends and Patterns in Mobile Learning

Innovative Application of Data Mining Technology in College Information System Based on Informatized Teaching Environment

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