The determination and analysis of factors affecting to student learning by artificial intelligence in higher education

Ozbey, Nigar; Karaköse, Mehmet; Uçar, Ayşegül

doi:10.1109/ithet.2016.7760740

Cited by 7 publications

(5 citation statements)

References 15 publications

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“…Tastimur, Canan (2016) believed that AI would help improve the validity and accuracy of HE quality evaluation [4]. Ozbey, Nigar (2017) believed that AI can provide useful help for HE to solve some complex problems, and analyzed the factors that AI affects college students' learning and identification process [5]. Salgado (2017) dynamically analyzed the changes of scientific management of HE in the era of AI based on ontology and the ecosystem of intelligent tools.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Artificial Intelligence in International Higher Education Field Based on Bibliometrics

Yanting Pan

2024

jes

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Artificial intelligence (AI), as a significant feature of modern development, has changed the form of education, especially with a significant impact on higher education (HE). Clarifying the integration and infiltration of HE and AI is of great significance for the future development of AI in HE. The Citespace is used to make a quantitative and qualitative analysis of the indicators such as authors, institutions, nationalities, disciplines, journals, and highly cited articles of AI research literature -located at HE included in the Web of Science core collection database. The results show that in recent years, AI in the field of HE has received extensive international attention, and the research results are in a growing trend. In the field of international HE, AI focuses on hot topics such as parameter design, deep learning, machine learning human-computer interaction and hybrid learning. And deep learning, learning space simulation design, learning effectiveness measurement, etc. will become an important development direction of international AI research in the field of HE.

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Section: Literature Reviewmentioning

confidence: 99%

Analysis of Artificial Intelligence in International Higher Education Field Based on Bibliometrics

Yanting Pan

2024

jes

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“…Only on the basis of the full integration of artificial intelligence and physical education teaching will physical education be fundamentally changed, and then, the entire physical education structure will be changed. erefore, the physical education reform discussed in this study is a process of changing the status and role of various elements of physical education based on the specific teaching environment and the effective support of artificial intelligence, including changing the form of teaching resources, teaching organization, and learning activities, and learning evaluation methods, among which the status and role of each element are important indicators to evaluate the effect of teaching reform [12][13][14][15].…”

Section: E Eory Of Educational Changementioning

confidence: 99%

Research on the Construction of the Quality Evaluation Model System for the Teaching Reform of Physical Education Students in Colleges and Universities under the Background of Artificial Intelligence

Guo

2022

Scientific Programming

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With the continuous progress of the times, the reform of physical education teaching in colleges and universities has to be promoted day by day. The most important task in the process of reform is how to improve the quality of physical education teaching. Only by reforming colleges and universities can we transport outstanding talents into the society. It is very important to improve the teaching quality by improving the physical education quality evaluation system. As artificial intelligence technology has been more and more widely used in different fields, various educational administration systems based on information management have been established in various colleges and universities. On the one hand, it has brought great convenience to the management of physical education in colleges and universities and improvement of the efficiency of sports education management, but on the other hand, there are many shortcomings in the process of practical application. For example, the application of the database does not fully reflect its function and convenience, and it is only used at the level of query and statistics. Therefore, a better evaluation system of physical education teaching quality has become the common expectation of all colleges and universities. This paper makes a powerful analysis of the current quality evaluation of physical education in colleges and universities and proposes a method of establishing a basic framework through expert systems, filling in details with the idea of knowledge base and fuzzy sets, and further using a three-layer B/S framework model to design universal teaching quality assessment system. When discussing the requirements, functional framework, and actual development of the teaching evaluation system, the characteristics of the traditional physical education evaluation model are deeply analyzed, and the system’s interactivity, flexibility, accuracy, and fairness are emphasized in the implementation process. Object-oriented design and analysis are carried out on the requirements of the system, and finally, black-box testing is carried out to ensure the reliability and correctness of the system logic.

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“…Before executing the experiment, the combination of criteria that determine or impact the artist's future song play must also be chosen. Processes such as variability and morphological charts are commonly utilized to attain this goal in the past [21][22][23]. ere is no association between several attributes of the datasets utilized in this paper, such as the amount of time that users of the dataset spent playing.…”

Section: Selection Of Relevant Attributesmentioning

confidence: 99%

“…e main function is to learn the weight parameter size of each factor in the rating prediction formula. On the other hand, steps 8 to 12 are the interest point recommendation algorithm, which function is to calculate the interest point recommended for each user based on the learned weight parameters and various implicit vectors [20,23].…”

Section: Algorithm-based Weight Parameter Learning Processmentioning

confidence: 99%

A Study of Information-Based Teaching Strategies for the Saxophone Based on Deep Learning

Cao

2022

Mobile Information Systems

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Education is one of the core elements in building the career of an individual. It needs proper strategies and techniques to fulfill the modern world’s requirements, such as intelligent learning systems, intelligent management systems, and intelligent computational systems. At present, there is a dearth of systematic debate on how to proceed along the road of machine learning (ML) and education. As a result, this study focuses on the use of artificial intelligence (AI) to promote saxophone informatization teaching strategies, particularly the new strategies brought by deep learning (DL) to saxophone teaching from the perspectives of teaching resources, teaching environment, teaching and learning strategies, teaching management, and teaching evaluation. A matrix decomposition strategy with dynamic weight learning is suggested by keeping the earlier aspects in consideration, which is used to produce a recommendation algorithm that fundamentally incorporates multiple contextual features such as geographic, temporal, and social characteristics, as well as the weight parameter learning process, and essentially constitutes the linear fusion technique’s building approach. All the experiments are carried out on the yelp dataset in order to check if the recommended algorithm is effective or not. The performance of the suggested method is compared to the benchmark algorithms in order to prove that the dynamic weight parameter learning technique is as effective as gradient descent. A comparison of the algorithm that employs one contextual element alone vs the method that uses three contextual factors is also conducted to demonstrate that the linear fusion of several components improves the system’s recommendation performance.

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The determination and analysis of factors affecting to student learning by artificial intelligence in higher education

Cited by 7 publications

References 15 publications

Analysis of Artificial Intelligence in International Higher Education Field Based on Bibliometrics

Analysis of Artificial Intelligence in International Higher Education Field Based on Bibliometrics

Research on the Construction of the Quality Evaluation Model System for the Teaching Reform of Physical Education Students in Colleges and Universities under the Background of Artificial Intelligence

A Study of Information-Based Teaching Strategies for the Saxophone Based on Deep Learning

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