Artificial intelligence and deep learning in educational technology research and practice

Cheng, Xusen; Sun, Jianshan; Zarifis, Alex

doi:10.1111/bjet.13018

Cited by 25 publications

(7 citation statements)

References 17 publications

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“…Chen et al conducted in-depth research on MIDI when studying music classification algorithms based on deep learning models and extracted 100 features from musical information such as musical instruments, composition, rhythm, pitch, melody, and chords, using machine learning classifiers such as the nearest neighbor algorithm and artificial NNS for music classification [ 9 ]. Cheng et al developed and studied the first humming recognition system based on deep learning, which uses typical string matching-based recognition technology and uses the letters U, D, or S to characterize the pitch change of the audio signal to represent the humming audio signal using a string composed of these three characters, and then use the string matching algorithm to calculate the matching probability of songs in the database [ 10 ]. Zhang et al applied the restricted Boltzmann machine to the classification of music genres and constructed a 5-layer restricted Boltzmann machine, but this method has an obvious defect that it can only be used in four.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Construction of AI Environmental Music Education Application Model Based on Deep Learning

Cheng

Xiao

2022

Journal of Environmental and Public Health

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The art of music, which is a necessary component of daily life and an ideology older than language, reflects the emotions of human reality. Many new elements have been introduced into music as a result of the quick development of technology, gradually altering how people create, perform, and enjoy music. It is incredible to see how actively AI has been used in music applications and music education over the past few years and how significantly it has advanced. AI technology can efficiently pull in the course, stratify complex large-scale music or sections, simplify teaching, improve student understanding of music, solve challenging student problems in class, and simplify the tasks of teachers. The traditional music education model has been modified, and the music education model’s audacious innovation has been made possible by reducing the distance between the teacher and the student. A classification algorithm based on spectrogram and NNS is proposed in light of the advantages in image processing. The abstract features on the spectrogram are automatically extracted using the NNS, which completes the end-to-end learning and avoids the tediousness and inaccuracy of manual feature extraction. This study, which uses experimental analysis to support its findings, demonstrates that different music teaching genres can be accurately classified at a rate of over 90%, which has a positive impact on recognition.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Construction of AI Environmental Music Education Application Model Based on Deep Learning

Cheng

Xiao

2022

Journal of Environmental and Public Health

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“…While the technology used to deliver online modules such as Virtual Learning Platforms like Moodle and Blackboard are not new, the role of technology is evolving with Simulations (e.g. (Brown et al, 2020), Artificial Intelligence (AI) and deep learning used increasingly (Cheng, Sun, & Zarifis, 2020). AI is also helping make customised recommendations for the students on…”

Section: Student Experience Online and Peer To Peer Engagementmentioning

confidence: 99%

Modeling students’ voice for enhanced quality in online management education

Efthymiou

Zarifis

2021

The International Journal of Management Education

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“…The arrival of the era of artificial intelligence drives the research on ideological and political education of college students from macro to micro, from broad to deepening, and from rough to precise [1][2]. At present, the research on the precise ideological and political education of college students in the era of artificial intelligence mainly focuses on the definition of connotation, value implication, and practical rationale [3][4].…”

Section: Introductionmentioning

confidence: 99%

Precision Teaching of Civics Integration in Universities and Primary Schools with Individual Differences in the Age of Artificial Intelligence

Jia

2024

Applied Mathematics and Nonlinear Sciences

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Leveraging the unique capabilities of intelligent technologies, this study presents an advanced approach to Civics and Politics education, emphasizing personalized and multimodal teaching strategies suitable for various educational stages. We establish an Integrated Civics and Political Teaching Platform using multimodal feature fusion algorithms coupled with personalized recommendation techniques. This platform incorporates genetic algorithm-based analysis of students’ learning behaviors, facilitating a Precision Teaching Service Model that tailors education to individual needs. A comparative study of semester scores highlights the model’s effectiveness: notably, a substantial 19.89 point increase in declarative knowledge scores and a significant reduction in the variability of strategic knowledge scores by 5.28 points. These results affirm the benefits of personalized teaching in enhancing student outcomes in Civics education.

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Artificial intelligence and deep learning in educational technology research and practice

Cited by 25 publications

References 17 publications

[Retracted] Construction of AI Environmental Music Education Application Model Based on Deep Learning

[Retracted] Construction of AI Environmental Music Education Application Model Based on Deep Learning

Modeling students’ voice for enhanced quality in online management education

Precision Teaching of Civics Integration in Universities and Primary Schools with Individual Differences in the Age of Artificial Intelligence

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