ArticleComputational intelligence and neuroscience2022
Supervision and Assistance Based on Mobile Information System in Art Video Teaching.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Application of flipped classroom combined with virtual simulation platform in clinical biochemistry practical course.BMC medical education · 2023Article
- Retracted: Supervision and Assistance Based on Mobile Information System in Art Video Teaching.Computational intelligence and neuroscience · 2023Article
- Practical Efficacy of the Mobile Video Feedback Method in Teaching Clinical Abscess Incision and Drainage Skills.Journal of medical education and curricular developmentArticle
Corrections and comments
- Retraction · 2023-07-12Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To enrich students' learning methods, improve their interest in learning, and enable students to fully understand and master the content of art video teaching, a supervision and assistance function based on mobile information system in art video teaching is proposed. Starting with the purpose of improving the auxiliary effect of art video teaching, this study deeply discusses the construction of personalized art video mobile information teaching system based on mobile information technologies such as self-media auxiliary technology and computer-aided technology. Therefore, this study takes android technology mobile information system as a breakthrough to design the video teaching system and introduces the design of mobile teaching information platform in detail. At the same time, according to the relevant requirements of art video teaching, this study designs the teaching system from the aspects of improving students' learning interest, promoting students' curriculum preference information, efficient real-time teacher-student interaction, and so on. According to the requirements of the six sections, the function of the teaching supervision and management module is improved. Through the test of result extraction and data analysis, the feasibility of the user preference extraction and analysis algorithm and the content similarity discrimination algorithm is finally verified. The results show that after more than 10 times of reading, the graphics of video teaching content can basically solve the problem of comparative similarity of the same type of teaching content. At the same time, after more than 30 times of reading, the teaching content can basically solve the problem of small probability error of user system.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.