PMID- 36035828
OWN - NLM
STAT- MEDLINE
DCOM- 20220830
LR  - 20231013
IS  - 1687-5273 (Electronic)
IS  - 1687-5265 (Print)
VI  - 2022
DP  - 2022
TI  - Analysis of the Current Situation of Teaching and Learning of Ideological and 
      Political Theory Courses by Deep Learning.
PG  - 5396054
LID - 10.1155/2022/5396054 [doi]
LID - 5396054
AB  - The objectives are to solve the problems existing in the current ideological and 
      political theory courses, such as the difficulty of classroom teaching quality 
      assessment, the confusion of teachers' classroom process management, and the lack 
      of objective assessment basis in teaching quality monitoring. Based on Artificial 
      Intelligence (AI) technology, a designed evaluation method is proposed for 
      teachers' classroom teaching and solves some problems such as high system cost, 
      low evaluation accuracy, and imperfect evaluation methods. Firstly, the boundary 
      algorithm system is introduced in the research, and the Field Programmable Gate 
      Array (FPGA) by deep learning (DL) is used to accelerate the server hardware 
      network platform and equipped with pan tilt zoom (PTZ) and manage multiple 
      AI + embedded visual boundary algorithm devices. Secondly, the network platform 
      can manage the PTZ and focal length of Internet protocol (IP) cameras, measure, 
      and capture face images, transmit data, and recognize students' face, head, and 
      body postures. Finally, classroom teaching is evaluated, and students' behavioral 
      data and functions are designed, debugged, and tested. The research results 
      demonstrate that the method overcomes the problem of high system cost through 
      edge computing and hardware structure, and DL technology is used to overcome the 
      problem of low accuracy of classroom teaching evaluation. Various indicators such 
      as attendance rate, concentration, activity, and richness of teaching links in 
      classroom teaching are obtained. The method involved can make an objective 
      evaluation of classroom teaching and overcome the problem of incomplete classroom 
      teaching evaluation.
CI  - Copyright (c) 2022 Jin Chao and Yijiang Zhang.
FAU - Chao, Jin
AU  - Chao J
AD  - Marxist Branch, Shaoxing University Yuanpei College, Shaoxing 312000, Zhejiang, 
      China.
FAU - Zhang, Yijiang
AU  - Zhang Y
AUID- ORCID: 0000-0003-4205-2127
AD  - Information and Mechanical and Electrical Engineering Branch, Shaoxing University 
      Yuanpei College, Shaoxing 312000, Zhejiang, China.
LA  - eng
PT  - Journal Article
PT  - Retracted Publication
DEP - 20220818
PL  - United States
TA  - Comput Intell Neurosci
JT  - Computational intelligence and neuroscience
JID - 101279357
SB  - IM
RIN - Comput Intell Neurosci. 2023 Oct 4;2023:9806352. PMID: 37829855
MH  - *Artificial Intelligence
MH  - *Deep Learning
MH  - Humans
MH  - Students
MH  - Teaching
PMC - PMC9410937
COIS- The authors declare that they have no conflicts of interest.
EDAT- 2022/08/30 06:00
MHDA- 2022/08/31 06:00
PMCR- 2022/08/18
CRDT- 2022/08/29 05:31
PHST- 2022/05/10 00:00 [received]
PHST- 2022/06/15 00:00 [revised]
PHST- 2022/06/22 00:00 [accepted]
PHST- 2022/08/29 05:31 [entrez]
PHST- 2022/08/30 06:00 [pubmed]
PHST- 2022/08/31 06:00 [medline]
PHST- 2022/08/18 00:00 [pmc-release]
AID - 10.1155/2022/5396054 [doi]
PST - epublish
SO  - Comput Intell Neurosci. 2022 Aug 18;2022:5396054. doi: 10.1155/2022/5396054. 
      eCollection 2022.