PMID- 36193185
OWN - NLM
STAT- MEDLINE
DCOM- 20221005
LR  - 20221005
IS  - 1687-5273 (Electronic)
IS  - 1687-5265 (Print)
VI  - 2022
DP  - 2022
TI  - Teaching Quality Evaluation of Animal Science Specialty Based on IPSO-BP Neural 
      Network Model.
PG  - 3138885
LID - 10.1155/2022/3138885 [doi]
LID - 3138885
AB  - Teaching quality evaluation is one of the most commonly used educational 
      evaluation methods, which is used to evaluate teachers' teaching ability and 
      teaching effect. In order to improve the effectiveness and accuracy of teaching 
      quality evaluation, a BP neural network model based on improved particle swarm 
      optimization (IPSO) is proposed. Firstly, the evaluation index system of teaching 
      quality is constructed with teaching attitude, teaching content, teaching method, 
      and teaching effect as indicators. Then, IPSO algorithm is used to optimize the 
      weight and threshold of neural network to improve the performance of BP 
      algorithm. Secondly, IPSO-BP algorithm is used for sample training to optimize 
      the model structure. Finally, the model is used to evaluate the teaching quality 
      of animal science-related courses in Inner Mongolia University for Nationalities. 
      The results show that compared with the ordinary BP neural network model, the 
      IPSO-BP model has fast convergence speed, good robustness, and strong global 
      search ability, and the evaluation accuracy rate is 96.7%. It is feasible in the 
      evaluation of teaching quality.
CI  - Copyright (c) 2022 Liyan Chen et al.
FAU - Chen, Liyan
AU  - Chen L
AD  - College of Animal Science and Technology, Inner Mongolia Minzu University, 
      Tongliao 028000, China.
FAU - Wang, Lihua
AU  - Wang L
AUID- ORCID: 0000-0001-6494-2214
AD  - College of Animal Science and Technology, Inner Mongolia Minzu University, 
      Tongliao 028000, China.
FAU - Zhang, Chunyou
AU  - Zhang C
AD  - College of Animal Science and Technology, Inner Mongolia Minzu University, 
      Tongliao 028000, China.
LA  - eng
PT  - Journal Article
DEP - 20220923
PL  - United States
TA  - Comput Intell Neurosci
JT  - Computational intelligence and neuroscience
JID - 101279357
SB  - IM
MH  - *Algorithms
MH  - Animals
MH  - China
MH  - *Neural Networks, Computer
PMC - PMC9525746
COIS- The authors declare that they have no conflicts of interest.
EDAT- 2022/10/05 06:00
MHDA- 2022/10/06 06:00
PMCR- 2022/09/23
CRDT- 2022/10/04 01:56
PHST- 2022/05/29 00:00 [received]
PHST- 2022/06/24 00:00 [revised]
PHST- 2022/07/01 00:00 [accepted]
PHST- 2022/10/04 01:56 [entrez]
PHST- 2022/10/05 06:00 [pubmed]
PHST- 2022/10/06 06:00 [medline]
PHST- 2022/09/23 00:00 [pmc-release]
AID - 10.1155/2022/3138885 [doi]
PST - epublish
SO  - Comput Intell Neurosci. 2022 Sep 23;2022:3138885. doi: 10.1155/2022/3138885. 
      eCollection 2022.