Integration of visual thinking strategies to undergraduate health assessment course: A mixed-method feasibility study

Choi, Jae Shi; Eun, Seung; Choi, Sang June; Kang, Bada; Kim, Soo Hyun; Bae, Juyeon; Tate, Judith A.; Son, Youn‐Jung

doi:10.1016/j.nedt.2022.105374

Cited by 6 publications

(1 citation statement)

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“…Finally, the authors demonstrated the efectiveness of the method through experimental validation. Te study in the literature [23] is an exploration of the validity of online education, and the authors frst stated the need for online education in the context of COVID-19. To investigate the validity of online education, the authors proposed a visual strategy instructional assessment method, which analyzed the feasibility of online education from the perspective of online education participants and designed single-and mixed-group experiments to verify the reliability of course assessment.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Neural Network for the Online Course Quality Assessment System

Bai

Yong-guo

2022

Mathematical Problems in Engineering

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Under the influence of COVID-19, online office and online education has ushered in a golden period of development. The teaching quality of online education has been a controversial issue. Our study takes online course teaching quality assessment as the starting point, explores the influencing factors of online course quality assessment with online courses as the research object, and analyzes the latest research proposal for an online course quality index. To make the online course quality assessment more intelligent, we propose an online course quality assessment method based on a fuzzy neural network. The method uses fuzzy rules as the baseline and adds a TSK perception mechanism to expand the perception domain of the fuzzy neural network and improve the course quality index prediction accuracy. At the input side of the fuzzy neural network, we preclassify the online course data into four parts, and each part of the data represents a different assessment domain. Due to the large data cost, we expanded the collective amount of data using data augmentation methods. In addition, we parse the structure of the fuzzy neural network hierarchy and introduce the construction and role of the TSK perception mechanism in the fuzzy rules. An optimal learning strategy is proposed in the fuzzy neural network training. Finally, in the experimental session, we verify the effectiveness of data augmentation and explore the distribution of course quality assessment weights. In the comparison of the model prediction results with the actual assessment results, our method achieves an excellent matching rate, which proves the high efficiency of our method in the online course quality assessment system.

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Section: Related Workmentioning

confidence: 99%