Research on flipped classroom teaching method of edible mushroom cultivation experiment based on Internet of Things technology

He, Shuling; Kong, Dejiang; Yang, Jingjun; Ma, Lingfa; Chang, Youngho

doi:10.1109/icicta51737.2020.00114

Cited by 1 publication

(3 citation statements)

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“…In terms of experimental teaching, Lei et al [16] integrated the flipped classroom into the teaching of IoT development and applied it to the whole semester. Moreover, to guarantee the transmission security of teaching resources in the process of experimental teaching, He et al [17] proposed a flipped classroom teaching method based on IoT technology for edible mushroom cultivation experiments and designed a secure convergence algorithm to improve the transmission efficiency of teaching resources.…”

Section: A Methods Of Teaching Courses In Iotmentioning

confidence: 99%

“…Pre means the proportion of correctly predicted positive classes to the proportion of all predicted positive classes, as shown in Eq (16). As shown in Eq (17), Rec is the proportion of actual positive samples that are predicted to be positive to the proportion of actual positive samples that are predicted to be positive. The F1 combines Pre and Re, as shown in Eq (18), where a higher F1 score indicates that the proposed algorithm is more reliable.…”

Section: Evaluation Indicatorsmentioning

confidence: 99%

“…where n is the hidden size and m is the input size. The time complexity of the model is the number of operations on the number of model parameters, as shown by Eq (17):…”

Section: Performance Evaluation Of Face Recognition In Tfrecmentioning

confidence: 99%

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A model study of teaching method reform of computer laboratory course integrating internet of things technology

Zhou,

Qian,

Aziz

et al. 2024

PLoS ONE

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The Internet of Things (IoT) is gradually changing the way teaching and learning take place in on-campus programs. In particular, face capture services improve student concentration to create an efficient classroom atmosphere by using face recognition algorithms that support end devices. However, reducing response latency and executing face analysis services effectively in real-time is still challenging. For this reason, this paper proposed a pedagogical model of face recognition for IoT devices based on edge computing (TFREC). Specifically, this research first proposed an IoT service-based face capture algorithm to optimize the accuracy of face recognition. In addition, the service deployment method based on edge computing is proposed in this paper to obtain the best deployment strategy and reduce the latency of the algorithm. Finally, the comparative experimental results demonstrate that TFREC has 98.3% accuracy in face recognition and 72 milliseconds in terms of service response time. This research is significant for advancing the optimization of teaching methods in school-based courses, meanwhile, providing beneficial insights for the application of face recognition and edge computing in the field of education.

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Section: A Methods Of Teaching Courses In Iotmentioning

confidence: 99%

Section: Evaluation Indicatorsmentioning

confidence: 99%