The relationship between teacher's gender and deep learning strategy: The mediating role of deep learning motivation

Abstract

Deep learning has gradually appeared in the field of teacher education research and is considered as an effective way to promote teacher professional development. To explore the relationship between teacher gender and deep learning strategies, as well as the mediating role of teachers' deep learning motivation, we conducted a questionnaire survey in this study on 429 valid teachers of different genders, ages and backgrounds. The results show that male teachers are more likely to adopt deep learning strategies and deep learning motivations than female teachers. Deep learning motivation is positively correlated with deep learning strategy while negatively correlated with superficial learning strategy. It can be seen that deep learning motivation plays a mediating role between teacher gender and deep learning strategies. These findings expand the understanding of teachers' learning and indicate that in professional development programs, we should pay more attention to the deep learning of female teachers, to help female teachers improve their deep learning motivation and deep learning strategies.

Conditions under which college students cease learning

“…Therefore, it is conceivable that individual differences in learning cessation may relate to performance in other educational outcomes. It is also intriguing to speculate whether attempts to promote deep learning through educational practices in humans may have some bearing on the likelihood to cease learning ( Darling-Hammond et al, 2019 ; Mehta and Fine, 2019 ; Rickles et al, 2019 ; Song et al, 2022 ). Caution is warranted though as the time scale of the present task was microanalytic as cessation was measured on a trial-by-trial basis.…”

Prediction accuracy improvement of pressure pulsation signals of reversible pump‐turbine: A LSTM and VMD‐based optimization approach

“…In the past few years, the field of time series prediction has witnessed significant advancements in deep learning techniques. [26][27][28] One popular type of recurrent neural network (RNN) model that has gained prominence in time series prediction is the long short-term memory (LSTM) network. LSTM networks exhibit exceptional memory and temporal modeling capabilities.…”

“…Traditional prediction methods often fail to fully capture the nonlinear and temporal characteristics in pressure pulsation signals of pump‐turbine units. In the past few years, the field of time series prediction has witnessed significant advancements in deep learning techniques 26–28 . One popular type of recurrent neural network (RNN) model that has gained prominence in time series prediction is the long short‐term memory (LSTM) network.…”