The reversible pump‐turbine plays an important role in hydropower stations, but pressure pulsation during their operation affects their performance and lifespan. Accurate prediction of pressure pulsation signals can provide an important basis for energy planning and stable operation of pumped storage units, thereby promoting sustainable development of the environment. This study introduces an optimization method that combines long short‐term memory (LSTM) and variable mode decomposition (VMD) to enhance the prediction accuracy of pressure pulsation signals. First, by decomposing the pressure pulsation signal into multiple relatively stable subsequence components using VMD, the characteristics of the original signal become more distinct. Subsequently, individual LSTM‐based time series prediction models were constructed for each modal function, and the hyperparameters related to subsequence were optimized using the sparrow search algorithm. To validate the efficacy of the proposed approach, this paper conducted experiments using pressure pulsation signals of a pump‐turbine obtained through numerical simulation. The experimental data was divided into training and testing sets, with the former used to train the LSTM model and the latter used for validation. The experimental results show that the optimized VMD with an optimized LSTM method can effectively improve the prediction accuracy of pressure pulsation signals in reversible pump‐turbine.

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

Abstract Students value a close, supportive relationship with their professors, which has been shown to enhance their learning in higher education. However, more needs to be known about how quality faculty-student relationships shape students’ engagement and approaches to learning in higher education. In a diverse sample of 966 undergraduates from two different institutions of higher education, the current study explored the relationship between faculty-student rapport, student engagement, and deep and surface approaches to learning. Faculty-student rapport was positively correlated with student engagement ( r = .50) and deep learning ( r = .30), and negatively correlated with surface learning ( r = -.21). Student engagement was positively correlated with deep learning ( r = .70) and negatively with surface learning ( r = -.32). Using multilevel modelling with students nested within classrooms, engagement was shown to mediate the effects of rapport on greater levels of deep learning (β = .31) and lower levels of surface learning (β = -.12). Although results held up across a range of demographic characteristics, some differences were noted for rapport-building among Asian American students and engagement across men versus women. These results have important implications for how faculty can engage students in the learning process by developing close, supportive relationships with their students and by extending their relationship with their students outside the classroom. 

Faculty-student rapport, student engagement, and approaches to collegiate learning: exploring a mediational model

Abstract Background The introduction of Small Private Online Courses (SPOCs) in English as a Foreign Language (EFL) instruction at Higher Vocational Colleges (HVCs) signifies a shift in education. Understanding the factors that affect deep learning in this SPOC context is crucial for improving educational outcomes. Objectives By employing grounded theory, we seek to explore the key factors that shape deep learning experiences for students in SPOC learning environments at HVCs and clarify the interrelationships among these influencing factors. Methods Semi‐structured interviews were conducted with 18 EFL students and 4 teachers and NVivo 11 software was utilised to aid in the qualitative analysis of the collected data. Through a rigorous three‐tier coding analysis, an “environment‐person‐mediation ‐behaviour” (EPMB) model was constructed, aiming to clarify the mechanisms that influence deep learning among EFL students in HVCs. Results Our findings reveal that intrinsic motivation and cognitive abilities are crucial for deep learning among EFL students in HVCs. Blended learning settings, English curriculum satisfaction, and English teachers' teaching methods serve as situational influencing factors. These factors are interconnected, mediating positive or negative effects on deep learning through various intermediaries like continuity, attraction, guidance, motivation, and regulation. Implications The findings offer pedagogical insights for HVC stakeholders, enabling them to enhance students' deep learning experiences. 

Factors affecting deep learning of <scp>EFL</scp> students in higher vocational colleges under small private online courses‐based settings: A grounded theory approach

English learning plays a critical role in the academic development and career preparation of undergraduate non-English majors in China. However, English achievement is often shaped by a range of psychological factors. While prior studies have separately examined the effects of learning motivation, self-efficacy, and learning strategies on academic performance, few have systematically explored how these variables interact to influence English achievement. This study investigated the relationships among learning motivation, self-efficacy, and learning strategies, as well as their mediating effects on English achievement among undergraduate non-English majors in Chinese universities. We collected 432 valid questionnaires and conducted structural equation modeling (SEM) to analyze the data. Results showed that learning motivation, self-efficacy, and learning strategies all positively predicted English achievement. Both self-efficacy and learning strategies served as mediators in the relationship between learning motivation and English achievement. Moreover, a chain mediation pathway from learning motivation to self-efficacy to learning strategies further enhanced the indirect effect of motivation on achievement. These findings suggest that, for undergraduate non-English majors, fostering learning motivation, strengthening self-efficacy, and adopting effective learning strategies may collectively support better outcomes in English learning. 

The Relationship Between Learning Motivation and English Achievement Among Undergraduate Non-English Majors in China: The Mediating Role of Self-Efficacy and Learning Strategies

Deep learning in teacher professional development using Goethean phenomenology: a self-study

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. 

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

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