European Journal of Education最新文献

Massive Open Online Courses (MOOCs) effectively support online learning behaviour; while constructing a sustainable learning process, MOOCs have also formed the social network. In addition, learners' burnout state has become a serious obstacle to the development and promotion of MOOCs. This study analyzes the potential social behaviour associated with the burnout state of the learning behaviour and designs a fruit fly trajectory model to comprehensively analyse the learning behaviour and social behaviour. Enabled by the sector search mechanism, one improved generalised regression neural network accurately predicts the burnout state. Then, the changes in social behaviour and learning behaviour during the occurrence of burnout state are demonstrated, and strategies for warning learning behaviour, intervening in the learning state and optimising social behaviour are further derived. The entire study might improve the online learning process of integrating learning behaviour with social behaviour, providing effective technical means and reliable decision for learners, and reduce the negative impact of burnout state.

As generative Artificial Intelligence (AI) is seen as a catalyst for a new learning and examination culture in higher education, it urges universities to reinvent themselves and to adapt to these changes effectively. By analysing the content of 67 university guidelines on generative AI, this study investigates how universities in Germany position themselves towards the rise of generative AI. Findings reveal that a majority of university guidelines explicitly permit both university lecturers and students to engage with generative AI, emphasising the importance of building AI literacy among both groups and preparing students for changing demands in the world of work. Importantly, 56.7% of university guidelines posit that the opportunities of generative AI for higher education outweigh the risks, underscoring the potential transformative impact on teaching and research. In addition, the results of a workshop with 25 faculty members were scrutinised to deepen and specify the findings of the content analysis.

The main purpose of this article is to investigate the prospects of using AI in the education of social workers and social work in general. The research methodology encompassed both pre-test and post-test assessments administered for closed examinations across five academic disciplines, all of which were instructed through the utilisation of the e-Kalaiva AI platform. The findings of the current investigation revealed that a 6-month implementation of the e-Kalaiva AI program, rooted in artificial intelligence (AI), enriched with machine learning (ML), augmented reality (AR), and virtual reality (VR) components, significantly enhanced the educational process within the domains of biology, computer science, sociology, social work, and psychology. It was ascertained that AI has the potential to aid educational institutions in achieving sustainable development objectives. AI could be a promising tool in social work in the UAE and globally.

This study aimed to examine teachers' classroom practices in the context of computational thinking skills and to determine their views on computational thinking. In the research process, the Q method was used in which quantitative and qualitative methods were used together. The participants of the research consist of 48 primary school teachers. The socioeconomic level of the environment of the schools where they work, professional seniority and gender variables of the teachers in the participants were collected. It was determined that teachers approached computational thinking skills positively, but they did not fully use the subdimensions of computational thinking skills in their classroom practices. It was determined that abstraction, which is seen as the most important dimension of computational thinking skills in the literature, was not consciously used by teachers in their classroom practices.

This research aimed to determine the methods for teaching piano by focusing on the synthesis of the concept of universalism and the author's philosophy of pedagogy, which affect the piano performance. Using an uneven scale, the author revealed that at baseline, the traditional approach to teaching piano was common (58%). However, this educational approach does not reflect the principles of universalism, which prevents the attainment of high results among the majority of students and does not demonstrate the potential for utilising an individualised approach to learning. The obtained results influenced the development of a teaching methodology that included a preliminary analysis of the repertoire, the enhancement of musicality during the interpretation of sounds and melodies, the acquisition of skills in sound contrast and polyphony, and the development of performance dynamics and creativity. Using the sociometric rating index, the researchers determined that the author's approach ($��I_{\text{s}}�$ = 1.37) was of the highest importance for improving piano skills because it was aimed at developing playing skills of varying complexity. On the basis of the respondents' piano composition performance quality, it was evident that the execution technique for pre-existing musical compositions ($��T_{\mathrm{ef}}�$ = 9.7) exceeded that of improvised pieces ($��T_{\mathrm{ef}}�$ = 9.3). The results obtained contribute to the development of technical and creative skills in piano performance, employing principles of universalism that influenced the creation of the author's methodology. The research prospects are related to a comparative analysis of the effectiveness of teaching piano under different author's methods.

The rise of artificial intelligence (AI) has significantly impacted education, yet few scholars have explored AI-assisted classrooms, particularly in language education in China. Understanding the roles of classroom climate, AI literacy, and resilience is essential, as these factors foster positive learning environments and enhance student engagement. In this sense, this study, grounded in Social Cognitive Theory, employs structural equation modelling to investigate factors influencing classroom engagement in AI-assisted Chinese English as a Foreign Language (EFL) classrooms. It examines data from 606 university EFL learners to explore the interactions among these variables and the mediating role of resilience. The findings indicate that classroom climate, AI literacy, and resilience all significantly predict classroom engagement, highlighting the importance of both environmental and cognitive factors in fostering active student participation. Furthermore, resilience serves as a crucial mediator, linking classroom climate and AI literacy to engagement. This study provides some insights for educators and policymakers, emphasising the need to cultivate supportive classroom environments, promote AI literacy programs, and strengthen students' resilience to optimise engagement in AI-assisted educational settings.

As an alternative to conventional instruction and evaluation methods, dynamic assessment aims to promote language learning by utilising an interactive approach. As a subset of dynamic assessment, the interventionist approach to dynamic assessment focuses on mediation from implicit to explicit. In spite of its central role in language learning and assessment, the interventionist dynamic assessment has received less attention in empirical research. To fill part of the gap, this study examined the contributions of interventionist dynamic assessment to the acquisition of modal verbs treating working memory and field (in)dependence as two moderating variables. Two intact classes of experimental and control groups, each with 30 subjects, took part in this quantitative quasi-experimental research. The experimental group received an interventionist dynamic assessment based on the target linguistic feature, while the control group received a traditional presentation-practice-production mode of instruction. The findings of the study revealed that while at the onset, both groups performed similarly, the experimental group outperformed the control group on the posttest, as determined by the between-subjects ANOVA. The difference between pretest and delayed posttest scores was also significant, corroborating the effectiveness of the treatment on the acquisition of the linguistic form. The study findings highlighted how interventionist dynamic assessment along with a high working memory and a field-independence trait influence the acquisition of linguistic features. The study has multiple implications for EFL learners and teachers.

This study investigated the effectiveness of the flipped learning model, compared with the nonflipped learning model, in improving second/foreign language metacognitive listening awareness and perceived strategy use. For this purpose, 40 young adult learners studying English as a foreign language in two intact classes were selected. They were assigned into flipped and nonflipped groups, which received flipped and nonflipped instruction, respectively. A mixed-methods embedded design was adopted. Data were gathered by employing the Metacognitive Awareness Listening Questionnaire and diaries. Analysis of covariance and thematic analysis revealed that the flipped model significantly fostered the learners' metacognitive awareness and strategy use in listening. Although the strategies related to planning and evaluation, person knowledge, directed attention, mental translation and problem solving were used in both groups, the flipped group demonstrated a greater tendency to use the metacognitive strategies requiring high-order processes. Flipped learning can drive L2 students to become metacognitively strategic listeners.