Dong Jianwei, Chen Xinya, Chen Chen, Chen Cheng
College of Education Science, Xinjiang Normal University, Urumqi, China.
College of Education Science, Xinjiang Teacher's College, Urumqi, China.
PLoS One. 2025 Sep 2;20(9):e0331560. doi: 10.1371/journal.pone.0331560. eCollection 2025.
Teacher leadership is widely regarded as a critical driver of school reform and educational quality improvement. Although the field has been extensively studied, empirical research remains limited in Xinjiang, China-a region characterized by its multiethnic and multilingual context. To address this gap, the present study developed and validated a culturally sensitive assessment tool based on a sample of 371 primary and secondary school teachers from Xinjiang. A structured questionnaire was designed encompassing four dimensions: professional guidance, educational collaboration, cross-cultural ICT-based teaching competence, and leadership cognition. In addition, we introduced an interpretable deep learning model-ITL-LSTM (Interpretable Teacher Leadership LSTM)-which employs a Diagonal BiLSTM structure for dynamic classification of teacher leadership profiles, achieving a prediction accuracy of 90.10%. The findings indicate that the proposed tool demonstrates strong applicability and scalability within the Xinjiang context, providing effective support for dynamic evaluation, personalized development, and evidence-based decision-making in multicultural educational settings.
教师领导力被广泛认为是学校改革和教育质量提升的关键驱动力。尽管该领域已得到广泛研究，但在中国新疆这个具有多民族和多语言背景的地区，实证研究仍然有限。为了填补这一空白，本研究基于来自新疆的371名中小学教师样本，开发并验证了一种具有文化敏感性的评估工具。设计了一份结构化问卷，涵盖四个维度：专业指导、教育协作、基于跨文化信息通信技术的教学能力以及领导力认知。此外，我们引入了一种可解释的深度学习模型——ITL-LSTM（可解释教师领导力长短期记忆网络），该模型采用对角双向长短期记忆网络结构对教师领导力概况进行动态分类，预测准确率达到90.10%。研究结果表明，所提出的工具在新疆背景下具有很强的适用性和可扩展性，为多元文化教育环境中的动态评估、个性化发展和循证决策提供了有效支持。
