Work

Development and validation of a deep learning–based assessment tool for teacher leadership: A case study from Xinjiang, China

Year: 2025

Type: article

Abstract: 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.

Source: PLoS ONE

DOI: 10.1371/journal.pone.0331560

Authors: Jianwei Dong, Xinya Chen, Chen Chen, Chen Chen, Cheng Chen, Cheng Chen

Institutions: Xinjiang Normal University, Xinjiang University, People's Hospital of Xinjiang Uygur Autonomous Region

Language: English

Topic: Teacher Education and Leadership Studies

Subfield: Education

Field: Social Sciences

Domain: Social Sciences

SDGs: Quality education, Reduced inequalities

Open Access status: gold

Estimated APC paid: $2,382