Personalized, subject-specific professional support combining teacher characteristics with school-level collaboration and leadership support¶

Strategy · All strategies
Evidence · 1 claim (1 for) · 1 study (1 associational), q3 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

Based on the finding that data use depends on both teacher characteristics and school factors, the authors recommend "personalized support, tailored to teacher characteristics and addressing subject -specific differences". Schools could develop subject-specific professional development programs focused on using digital data in teaching, aligned with teachers' individual needs, incorporating case studies or practical examples of learning analytics tools. They also recommend fostering a culture of collaboration — mentoring or data sharing workshops — and strong leadership support including professional development opportunities and clear goals for digital data integration.

Design Implications¶

Context¶

Requirements¶

• School-level enablers such as principal support, goal clarity, and formal and informal collaboration to strengthen teacher will, skill, and tool access

Constraints¶

• The article notes ethical implications such as data privacy and bias must be considered, and that AI integration should be done thoughtfully with adequate teacher training

Target Learners¶

• Upper secondary school teachers across subject areas, including vocational teachers

Target Learning Goals¶

• Teacher data literacy and pedagogical use of digital student data

Affordances¶

• Will Skill Tool Model Digital Data Use

Claims¶

• Digital data use differs by teaching subject: STEM teachers report lower use than languages/arts/humanities and vocational teachers, and vocational teachers report the highest use [+W]

Related Strategies¶

• Design GenAI lesson-planning tools around teacher-specific alignment, integrated multimedia student materials, bite-sized activities, and sustained professional development
• Systemic, human-centred approach treating digital data use as a school-and-teacher system, with AI integrated into professional development
• Eight recommendations for supporting teachers' technology integration, including personalized professional learning and formalized coaching
• Invest in sustained, role-specific, discipline-specific professional learning and structured student AI literacy pathways across PK16
• Share discipline-specific GenAI examples aligned with instructional needs to improve teacher adoption
• Tailor institutional GenAI training to teachers' prior GenAI use experience
• Shift teacher professional development from information-based training to experience-based AI design activities in sustainability contexts

Examples¶

-

Key Sources¶

• Michos, K., Schmitz, M.-L., & Petko, D. (2025). Learning Analytics in Schools: Is Digital Data Use Influenced by Teacher-Level or School-Level Factors? Journal of Learning Analytics, 12(3), 87–101. https://doi.org/10.18608/jla.2025.8567