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1. Toward Automated Feedback on Teacher Discourse to Enhance Teacher Learning

   https://doi.org/10.1145/3313831.3376418  

   Jensen, Emily; Dale, Meghan; Donnelly, Patrick J.; Stone, Cathlyn; Kelly, Sean; Godley, Amanda; D'Mello, Sidney K. (April 2020, Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (CHI 2020))
2. Leveraging Mathematics Teacher Leaders in Support of Student and Teacher Learning

   https://doi.org/10.1080/19477503.2022.2140989  

   Rigelman, Nicole; Lewis, Chandra (January 2023, Investigations in Mathematics Learning)
3. How do mathematics teachers describe teacher leadership and teacher leaders’ responsibilities?

   Aqazade, M; Ekmekci, A (April 2024, The 2024 Annual Meeting of American Educational Research Association.)
4. Iterative teacher-aware learning.

   Yuan, L.; Zhou, D.; Shen, J.; Gao, J.; Chen, J. L.; Gu, Q.; Wu, Y. N.; Zhu, S.-C. (January 2021, Neural Information Processing Systems (NeurIPS 2021).)

   In human pedagogy, teachers and students can interact adaptively to maximize communication efficiency. The teacher adjusts her teaching method for different students, and the student, after getting familiar with the teacher’s instruction mechanism, can infer the teacher’s intention to learn faster. Recently, the benefits of integrating this cooperative pedagogy into machine concept learning in discrete spaces have been proved by multiple works. However, how cooperative pedagogy can facilitate machine parameter learning hasn’t been thoroughly studied. In this paper, we propose a gradient optimization based teacher-aware learner who can incorporate teacher’s cooperative intention into the likelihood function and learn provably faster compared with the naive learning algorithms used in previous machine teaching works. We give theoretical proof that the iterative teacher-aware learning (ITAL) process leads to local and global improvements. We then validate our algorithms with extensive experiments on various tasks including regression, classification, and inverse reinforcement learning using synthetic and real data. We also show the advantage of modeling teacher-awareness when agents are learning from human teachers. 

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5. The Principal-Teacher Churn: Understanding the Relationship Between Leadership Turnover and Teacher Attrition

   https://doi.org/10.1177/0013161X211051974  

   DeMatthews, David E.; Knight, David S.; Shin, Jinseok (January 2021, Educational Administration Quarterly)

   Purpose: Principals are critical to school improvement and play a vital role in creating inclusive and high-performing schools. Yet, approximately one in five principals leave their school each year, and turnover is higher in schools that serve low-income students of color. Relatedly, high rates of teacher turnover exacerbate challenges associated with unstable learning environments. Our study examines the extent to which principal turnover influences teacher turnover. We build on past work by exploring how the relationship between teacher and principal turnover differs in urban, high-poverty settings and by examining the effects of chronic principal turnover. Research Methods/Approach: We draw on a student- and employee-level statewide longitudinal dataset from Texas that includes all public K-12 schools from school years 1999–2000 to 2016–17. We estimate teacher-level models with school fixed effects, allowing us to compare teacher turnover in schools leading up to and immediately following a principal exit, to otherwise similar schools that do not experience principal turnover. Findings: Teacher turnover spikes in schools experiencing leadership turnover, and these effects are greater among high-poverty and urban schools, in schools with low average teacher experience, and in schools experiencing chronic principal turnover. Implications: Improving leadership stability, especially in urban schools experiencing chronic principal turnover may be an effective approach to reducing teacher turnover. Principal and teacher turnover and their relationship with each other requires further investigation. The field would benefit from qualitative research that can provide important insights into the individual decisions and organizational processes that contribute to principal turnover. 

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