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School improvement plans are strategic documents most schools complete on an annual basis. Research on school improvement planning highlights that high-quality plans contribute to student achievement gains, but many plans are of poor quality. Principals serve in a critical role within the school improvement process. In this article, we review research on decision-making and biases to provide a set of recommendations to improve the school improvement planning process. Specifically, we highlight how a set of biases can contribute to flawed planning that reduces the likelihood that the school improvement plan will be comprehensive, strategic, and useful. We also include a set of definitions and "planning guardrails" principals can use when developing a school improvement team and facilitating the school improvement planning process.

Purpose: The purpose of this study is to describe the underlying topics and the topic evolution in the 50-year history of educational leadership research literature. Method: We used automated text data mining with probabilistic latent topic models to examine the full text of the entire publication history of all 1,539 articles published in "Educational Administration Quarterly" (EAQ) from 1965 to 2014. Given the computationally intensive data analysis required by probabilistic topic models, relying on high-performance computing, we used a 10-fold cross-validation to estimate the model in which we categorized each article in each year into one of 19 latent topics and illustrated the rise and fall of topics over the "EAQ's" 50-year history. Findings: Our model identified a total of 19 topics from the 1965 to 2014 "EAQ" corpus. Among them, five topics--inequity and social justice, female leadership, school leadership preparation and development, trust, and teaching and instructional leadership--gained research attention over the 50-year time period, whereas the research interest appears to have declined for the topic of epistemology of educational leadership since the 2000s. Other topics waxed and waned over the past five decades. Implications: This study maps the temporal terrain of topics in the educational leadership field over the past 50 years and sheds new light on the development and current status of the central topics in educational leadership research literature. More important, the panoramic view of topical landscape provides a unique backdrop as scholars contemplate the future of educational leadership research.

This study investigated the relationship between the leader's gender, leadership position. Twitter use, and influence on information diffusion in the communication network on Twitter. We collected the 30,200 latest tweets of 151 active Twitter users who held educational leadership positions. Results of social network analysis and multiple regression analyses suggest a gender inequality in the leader's influence on information diffusion in the network. Findings also indicate no significant relationship between leadership position (district vs. building) and a leader's influence in the network. Moreover, Twitter following was positively associated with the leader's influence in the network, whereas the number of followers, weekly tweets, and the time of Twitter account creation did not predict the leader's influence. Practical implications on how leaders use Twitter to disseminate information are discussed.

In this study, we examined the role of leadership styles and multi‐dimensional learner engagement in how students emerge as learning leaders in asynchronous online discussions. Grounded in the conceptual framework of two dominant leadership styles of transformational and transactional leadership, this study applies the two leadership styles—transformational leadership and transactional leadership—to the Leader Identification Method (LIM) which defines three types of leader roles (i.e., full, transactional and attractive facilitator) in online learning. We collected data from 20 students enrolled in a graduate‐level online course that required participation in 12‐week discussion activities. Results of the longitudinal data analyses show that person‐focused, transformational leadership and active participation in online discussions were significant factors that enabled students to emerge as learning leaders. Students are more likely to become leaders by exhibiting transformational leadership behaviour and productively interacting with one another in an online discussion community. Lay Description: What is already known about this topic: Students learn through social interactions in an asynchronous discussion forum.Building a learning community involve students who play different leadership roles.Various learner traits could interact with leadership emergence.Student leadership influences individual and group learning in an online community. What this paper adds: This study operationalizes learning leadership in the context of online discussions.This study proposes the Leader Identification Method (LIM) to detect learning leaders.This study suggests measurable models of leadership styles and learner engagement.The study analyzes a comprehensive model of leader emergence longitudinally. Implications for practice: LIM can be a means for practitioners to differentiate learning leaders from non‐leaders.Trackable data can inform practitioners of students' leadership style and engagement.Leadership development needs to focus on positive emotion and transformational leadership.The conceptual framework guides future studies for learner leadership in online learning. [ABSTRACT FROM AUTHOR]

• Artificial intelligence can improve the efficiency and accuracy of leaders' DIDM. • Lurking biases in artificial intelligence may be amplified in leaders' DIDM. • The moral values we uphold may clash with using AI to make data-informed decisions. • Leaders are recommended to ensure the transparency of AI in their decision making. • Leaders should treat AI as a decision support, rather than a replacement. Artificial intelligence (AI) refers to a type of algorithms or computerized systems that resemble human mental processes of decision making. Drawing upon multidisciplinary literature that intersects AI, decision making, educational leadership, and policymaking, this position paper aims to examine promising applications and potential perils of AI in educational leaders' data-informed decision making (DIDM). Endowed with ever-growing computational power and real-time data, highly scalable AI can increase efficiency and accuracy in leaders' DIDM. However, misusing AI can have perilous effects on education stakeholders. Many lurking biases in current AI could be amplified. Of more concern, the moral values (e.g., fairness, equity, honesty, and doing no harm) we uphold might clash with using AI to make data-informed decisions. Further, missteps on the issues about data security and privacy could have a life-long impact on stakeholders. The article concludes with recommendations for educational leaders to leverage AI potential and minimize its negative consequences. [ABSTRACT FROM AUTHOR]

An asynchronous online discussion forum has been a prevalent means of collaborative online learning. Yet, it remains challenging for instructors to identify who and how students emerge as learning leaders. To overcome this challenge, this study aims to: (a) conceptualizing learning leadership in the context of online asynchronous discussions, (b) proposing a Leader Identification Method (LIM), and (c) testing the proposed method by using empirical data on how learning leaders behave differently from their peers in terms of behavioral, cognitive, and emotional engagement. Assuming that learning leaders emerge from social interactions in online discussions and drawing on the literature on leadership and social network analysis, we conceptualized three types of learning leadership roles: full facilitator, transactional facilitator, and attractive facilitator. These leadership roles are assumed to be associated with learners' levels of engagement in behavior, cognition, and emotions. To that end, we developed the LIM that factors in learners' behavioral, cognitive, and emotional engagement. Using a case study method to test the LIM, we collected two cohorts' online interaction data, as well as the data on the cohorts' behavioral, cognitive, and emotional engagement. The results established the reliability and validity of the LIM. Specifically, compared to non-leaders, the learning leaders exerted more transformational leadership, higher cognitive engagement, and more frequent emotional expression. The LIM pave the foundation for identifying learning leadership and providing pedagogical support to foster learning leadership development in online collaboration. • This study conceptualized learning leadership in the context of an online discussion. • Drawing upon the conceptual framework, we proposed the Leader Identification Method (LIM). • This case study leveraged mixed data to evidence the utility value of the LIM. • This study showed the potential of LIM to identify three types of online learning leaders. • The identified leaders demonstrated more transformational leadership and higher engagement. [ABSTRACT FROM AUTHOR]