A school is a stable organization that usually knows how to manage in its uncertain environment. For example, during the Covid-19 pandemic, despite the difficulties, many schools increased their budgets, recruited temporary staff, promoted teachers’ digital literacy, and introduced online platforms (Reimers & Schleicher, 2020). One of the factors enabling this stability is the middle leaders (MLs). MLs are teachers who also perform part-time administrative roles, such as subject leader and head of year. Their primary tasks are coordinating the curriculum, scheduling exams, developing teachers’ professional skills, and monitoring students’ achievements and behavior (De-Nobile, 2021). Positioned as mediators between senior management and teaching staff, MLs facilitate information flow and promote organizational learning, which can reduce uncertainty and foster stability and order in the school.

Organizational learning is the process whereby individual personal knowledge becomes organizational knowledge (Argyris & Schön, 1996). According to this theory, such a process contains two kinds of learning. Single-loop learning is learning that uses existing knowledge, norms, policies, and strategies to correct errors. Double-loop learning is learning that not only seeks to solve a problem but also to question and change the basic norms and assumptions that create it. Both forms of learning are essential for organizational functioning. However, in times of uncertainty, engaging in double-loop learning can be a significant advantage in clarifying organizational routines and enhancing the organization’s ability to answer internal and external expectations. In addition, it can lead to a better understanding of the sources of problems, and provide the organization with innovative solutions that better suit its goals.

However, double-loop learning can also be challenging to implement because it requires a collaborative climate, reflection, and openness to change, which run counter to the usual organizational culture of competition and success by any means (Senge, 1990). While it has already been found that MLs are essential for school management, it is still unclear how they contribute to organizational learning. The present study seeks to delve deeper into this topic, and understand how MLs influence organizational learning and school uncertainty. 

Preliminary findings indicate that MLs often engage in single-loop learning, which primarily focuses on immediate problem-solving or what they term “putting out fires” and “patching up holes”. Such learning involves identifying and addressing problems swiftly to prevent escalation. For instance, in one case, the grade-level coordinator told us how she stepped in to teach a class in the absence of a subject teacher, thereby reducing the immediate disruption and maintaining the educational routines. However, the findings show that this order is temporary since the same problems arise again the next day with another teacher. In addition, we did not find indications of double-loop learning. Following the previous example, the grade-level coordinator did not look for the reasons for teacher absenteeism, at the organizational routine of checking the teachers’ attendance, or the reward system accompanying attendance and absence. In another case, the subject coordinator told us that she did not know how to promote a bad teacher. The coordinator can see that he is trying, he prepares, works, and asks for help, but she does not know how to guide him to connect with the students, partly because she does not know the background to his difficulties, and how to provide him with professional guidance so he can improve. In other words, she knows what the problem is but does not know how to break it down, examine its components, and reassemble them in a way that suits the teacher, the students, and the school.

The present multiple-case study was conducted from 2021 to 2022 to better understand a complex phenomenon (Yin, 2018), in this case, the MLs’ contribution to organizational learning. Four large, high-achieving Israeli high schools attended by more than 1,000 students, were studied. These schools, led by principals with at least three years of leadership seniority, varied in aspects of sector (state-secular and state-religious), district (south and center), and student demographics (medium and high SES). A team of four researchers collected the data. They conducted semi-structured interviews with four school principals and 61 middle leaders, specifically subject and grade-level coordinators. These MLs hold broad responsibility for all the students, and lead a team of teachers teaching mandatory subjects such as language, English, and mathematics, or specific age groups (respectively). In addition to interviews, the research team observed 33 team and management meetings, and collected relevant documents such as school schedules and work plans. All collected data, including audio recordings of interviews and meetings, were transcribed, and securely stored in a protected digital folder accessible only to the research team. The data was analyzed both inductively and deductively using Dedoose software. The inductive analysis involved examining all the data to develop categories and subcategories, and grouping the main recurring themes in the interviews. For instance, categories such as decision-making processes and problem-solving strategies among MLs were identified. Concurrently, a deductive analysis was performed in accordance with the conceptual framework of organizational learning theory (Argyris & Schön, 1996), which focuses on single-loop learning and double-loop learning. Ethical standards were maintained by presenting the research objectives to the interviewees, protecting their privacy and anonymity, and obtaining their informed consent.

We conclude that the MLs focus on single-loop learning, and play a pivotal role in maintaining organizational order and stability. This often leads to mere school survival, as expected from the school environment, but less to double-loop learning, which can clarify school uncertainty. It is interesting to note that the MLs present great confidence in their role, and know exactly what to do. In fact, the better they understood how to perform what was expected of them, the less they engaged in double-loop learning. For example, the greater the pressure from the senior management to prepare the students well for exams, the fewer questions were asked about exams as a valid and reliable measure to test the students’ knowledge. This leads us to another conclusion, that the short-term effectiveness of single-loop learning inhibits engagement in double-loop learning. This means that the effectiveness and immediacy of MLs in problem-solving often reduce the perceived need or motivation to engage in double-loop learning. Such learning, though more challenging, can improve the school and even reduce uncertainty. However, it might create a different form of ambiguity as it changes well-known assumptions, rules, and routines. This conclusion helps to explain the complexities involved in implementing change within schools. This study has theoretical and practical contributions. Theoretically, it offers new insights regarding the nature of MLs and their role in navigating uncertainty and preventing crises within the school. Moreover, it emphasizes the importance of balance in organizational learning. It is not either immediate problem-solving in single-loop learning or long-term deep learning in double-loop learning, but a combination that requires a lot of practice. The practical contribution is a guideline for MLs’ professional development program that emphasizes such a combination: how to do both– promote clarity by problem-solving, and innovative system thinking by engaging in double-loop learning.

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