麻豆淫院

Across education systems internationally, school leaders are increasingly required to use data to demonstrate improvement (Selwyn, 2019; Allen et al., 2018). Attendance, attainment, behaviour and engagement metrics shape accountability, inspection and leadership decision-making. Yet despite this growing reliance on data, persistent gaps in inclusion, belonging and wellbeing remain difficult for schools to interpret or address.

Drawing on doctoral mixed-methods research analysing lived-experience data from more than 26,000 staff and students, this blog post argues that the issue is not a lack of data but a misalignment between what schools measure and what they need to understand.

When measurement narrows understanding

Traditional school data is effective at capturing outcomes but far less effective at capturing experience. In most schools, data systems focus on indicators such as attendance, attainment, behaviour incidents and exclusions, typically recorded through Management Information Systems (MIS). While these metrics show what is happening, they rarely explain how it feels to learn or work in a school — or why certain groups disengage.

Critical data studies have long argued that data is not neutral but shaped by social values and power relations (Selwyn, 2019). Feminist Data scholarship highlights how data systems often privilege dominant perspectives, while Queer Data challenges assumptions that identities are fixed or easily categorised (D’Ignazio & Klein, 2023; Guyan, 2022). These perspectives encourage educators to question how data is collected, whose experiences it represents and whose voices may be missing.

In many school systems, demographic characteristics such as gender, disability, ethnicity and home context are treated as discrete variables rather than intersecting realities. This can obscure the experiences of individuals whose identities are multidimensional, fluid or undisclosed.

‘In many school systems, demographic characteristics … are treated as discrete variables rather than intersecting realities [which] can obscure the experiences of individuals whose identities are multidimensional, fluid or undisclosed.’

What the research revealed

Across the dataset, disparities emerged that were not visible through headline metrics alone. For example, 33 per cent of students who identified as neurodivergent or having additional learning needs strongly disagreed that teachers listen to their perspectives, compared with 8.7 per cent of their peers.

When analysed intersectionally, these patterns were stronger among students with invisible disabilities, those who did not disclose needs and those experiencing socioeconomic disadvantage. These students reported significantly lower levels of belonging and perceived support.

Staff experiences reflected similar dynamics. Participants occupying multiple marginalised positions described reduced trust, limited professional opportunity and reluctance to engage honestly with institutional data collection processes.

These findings echo wider research demonstrating that belonging strongly predicts engagement, wellbeing and outcomes (Allen et al., 2018). Yet belonging itself is rarely measured directly within school data systems.

Introducing Kaleidoscopic Data

In response to these limitations, doctoral research introduces ‘Kaleidoscopic Data’, a human-centred analytical framework for educational evidence.

Rather than treating identity and experience as fixed categories, Kaleidoscopic Data recognises that lived experience is dynamic, relational and shaped by context. Like a kaleidoscope, meaning shifts depending on perspective.

Methodologically, this approach combines quantitative patterns with qualitative lived experience. Large-scale survey data is interpreted alongside participant voice, allowing leaders to understand not only what patterns exist but why they occur.

Voice is treated not as anecdotal but as evidence that contextualises quantitative trends.

Social capital as a missing lens

A further insight from the research is the importance of social capital in understanding inclusion. Drawing on relational conceptions of social capital (Hanifan, 1916; Putnam, 2001), the findings suggest that trust, belonging and connection operate as forms of capital within school communities.

Traditional datasets focus on individual outcomes. Kaleidoscopic Data instead highlights relational dynamics within schools, revealing how trust, safety and connection shape engagement and wellbeing.

In this sense, Kaleidoscopic Data enables leaders to see patterns of inclusion that traditional metrics alone cannot capture.

Implications for leadership and practice

These findings position data use as an ethical and leadership issue rather than simply a technical one. Decisions about what is measured, how questions are asked and how insights are interpreted shape whose experiences become visible.

Schools might complement attainment and attendance metrics with anonymous lived-experience surveys capturing belonging, safety and voice. Such approaches can help identify inclusion challenges earlier and support more responsive leadership.

Human-centred data approaches do not replace existing metrics but expand what counts as evidence, enabling leaders to interpret outcomes within the lived realities of their communities.

Concluding thoughts

As schools increasingly rely on data to guide improvement, the challenge is not simply collecting more information but interpreting it meaningfully. Kaleidoscopic Data offers a way to bridge this gap, helping leaders understand patterns of inclusion, belonging and trust that traditional datasets often overlook.

References

Allen, K.-A., Kern, M. L., Vella-Brodrick, D., Hattie, J., & Waters, L. (2018). What schools need to know about fostering school belonging: A meta-analysis. Educational Psychology Review, 30(1), 1–34.

D’Ignazio, C., & Klein, L. F. (2023). Data feminism. MIT Press.

Guyan, K. (2022). Queer data. Bloomsbury.

Hanifan, L. J. (1916). The rural school community center. ANNALS of the American Academy of Political and Social Science, 67(1), 130–138.

Ponsford, N. (2025). Intentional inclusion: Investigating equitable education and intersectional EdTech [Doctoral thesis, Bournemouth University].

Putnam, R. D. (2001). Bowling alone. The collapse and revival of American community. Simon & Schuster.

Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.