Pedagogies for Leading Practice

Sandra Cheeseman, Rosie Walker

• Publisher: Routledge
• Year: 2018
• ISBN:
  9781351266901
• Edition: 1
• Topic: Education
• Subtopic: Education General

Bringing together the experiences of professionals from around the world, this essential text explores the intersections between pedagogy and leadership to consider how effective Pedagogical Leadership can be used to foster the collaborative engagement of children and their families, staff and practitioners, and ensure high quality provision in early years settings and services.

Pedagogies for Leading Practice showcases a vast range of experiences and ideas which are at the heart of professional practice. Written to provoke group discussion and extend thinking, opportunities for international comparison, points for reflection, and editorial provocations will help students, policy-makers and others engage critically with wide-ranging approaches to leadership in early years practice. Considering varied forms of collaborative working, the challenges involved in becoming a pedagogical leader, and the role of management in meeting insitutional demands and the needs of the wider community, chapters are divided into four key sections which reflect major influences on practice and pedagogy:

• 
  
  

• Being alongside children

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• Those who educate

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• Embedding families and communities

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• Working with systems

Offering insight, examples and challenges, this text will enhance understanding, support self-directed learning, and provoke and transform thinking at both graduate and postgraduate levels, particularly in the field of early childhood education and care.

Section 1

BEING ALONGSIDE CHILDREN

1

Engaging with data to foster children’s learning

From population data to local projects

Sandra Cheeseman

The last decade has seen increased attention to population data studies to inform early childhood policy and practice. Designed to capture broad trends in social, economic and education patterns, “big data”, as these studies have come to be known, are increasingly cited as the “evidence base” for recommending policy approaches to early education and care. Taking a broad population view, big data studies aim to capture a macro-view of human experience. Capturing broad trends and changes in social, economic, health, and education outcomes, big data studies aggregate information and generate results, “… as a means of monitoring the status of early childhood development and then tracking progress over time” (Janus, Harrison, Goldfeld, & Guhn, 2016, p. 1). Not designed to capture the nuanced experience of the individual, or represent the complexity of lived experience, population data sets provide a helicopter view of life, a high-level overview. They require cautious and careful analysis, to avoid over-simplifying lived experience and representing life as quantifiable or measurable.

This chapter will examine the trend toward big data as it relates to early childhood policy and practice. Beginning with an international perspective and the announcement of the Organisation for Economic Coppoeration and Development’s (OECD) International Early Learning Study (IELS) (OECD, 2015), I join others in problematising the status of international comparative work that is carried out through a standardised assessment of children’s learning and development. I contemplate the potential of such instruments to generalise and simplify the complexity of young children’s lives and contribute to a narrowing focus on their early childhood experience, in order to achieve a limited range of desirable outcomes. In the second part of the chapter, I focus more specifically on the Australian context and findings from their early years population study, the Australian Early Development Census (AEDC). I conclude with a reflection on how Educational Leaders might be both challenged and inspired by their engagement with big data and contribute to meaningful understandings of early learning using their own wisdom, reflection and localised research in working alongside young children.

I come to this chapter with a background as an early childhood teacher with a more recent experience as a research academic. I draw on these two experiences to find synergies between research and practice and to advocate for the importance of the intellectual work of teachers as researchers in their daily practice. As a long-time proponent of localised qualitative studies, I have more recently recognised the need to get some “skin in the game” of big data studies. The chapter invites Educational Leaders to critically engage with the trend toward big data with a view to making informed contributions to its design and use. Moving from broad based population studies to locally contextualised practitioner inquiry projects may offer early childhood educators a way to contribute and influence big data conversations. In this way, Educational Leaders can capitalise on what big data studies offer but use them to shift conversations toward more democratic and inclusive understandings of the complexity of local contexts and the lived experience of young children.

An international trend

The world of big data in relation to young children has gained unprecedented momentum over the past decade both internationally and in Australia. In a relatively short space of time, advances in technology have enabled not only the capacity to collect and collate big data sets in ways that we have not known before, but now to also link data sets, and to correlate and compare findings from one source to others. For example, it is now possible to link population data to systems data generated through quality assurance schemes, and then later to school achievement standardised test results. Such linkages afford governments the capacity to connect the experiences of children in their early years settings with their later educational attainments. A confidence in these linkages and their assumed value appears to be an attractive option for governments attempting to design education policy based on science. The rhetoric is couched in terms of the end benefit to children, in better understanding how best to support their learning and development (OECD, 2015). Urban and Swadener (2016) note the persuasiveness of this, but question if such linkages will in fact lead to the desired aim of a more socially just and equitable future.

Confidence in big data relating to early childhood was signalled perhaps most clearly by the OECD’s announcement to conduct an IELS (OECD, 2015).¹ While in the design stage, the purpose of the study was said to, “…provide countries with a common language and framework to learn from each other and, ultimately, to improve children’s early learning experiences. Countries interested in this study are particularly focused on improving equity of outcomes for disadvantaged children” (OECD, 2015, p. 9).

The OECD’s ambition to provide a “common language” and “framework” along with claims that such a study will improve equity outcomes, has been widely critiqued. In particular, Moss et al. (2016) warn of the potential of such a comparative work to reduce childhood to a series of desirable, narrow academic outcomes, all geared toward the production of a citizen measured by the capacity to contribute to national and global goals. Aimed at capturing information about children aged 4 to 5 years, the identified domains for assessment were determined as: self-regulation, oral language/emergent communication, mathematics/numeracy, executive function, locus of control and social skills. The domains identified draw on earlier work undertaken by University College, London, and have been affirmed by the OECD as being “…predictive of early learning skills” (OECD, 2017, p. 18). The explicit intent of the study states that

In time, the information can also provide information on the trajectory between early learning outcomes and those at age 15, as measured by PISA. In this way, countries can have an earlier and more specific indication of how to lift the skills and other capabilities of its young people

(OECD, 2015, p. 103)

While not dismissing this well-intentioned and interesting piece of work, the end point of PISA (Program for International Student Assessment) (see OECD, 2018) – the academic performance of 15-year olds, on a selected dimension of human achievement – as the focus of determining what might shape the experiences of very young children, is somewhat concerning. The potential to reduce learning and development outcomes for ever-younger children to a handful of predictive characteristics, runs the risk of technicising early childhood programmes and promoting pedagogies that produce identifiable results on a limited range of outcomes (Moss et al., 2016; Pence, 2017; Urban & Swadener, 2016).

Vandenbroeck (as cited in European Early Childhood Education Research Association, 2017) argues that the claims of studies such as PISA reflect a myth, that “facts and objectivity…produce truth” (p. 4). Lister (2003, cited in Vandenbroeck, Roets, & Roose, 2012) warns of the propensity for governments to value measures based on predefined outcomes as the most valid form of research. “The evidence-based paradigm and the subsequent outcome-focused research, frame children as ciphers for future economic prosperity in becoming self-providing, autonomous and responsible individuals, rather than recognising them in what they are” (Vandenbroeck et al., 2012, p. 543). While considerable debate surrounds the collection, use and potential dangers of the International Early Learning and Child Well-Being Study (IEL&CWS) (see Moss et al., 2016), such population studies appear to be of increasing interest to governments seeking a scientific evidence base on which to establish policy decisions.

The rhetoric of big data is hard to contest. Seeking to overcome the impact of social and economic inequities and providing a knowledge base on which to plan for brighter futures, the promise of big data studies is indeed persuasive. Their potentials and limitations must, however, be critically examined to ensure that they do not supplant other notable research methods that provide rich understandings of human experience. Without such critical reflection, there is the risk of narrowing the focus on those dimensions that can easily be measured in a big data study while silencing or diminishing the importance of those things not so easily captured within big data methodologies.

The prominence and importance of school readiness

A further concern expressed about the focus on big data studies in early childhood is as Keating (2007) claims, their focus on young children as largely framed in terms of their “readiness for school” with a view to recommending strategies to mitigate the limitations for children who are “less ready” and help them overcome their “lack of readiness” (p. 562). Vandenbroeck et al. (2012) concur and warn that defining early childhood education and care (ECEC) as a preparation for school narrows the focus of ECEC and dismisses the views of parents, children and practitioners about other important aspects of learning and development. Secondly, they highlight that such a focus on the child being readied for school discredits and makes invisible the “…fundamentally pedagogical question: how can we make schools ready for diverse children and their parents?” (Vandenbroeck et al., 2012, p. 543).

The danger of population studies that frame the desirable end point as academic achievement in later schooling is that they have the potential to narrow the possible ways of constructing early childhood education and care programmes and direct attention to a relatively small number of desirable skills and attributes. Such assumptions have the potential to ignore the cultural, social and historical diversity that underpin children’s rights and run the risk of privileging particular ways of being and knowing. My position at this point is not to suggest that there is no place for population studies, but I caution that big data studies have the potential to mislead and provide a false sense of confidence if not treated with a high level of analysis and contextualisation. Understanding the purpose and limitations of big data studies is the first step in using them wisely

An Australian example of big data – the Australian Early Development Census (AEDC)

In 2009, the Australian Government commissioned the Australian Early Development Index (AEDI). Based on a Canadian population data instrument – the Early Development Instrument (EDI) was designed to measure the developmental health and well-being of populations of young children (Australian Government, 2018a). The triennial data collection based on the AEDI is now known as the Australian Early Development Census (AEDC) (Australian Government, 2018b). Data collection is held every three years, with collection in 2009, 2012, 2015 and 2018. The census involves teachers of children in their first year of full-time school (at approximately age 5) completing a research tool that collects data relating to five key areas of early childhood development referred to as “domains”. Table 1.1 shows the five development domains and descriptors that form the basis of the AEDC data collection.

Table 1.1 Descriptions of the AEDC development domains

Domain	Icon	Domain description
Physical health and wellbeing		Children’s physical readiness for the school day, physical independence and gross and fine motor skills.
Social competence		Children’s overall social competence, responsibility ans respect, approach to learning and reading to explore new things.
Emotional maturity		Children’s pro-social and helping behaviours and absence of anxious and fearful behaviour, aggressive behaviour and hyperactivity and inattention.
Language and cognitive skills (school-based)		Children’s basic literacy, interest in literacy, numeracy and memory, advanced literacy and basic numeracy.
Communication skills and general knowledge		Children’s communication skills and general knowledge based on broad developmental competencies and skills.

Similar to the IEL&CWS, the AEDC domains have been shown to predict later health, education and social outcomes (Australian Government, 2013). Importantly and similar to the IEL&CWS, the AEDC is not a screening tool and is thus not intended for individual diagnostic purposes (Goldfeld, Sayers, Brinkman, Silburn, & Oberklaid, 2009). The AEDC data is used by communities, policy-makers and researchers “to review the status of children’s development and to guide service planning to improve children’s outcomes” (O’Connor et al., 2016, p. 33).

As stated, the purpose and intent of the AEDC has merit. In capturing trends, the information gained can provide a useful starting point for reflecting on how Australian children are faring and where more supports might be useful. While acknowledging that the AEDC was never designed for service level interpretation, the trends highlighted in the data can be important starting points for early childhood professionals to engage in the critical conversations ...

Table of contents

• Cover
• Half Title
• Series Page
• Title Page
• Copyright Page
• Dedication
• Acknowledgements
• Series Editors’ Acknowledgements
• Table of Contents
• List of figures and table
• About the series editors and contributors
• Preface: Finding purpose and direction
• SECTION 1: Being alongside children
• Editorial provocations: Engaging readers and extending thinking
• SECTION 2: Those who educate
• Editorial provocations: Engaging readers and extending thinking
• SECTION 3: Embedding families and communities
• Editorial provocations: Engaging readers and extending thinking
• SECTION 4: Working with systems
• Editorial provocations: Engaging readers and extending thinking
• Coda Thinking forward
• Index