Teacher education in England has undergone dramatic changes over the past forty years. From the secret garden of the curriculum with the teacher as a scholar mostly undisturbed by central government until James Callaghans speech at Ruskin college in 1976, it has gradually moved towards teaching conceived of as a craft by government, and a “school-led” system with high accountability mechanisms has emerged (Childs & Menter 2013). Meanwhile how teachers are educated or trained has become a central means for government to change education (Furlong 2001). The recent changes in initial teacher education (ITE) from 2010 onwards resulted in the majority of teacher training places being awarded to schools in 2018 (mostly through school direct – it should also be noted that even though schools hold the training places, the universities still do the majority of the actual training – the paper elaborates upon this distinction). This paper attempts to understand how policy was changed from university dominance over ITE in 2010, to the “school-led” system of 2018. It does so through analysing the period 1976-2018 with an emphasis upon 2010-18, employing a neo-institutionalist analytical framework (Scott 2013 – presented below) in order to capture the institutional changes experienced within the ITE organizational field.

               Since the limited oversight of the “secret garden era” (1944-1984), the concept of accountability through inspection and data has become a central element of government education policy creating new levers of control (Ozga 2009), arguably resulting in an “audit-culture” (Graf 2009), with potential implications for teacher education (perhaps even more so since 2010) (Childs & Menter 2013). Moreover, a rise in the use of “evidence” and “expertise” in education policy documents has been observed. Although often characterised by an import of foreign “best practice” (Adamson et al. 2017; Chung 2016), reviews/reports such as the recent (2015) Carter review is a common enough occurrence in English education policymaking with ramifications for policy development (Jones 2016), therefore we may expect both national and non-national evidence used to justify policy. Additionally, as mentioned above, the profession and its associated norms have been undergoing change, from the teacher conceptualised in discourse as a scholar (pre 1976, Furlong 2001), to the teacher described as a craftsman under Secretary of State for Education Michael Gove (Childs & Menter 2013).

The analytical framework centres upon understanding regulative, normative, and cultural-cognitive institutional pillars (Scott 2013) in order to capture the full contextual changes education policy has undergone. Based upon the developments in education policymaking explained above, we may assume to find different developments relatable to each of the three modes of change; within the regulative pillar, we expect coercive changes facilitated through a “governing by numbers” approach, enabling direct government control through sanctions and funding incentives (Ozga 2009). Normative change we assume is related to the changing teacher profession (Furlong 2001; 2016) and views on education (Biesta 2009; 2014), as well as changes within the governing structure (mainly the end of the “quangos”) (Childs & Menter 2013). Central to the cultural-cognitive pillar are views on what is possible and acceptable in ITE, here we predict an importance of the “audit-culture” (Graf 2009) coupled with “evidence” in the form of blueprints and “expert” opinions of what is considered legitimate “best-practice”. The ideational contestation of “best practice” we envision is one of the driving forces behind the change from the secret garden towards the school-led system. Lastly, we keep in mind the preposition of Scott (2013) that what is central to institutional change is the contestation between, and within, the pillars and that contestation within the cultural-cognitive pillar is likely the driving force behind change.

The analysis was done with an inductive – deductive two-step approach. The first step comprised a broad soaking and poking phase (George & Bennett 2005) reinforced by the circular method of the research process (Flick 2009), where documents, interview objects, and relevance of the theoretical approach was identified through a combination of literature review, initial expert interviews (N=3) and participation in public policy discussion events involving government and non-government actors (N=3). The second step comprised document analysis of the identified documents (ongoing, currently N=36 – of various length from 1 page up to 128) as well as elite interviews with relevant actors (ongoing - currently N = 8) chosen through purposeful sampling centred upon national state authorities, interest organisations, and experts consulted by these organisations and agencies. The inductive-deductive approach was carried over into the second step, where an eye was kept upon emerging “new” information with implications for our hypotheses, keeping in mind that one piece of evidence can be inductive or deductive but not both (however one case can contain both inductive and deductive research – George & Bennett 2005). We established the timeframe 1976-2018 as most relevant in terms of available documents and interesting content. Following the approach of Childs & Menter (2013), broader lines were drawn from 1976 up to 2010 to understand the historical context of ITE policymaking, before more intense scrutiny was put upon the period of 2010-2018 due to the majority of change having taken place during that time. Broad policy documents (white papers, consultations and their responses, public documents of policy position), instructive documents (circulars, instruction papers), and reports by government and non-government actors were analysed in depth to determine the form and structure of argumentation, the content in terms of logics related to the use of data and evidence, and target audiences (addressees). The document analysis also consisted of both qualitative coding, and quantitative text analysis of text corpuses using the software MAXQDA, where word frequencies and the presence/absence of concepts across corpuses were analysed.

preliminary results: We observe ideological contestation regarding who should govern education and how, coupled with contestations regarding the purpose of education, with implications for how teachers were to be trained. In broad lines, we find that this ideational contestation at a national level destabilised the normative authority of educationalists as the sole “experts” on education by the mid 1980s. Through further destabilisation of existing norms, and contestation of “best practice”, central government attempted by the late 1990s to implement coercive mechanisms (national curriculum) to shape teacher education, with mixed results. Here we note a dual change of importance, on the one hand the need for accountability had been firmly established (the right of government to meddle in education), while the concept of evidence and data as central for policy was beginning to take hold. By 2010, government had the legitimacy required to utilise coercive mechanisms to further change the normative landscape of ITE, aided by a wider audit-culture where accountability in the form of inspections and the use of “data” has become taken for granted. The resulting outcome/performance focused approach has arguably transformed ITE. The rise of "evidence" in any form as a bestower of legitimacy, together with the coercive mechanisms of data driven governance appears to have created a new institutional environment where education (and teacher education) is becoming self-sustained at a school-level through a self-referencing system driven by data. Scholarly value, the old prerogative of the educationalist, is now replaced by data as evidence. Thus, we note a cognitive shift centred upon what is believed to be “best practice”coupled with a change in normative authority. Within this new environment, a shift from the university to the school is both possible, and conceivable as “value for money”, it all depends on how data is interpreted.

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