The impetus for marketisation, standardisation, and accountability in education has been variously but widely argued as the means to raise student attainment (DfE, 2010a; Sahlberg, 2006). Linked to funding, pupil numbers and accountability measures, student attainment data creates the metrics through which government policy is driven (Winter, 2017; Lingard, 2011). From this perspective, this section describes the purpose of student attainment data and how this is woven into and supports each mechanism.
2.4.1 Uses of Data
The data collected on or about students’ attainment is varied, wide-ranging and may serve several purposes (Stokes, 2016). Shared with a variety of stakeholders, both in and out of the classroom (Kelly and Downey, 2011) attainment data has been used for example to explore differences in groups of students by their characteristics (Black-Hawkins et al., 2017; Jerrim, 2017), carry out question-level analysis of test papers (Thomson, 2015, 2019) and to monitor school and teacher performance (Perry, 2016). Although not straightforward (Strand, 2014a, 2015), attainment data can also be used to identify need, allocate resources, funding and support
(Goldstein, 2001).
Drawing attention to the differences in student attainment also serves to identify which students are underperforming and highlights any attainment gaps. Defined as the “disparity in performance on various educational measures between different groups” (Goodman and Burton, 2012, p. 500), attainment gaps are persistent and widen as a child moves from primary to secondary school (Andrews et al., 2017). Among the complex combination of factors that determine outcomes, it is difficult to isolate the exact causes of the attainment gaps between students of different socio- economic status, ethnic group or gender. Having said this, tackling attainment gaps
between different groups of students remains a priority for schools and has been for some time (EEF, 2018b; Sharp et al., 2015; Wilson, 2014).
In addition to identifying the attainment gaps between different groups of students, student-level attainment data provides some of the information required to judge school performance. However, attributing the improvements in student attainment to specific actions has not been straightforward (Goldstein, 2001), any change in attainment could be associated with a range of different factors including teacher intervention, changes to assessment methods or changes in cognitive demand of the examination papers. The steady rise in KS2 English and maths results from 1995 to 2003 and the changes in the variation in the grade boundaries between the different level outcomes are a case in point. In this instance, the impact of the test technique and teaching to the test were found to be contributory factors in the rise of
standards (Tymms, 2004). Similarly, Gove (2009) suggested that the continued rise in GCSE attainment was driven by secondary schools “gaming the system” in order to meet to reach their 5A*-C GCSE targets. As discussed earlier, key accountability measures linked to student attainment and used to judge school performance may directly impact school exam entry practices.
Student attainment data also plays a part in the performance management process of teacher appraisal and evaluation. Contrasting arguments suggested that, even where value-added measures were taken into account, student examination outcomes were not reliable measures of teacher effectiveness (Goldstein, 2001). However, despite its limitations, when compared to classroom observation or pupil survey, student attainment data was argued to be the best way to judge teacher effectiveness
(Murphy, 2013). There are inherent difficulties in measuring the progress of students year on year as they move from one teacher to the next or from one key stage to the next (Wilkins, 2011; Goldstein, 2001). Furthermore, the inclusion of student
attainment data in the decisions around performance-related pay can also distort teacher practice and narrow the curriculum experiences for students, in addition to having a divisive impact within the school environment, (NASUWT, 2016; Marsden, 2015; Baker et al., 2010).
2.4.2 Data and the STEM Agenda
The analysis of student attainment data can play a significant part in monitoring the progress of students in particular fields of study and the participation of students in the STEM subjects has been of interest to the government for some time (Hyam, 2006). Since science and innovation were at the heart of the UK’s economic plan, the intention to close the STEM skills gap was addressed by taking direct action to
increase the uptake of single sciences at GCSE and A-level sciences (Archer, 2015; HM Treasury, 2014). The government announced that, from September 2008, all 14-year- olds achieving a Level 6 score in the KS3 national tests would be entitled to follow a triple science GCSE course (Tomei et al., 2015). However, numerous factors whether at school-level, involving parental science capital or gender role models contribute to student’s decisions to continue studying science post-16 (DeWitt and Archer, 2015; Bennett et al., 2013; Hampden-Thompson and Bennett, 2013).
The long-term wider goals for STEM, to raise the number of students studying sciences, used changes to examination and curriculum as its principal drivers. The coalition government used school performance and accountability to drive
widespread curriculum reform, as seen with the introduction of the new national curriculum with more demanding content and the revised A-level specifications (HM Treasury, 2014). National data sets pointed to the year on year increase in the number of students achieving A and A* grades in GCSE sciences with the resultant conclusion that the exams were getting easier (Gove, 2011). The Royal Society
review of science examination papers across a three-year period confirmed that there was insufficient challenge for the more able students (Osbourne, 2011). As such, more rigour was introduced, multiple-choice examination papers withdrawn, opportunities to retake exams reduced and certain vocational qualifications were no longer given GCSE equivalent status (e.g. BTECs). The resulting changes to the accountability measures published in the performance tables have had an impact in the subjects that students take at GCSE (Parameshwaran and Thomson, 2015). Structures of control have been put into place through the accountability framework that requires schools to manage their curriculum offer in science. Whether the changes to the curriculum will ultimately lead to a greater number of students joining the STEM pipeline remains to be seen.
2.4.3 Data and International Comparisons
It was England’s declining performance in the PISA tests in 2012 that was to have a direct impact on government education policy as evidenced in the speech given by Michael Gove, the then Secretary of State for Education, to the House of Commons (DfE and Gove, 2012). Through “policy borrowing”, Gove set out to replicate in the UK the educationally successful structures established in other countries. Something that Alexander (2012, p. 5) considered to be a “naïve belief”. Using the PISA
results, Gove claimed that England was falling behind South East Asian countries, “leaving our children behind in the global race” (DfE and Gove, 2012). The reforms that he outlined in the speech have to some extent been adopted and have not only changed the curriculum but the accountability frameworks in England too. Jerrim (2013) demonstrated that the alarm bells raised over England’s failing education system were unwarranted, as the evidence lacked the necessary strength to justify the sweeping changes that the Conservative government introduced. Amplified by the media, the panic resulting from England’s perceived poor performance in the
PISA tests is symptomatic of the keen interest in its outcomes, this lays the data open to misinterpretation and misuse (Tomei et al., 2013). Furthermore, others have challenged the validity and reliability of PISA scores, particularly the way in which they have been used as a measure of a whole cohort learning across all subjects rather than merely a sample of 15-year-old’s performance in three subjects (Meyer and Benavot, 2013; Alexander, 2012).
The current education environment is one in which data is an integral tool to a teacher’s knowledge of their students but is also used as a tool by a range of
stakeholders with differing agenda. It is used in policy decisions fundamental to the instigation of many of the changes that we have seen in education as a whole. As a means of control, student attainment data and the accountability measures that report the outcomes of standardised tests, determine to some extent what happens in the classroom (Winter, 2017). The prime use of student data in this study is to examine what it reveals about the changes in science attainment over time and to compare the outcomes of students with different background characteristics in response to the changes to curriculum and assessment.