In addition to basic numerical skills, the concepts of ‘mathsanxiety’ and ‘maths confidence’ are both used in the literature to describe factors that can also affect mathematical calculation abili- ties. For example, McMullan et al. (2012) used both of these terms in their study; Bull (2009) focussed on mathsanxiety; while Wilkins (2015) investigated maths confidence. This research study focuses on mathsanxiety because anxiety seems to be an underlying factor that can affect the development of confidence. In addition, it appeared to relate better to the feelings expressed by students when they talked about their feelings and experiences while learning maths concepts and doing maths tasks. Regarding mathsanxiety, it is common in the literature to follow the definition given by Richardson and Suinn (1972), who defined mathsanxiety as “feelings of tension and anxiety that interfere with the manipulation of numbers and the solving of mathe- matical problems in a wide variety of ordinary life and academic situations” (p. 551). This sug- gests that maths calculation abilities cannot be separated from mathsanxiety, and this result is of particular importance in this context as Bull (2009), Glaister, (2007) and Walsh (2008) have confirmed the existence of mathsanxiety in nursing students, and Pozehl (1996) found that nursing students had higher levels of mathsanxiety than non-nursing students. Furthermore, Bull (2009), Glaister (2007) and McMullan et al. (2012) have found that mathsanxiety and neg- ative attitudes towards maths have negatively affected students’ performance in drug calculation tests. In order to address mathsanxiety, nursing educators thus need to foster a supportive learn- ing environment using multiple teaching strategies to reduce mathsanxiety and develop maths skills (McMullan et al., 2012).
Our instrument for measuring the level of first year science students’ mathematics anxiety also included components investigating students’ mathematical confidence, affective engagement, behavioural engagement attitude to technology associated with the study of mathematics. The anxiety scale itself, pertinent to this current research, consisted of the Abbreviated MathsAnxiety Scale (AMAS) of Hopko, Mahadevan, Bare and Hunt (2003) with some minor modifications, plus two additional items in consideration of our student populations and the context of the subject, SC1102, in which the students were enrolled (Table 1). We chose the anxiety scale of Hopko et al., (2003) because its brevity was considered a significant advantage and it compares well with other widely used mathsanxiety rating scales (Ashcraft & Moore, 2009). Students responded to items on our modified AMAS using a 5-point Likert scale ranging from 1 (low anxiety) to 5 (high anxiety), with the total score representing the mean response of the eleven items.
There is clear evidence that at both national and specific vocational levels there are concerns about numeracy and communications skills. Research has indicated that in both of these areas there are barriers that restrict skills development and inhibit academic performance. Research studies indicate that communication apprehension and mathsanxiety are barriers to skills development in their specific areas and can therefore restrict academic performance and achievement. The first year intakes to courses at XXXXXX University were questionnaired prior to contact with academic staff. The results show that the students recruited to these different courses had significantly different levels of communication apprehension and mathsanxiety. Previous studies and the current research both show that gender and educational background are influencing factors.
Table 4 shows the model parameters for predictors of mathsanxiety at age 18 (an equivalent table reporting the same model fitted to standardized scores can be found in the electronic supplementary material). With respect to statistical significance, biological sex, SDQ emotional symptoms (pre- transition), the change in SDQ emotional symptoms, maths attainment (pre-transition), the change in maths performance across the transition, and general anxiety at age 18 all significantly predicted mathsanxiety. Bearing in mind that mathsanxiety could range from 1 to 5, males were 0.37 points lower on this scale than females, and a point increase on the general anxiety scale (which ranged from 1 to 4) equated to a 0.36 increase in mathsanxiety. With respect to the substantive predictors, a unit change in maths attainment pre-transition (at age 9 measured on a 5-point scale) equated to a 0.46 unit decrease in mathsanxiety 8 years later at age 18. A unit increase in the rate of change (i.e. the slope) in maths attainment across the transition equated to a −0.32 decrease in mathsanxiety at age 18. To unpick what this value means, first let’s look at what a typical change in maths attainment would be across the school transition. In our earlier model we operationalized the primary-to-secondary education transition as the 3-year period between ages 9 and 12 and found that maths attainment increases at a rate of 0.48 units per year (table 1). A typical change in maths attainment across three years during which the school transition occurs would be 3 0:48 ¼ 1:44 units. Imagine a child who shows no improvement in their maths ability over the same three years. Their slope for attainment will be 0, and it will be 1.44 units lower than a typical child. Their predicted mathsanxiety at age 18 will correspondingly be −1.44 × −0.32 = 0.46 higher on the 5-point mathsanxiety scale than the average child. Put another way, the rate of change in maths attainment had a small effect on mathsanxiety at age 18.
Ability grouping showed a clear pattern and had a strong impact on whether a student believed they were in the right group and consequently their level of confidence, or mathsanxiety. Similarly there also seemed to be an effect depending on which group the students was in, though not as strong. This is consistent with Boaler (2000) who found that many students were uncomfortable with the set they were in. This relationship could perhaps occur due to a move between sets being unexpected and new territory as opposed to whether the student believes they are capable of achieving within a higher or lower class. It could be beneficial to both the pupil and the school to gradually ease a student into a new class and to include them in the decision making process.
Previous studies have identified the lack of primary school teachers with mathematical backgrounds (Vorderman, 2011; Hillman, 2014) and the importance of teacher-pupil relationships (Birch and Ladd, 1997; Attard, 2013; Coe et al, 2014), along with teachers confidence in teaching mathematics (Beilock et al, 2010). The effect that teachers have on their pupils has already been evidenced with suggestions that a focus on teaching methods that encourage students to get the right answer (Geist, 2000) leads to methods focusing on repetition and testing, which undermines the pupils’ natural thinking processes and places the pupil in a more passive role (Sun and Pyzdwroski, 2009; Tall, 2014). The focus on memorisation leads to a lack of engagement and risks students’ learning being hindered due to their learner needs not being met (Oberline, 1982 in Jackson, 2008), which can lead to negative attitudes to mathematics (Popham, 2008) as well as mathematics anxiety (Scarpello, 2007; Jackson, 2008; Chinn, 2012; Marsall et al, 2016). The repetitive nature of this process builds negative mathematical dispositions (Damon, 2007; National Numeracy, 2016a), reinforcing negative attitudes towards mathematics: this is experienced by many children in the early years of their education (Scarpello, 2007). Teacher attitudes and confidence are therefore important factors to consider when discussing influences of pupil attitudes. Whilst this study originally discussed teaching methods in the literature review, it also highlighted the importance of teacher confidence (Beasley et al, 2001; Beilock et al, 2010) given the evidence to suggest that teachers confidence in the methods they teach is more important than the methods themselves (Boylan, 2019).
Technology is fully integrated within the resource. As well as graphics calcu- lators, the Maths Quest for Queensland series features computer algebra systems, spreadsheets, dynamic geometry software and several graphing packages. Not only does the text promote these technologies as learning tools, but demonstration versions of the programs (with the exception of Microsoft Excel) are also included, as well as hundreds of supporting files on the free accompanying CD-ROM.
The tests were administered by schools with some support from the Shared Maths team and other employees from the Centre for Evaluating and Monitoring (CEM). Although schools were instructed to deliver tests under ‘exam’ conditions, and they were completed on computers, the testing was not fully ‘blind’.One of the key limitations of the study is the lack of data on control group activities (‘business as usual’). Anecdotal’ evidence from the LAs suggested that some of the control schools were independently making a focused effort to improve pupils’ maths attainment during the trial using alternative strategies. Considering that these schools were waiting list controls, and therefore interested in receiving Shared Maths after the trial this may have resulted in heightened awareness of maths performance. However, there is no reliable evidence to support this observation. It should be noted that schools were selected on the basis that they needed to improve their maths performance and had the capacity to do so.
Early evaluation of NC by its developer, Edge Hill University, indicated positive e ﬀects (Edge Hill University, 2018 ). The study suggested that children made an equivalent of 17 months ’ progress in 4 months (4 times the expected progress). Teachers reported that children were showing more con ﬁdence and interest in learning mathematics in class after NC. However, gains were measured using the Sandwell Early Numeracy Test ( https://www.gl-assessment.co.uk/products/sandwell-early-numeracy-test-sent/ ), which is closely aligned with (and even practised as part of) the intervention. Crucially, the evalu- ation did not compare the progress of these children with similar children not receiving NC. In other words, there was no suitable counterfactual, so it is not clear if the children would have made the same progress if they had not had the intervention. The ﬁrst large-scale independent evaluation of NC (Torgerson et al., 2011 ), involving 522 pupils from 53 schools in England, reported a short-term impact on children ’s maths attainment, measured using the standardised Progress in Maths test (PiM), when compared to those receiving no intervention (ES = 0.33). This was based on post-test scores only, and the intervention group was already ahead at pre-test based on the Sandwell Early Numeracy Test, so it is again unclear how e ﬀective NC has been.
You have probably been told countless times that maths graduates are highly sought after by employers and that there will be a wide range of careers that you can choose from. Perhaps that was even one of your main motivations for choosing a maths degree - the prospect of a well paid, interesting and satisfying job. You may also have found yourself scratching your head, wondering what you will do, confused about what maths graduates actually end up doing.
The aim of the national network of Maths Hubs is to ensure that all schools have access to excellent maths support. They are responsible for the coordinated implementation of national projects to stimulate improvement and innovation in maths education. The role of the NCETM is to ensure that all the support provided is informed by robust evidence about what works, both in terms of maths teaching and the professional development of teachers of maths. The network brings together the emerging national leaders of maths education and aims to make innovative school-led national subject improvement a reality. This specialist support complements the more general workforce development and