Science access students: An exploration of their cognitive test anxiety

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Science access students: An exploration of their cognitive test anxiety

Yougan Aungamuthu Shaun Ramroop

[email protected] [email protected]

University of KwaZulu Natal University of KwaZulu Natal

South Africa South Africa

ABSTRACT

Test anxiety amongst Science access students is often not given the due attention

that it deserves and this can be a potentially leading factor of success or failure

amongst Science access students as they prepare for entrance to read for a degree.

The current study focuses on cognitive test anxiety which refers to the worry filled

thoughts a student encounters during or after an assessment with regards to their

performance on that assessment. Data was collected using the Cognitive Test

Anxiety (CTA) questionnaire ( and analyzed using an index based on the median

that classified the students as high or low anxiety groups. Current literature suggests

that there are a number of different variables associated with cognitive test anxiety.

Via a logistic regression model, a relationship between student's cognitive test

anxiety and five variables (gender, matric points, matric mathematics mark, lecture

group and number of modules passed by students in their mid-year university

examinations) was investigated. With respect to the CTA of Science access

students, the variables matric mathematics mark and matric points were found to be

the influential variables.

Keywords: Cognitive Test Anxiety (CTA), factors, median, high anxiety, low

anxiety, logistic regression.

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INTRODUCTION

Education is pivotal in transforming the socio-economic landscape of South Africa;

historically, a landscape shaped by the legacy of apartheid wherein inequity of

resources marginalised the education of Black learners. For South Africa to develop

and progress there is a dire need to ensure that the country produces graduates, in

particular, more Black graduates . This is a challenge given that the majority of

government schools are dysfunctional . Further, various national and international

assessments in mathematics and science suggest that well-resourced schools

outperform their under-resourced counterparts . In order to facilitate meaningful

access to higher education for students who manage to qualify for university

admission, issues associated with student transition and learning need to be

examined and understood so as to develop appropriate responses that would

enable students to maximise their learning goals and minimise dropout and failure.

As such, this paper explores the test anxiety of Science Access students; students

who come from historically disadvantaged schools and communities that were

marginalised by apartheid in South Africa. Based on matric marks in the national

matric examinations, Science Access students do not qualify for acceptance into an

undergraduate Science degree. However, Science Access students gain access to

study for a science degree through a Science Access programme run by the

university. Selection into the Science Access programme is based on students

having attended a school classified as under resourced by the Department of Basic

Education; other criteria for selection are discussed in section 3.2. Science Access

students have to pass five modules in the programme so as to be eligible for

admission to a science degree the following year. The Science Access programme

seeks to prepare students for the rigors of academia through modules in Maths,

Biology, Physics, Chemistry and Scientific Communication. For a further description

of Science Access students, refer to Downs (2010), Pillay (2009), Reynolds (2008),

and Rollnick (2010).

Aungamuthu, Ramroop: Science access students

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Based on conversations with Science Access students, the purpose of which was to

elicit reasons for poor test performances, students cited anxiety, worry, and 'going

blank' during tests, as well as reasons related to finance. The researchers in this

study therefore decided to survey and measure student levels of test anxiety and go

beyond what may be construed as anecdotal evidence. Coupled with students'

disadvantaged schooling backgrounds and the high stakes associated with

university, the researchers considered this a reasonable approach since, in most

cases, Science Access students are first generation university students who may

not be privy to the cultural capital necessary to negotiate various aspects of

university life.

LITERATURE REVIEW AND THEORETICAL FRAMEWORK

Literature Review

The literature review aims to answer three questions:

·What is cognitive test anxiety?

There are various definitions and models of test anxiety which suggest that test

anxiety is a multi-dimensional construct . Research has identified three dimensions

of test anxiety, namely, a physiological dimension; a behavioural dimension, and a

cognitive dimension . This study focuses on the cognitive dimension of test anxiety

which refers to the worry-filled thoughts that a person has prior, during, or after an

assessment with regards to their performance .

·What research evidence is there that cognitive test anxiety is

associated with academic performance?

Test anxiety is an important variable at all levels in education systems . However,

cognitive test anxiety has been repeatedly linked with decreased academic

performance . Students with high levels of cognitive anxiety tend to have negative

thoughts about their abilities thereby inhibiting their achievements . Negative worry-

type thoughts restrict students' working memory and so their ability to concentrate

Journal For Educational Studies 13 (2) 2014

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and to remember information is impeded . Other symptoms of cognitive test anxiety

are feelings of nervousness and discomfort before lectures, inability to recall

information during tests, lack of interest in a subject, and doubting one's abilities to

complete tasks; all of which inhibit academic performance .

·Who is at risk of high levels of test anxiety?

Many variables are associated with high test anxiety. While some researchers

contend that females report higher levels of cognitive test anxiety than males , other

studies have found gender to have no statistically significant effect . However, found

that low achieving males reported higher levels of cognitive test anxiety than low

achieving females, thereby indicating that gender is a contentious variable.

Besides gender, socioeconomic variables like type of school (public versus private)

and the education level of parents have been shown to affect test anxiety . In

addition student level variables like self-efficacy beliefs, self-control, test scores,

and motivation have been linked to test anxiety . In this study, test scores were

operationalised by considering students' marks obtained at school level

assessments (matric final exam marks) and university mid-year examination

performance.

While much of the research in test anxiety centres on student level variables, few

studies explore the effects that context variables like classroom environment or

teacher attributes have on the levels of student anxiety . This study examines

whether or not there is a classroom effect associated with cognitive test anxiety by

analysing the latter in relation to the lecture groups which students are randomly

assigned to on registration. This is further discussed in section 3.2 below.

Theoretical Framework: Cognitive Interference Model

The cognitive test anxiety (CTA) questionnaire (Cassady &Johnson, 2002) was

used to collect data for this study. From a theory perspective, the CTA questionnaire

is based on the cognitive interference model of cognitive test anxiety (Cassady &


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Johnson, 2002). Within such a perspective, people with high levels of test anxiety

cannot control intrusive thoughts during a test; thoughts which distract them from

appropriate tests prompts thereby preventing meaningful concentration on test

questions .

METHODOLOGY

Research question

This study stemmed from conversations with Science Access students and their

tutors; conversations regarding poor academic performance in mathematics. During

student consultation times, underperforming students would report that they felt

nervous about tests, in some cases forgetting their work in test situations, and

generally feeling worried about their performance. Thus, as an intervention strategy,

the researchers in this study explored the extent to which test anxiety impacts on

student academic performance. One of the researchers in the study is also the

module coordinator for the Foundation Mathematics module; part of the

coordinator's responsibility being to monitor student performance and make

appropriate interventions. The following research questions are answered in this

study:

·How is cognitive test anxiety distributed among Science Access students?

This question was statistically explored in four ways. Firstly, to find out if

there was a relationship between cognitive test anxiety and student matric

exam performance. Students' matric points and matric mathematics marks

were used as indicators of performance in matric. Secondly, the existence of

a link between gender and cognitive test anxiety was investigated. Thirdly,

the classroom environment was examined by looking for differences in the

distribution of cognitive test anxiety based on lecture group membership.

Lastly, student performance in mid-year university examinations was

analysed against student cognitive test anxiety. This was done by looking at

the number of modules a student passed in the exam in relation to their levels

of cognitive test anxiety.


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·Are there differences in examination performance between the low and high

cognitive test anxiety groups? This question was answered by looking at

students' mid-year exam marks for each module and testing for differences in

performance based on cognitive test anxiety status (test anxiety status is

further described in Section 3.3 below).

·What factors are associated with cognitive test anxiety? This question was

answered by modelling the relationship between cognitive test anxiety and

the five independent variables described below.

Characteristics of the respondents based on independent variables

Within the Foundation Programme stream of the Science Access programme at the

University of KwaZulu-Natal (UKZN), Foundation Mathematics is offered as one of

five compulsory modules making up the Foundation Programme. For this study all

registered students in the Foundation Programme on the Pietermaritzburg campus

of UKZN were targeted as respondents. However, seven students were absent on

the day that data collection took place, thus reducing the number of respondents to

eighty five. Lecturers of the Foundation Maths module explained to students that

participation was voluntary and students could withdraw from participation at any

time. Further, the aims of the research were discussed with students. Lecturers

emphasised that the information collected from the survey was not going to be used

towards students' marks. Forty five respondents were female and forty were male;

gender was one of the independent variables used in the analysis.

Matric points were the second independent variable used. Matric is the name given

to the twelfth year of schooling in the South African basic education system. Students

sit for exams towards the end of their matric year and the marks obtained for these

exams are used as criteria for entrance to tertiary institutions. For example, at UKZN

points are awarded on six matric subjects (excluding the matric subject Life

Orientation) as follows:


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·8 points for each matric subject mark in the range 90% to100%;

·7 points for each matric subject mark in the range 80% to 89%;

·6 points for each matric subject mark in the range 70% to 79%; and so on

down to

·1 point for a mark in the range 0% to 29%.

For admission into the Foundation Programme, students require at least sixteen

matric points (excluding points for Life Orientation), and at least a 30% mark in matric

mathematics and one other science subject at matric level. 80% of the sample had

matric points in the range 22 to 28; 3.5% had points less than 22; 7% had points

greater than 28; and the matric points of eight students or 9.4% of the sample were

not available on the university administration system.

The matric maths mark of the sample, a third independent variable, was distributed

as follows: 31.7% of the sample scored below 40%; 25.9% of the sample scored in

the range 40% to 49%; 25.9% of the sample scored in the range 50% to 67%; and the

remaining 16.5% of the sample's marks were not available on the university student

management system. The mean matric maths mark was 43.5%, the median was

42%, the lowest mark was 19%, and the highest mark was 67%.

A fourth independent variable was the lecture group to which participants belonged.

This was used to explore whether or not the classroom environment was associated

with cognitive test anxiety. Forty three students belonged to lecture group one and

forty two students belonged to lecture group two. At the beginning of the academic

year, students are randomly allocated to lecture groups by the UKZN administration.

Students attend all academic-related activities in their respective groups.

Lastly, a fifth independent variable considered in this study was the number of

modules passed by students in the mid-year university examinations. Students sit

for examinations in four of their five modules; in one module, Scientific


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Communication, no exam is written but there is continuous assessment. The

number of modules passed variable is an indicator of academic risk i.e. the more

modules passed, the less risk of academic failure and university drop-out. 53% of the

sample passed all five modules in the Foundation programme; 22% passed four

modules; 11% passed three modules, and 11% also passed two modules, whilst the

remainder of the sample passed one module. Underlying this variable was the mark

scored by each student in each of the five modules; this was used to probe for any

relationships between cognitive test anxiety and individual modules.

In summary, with the exception of the variable lecture group, all independent

variables used in this study were student level variables. With the exception of the

variable gender, all student level variables were included as indicators of student

academic ability. However, owing to the disadvantaged nature of Science Access

students' schooling, we recognise this assumption as a bias in our study.

Research instrument

A cognitive test anxiety (CTA) survey was administered to students during one of

their mathematics lectures in the second semester of 2013. The survey was

administered on the day that students were writing a test in Biology which was also

five days before their Mathematics test; this was done to ascertain students'

thoughts and feelings within a test taking scenario .

With regards to validity and reliability, the CTA survey was pilot tested and exhibits

internal consistency of : a=0.91 which has been shown to correlate with other

anxiety instruments . state that there are a number of different reliability coefficients.

One of the most commonly used is Cronbach's alpha, which is based on the average

correlation of items within a test if the items are standardised. If the items are not

standardised, it is based on the average covariance among the items. Cronbach's

alpha which can range from 0 to 1 was also calculated as part of the reliability test to


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assess how consistent the results were and whether we would get similar results to

generalise if we increased the sample size. A value of 0.7 or higher is a very good

value that can lead to the assumption that the same results would be obtained if the

survey was carried out with a larger sample of respondents. In this study, Cronbach's

alpha equalled 0.838 indicating good internal consistency. Further, the Guttmann

Split Half coefficient was 0.781 which further indicated good internal consistency.

The items in the survey fell into three categories related to test taking and

preparation periods :

·The following are examples of items which covered the first category, namely

comparing self to other students:

While taking an important examination, I find myself wondering

whether the other students are doing better than I am.

I have less difficulty than the average college student in getting test

instructions straight

·The second category, namely engaging in thinking irrelevant to the test or

test preparation, contained the following items:

During tests, I find myself thinking of the consequences of failing.

During tests, the thought frequently occurs to me that I may not be

too bright.

·The third category, namely having thoughts grounded in worry or missing

cues in test questions, had items like:

I lose sleep over worrying about examinations.

At the beginning of a test, I am so nervous that I often can't think

straight.

The CTA survey had items which had to be reverse-coded for high anxiety; these

were item numbers 3, 5, 8, 9, 10, 13, 17, 18 and 21. In other words, for the

aforementioned items, a score of 1, 2, 3 or 4 would be reverse-coded to 4, 3, 2 or 1

respectively.


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Once reverse-coding was completed, a cognitive test anxiety score for each

participant was calculated by summing up their responses across each of the twenty

seven Likert scale survey items; each sum represented the cognitive test anxiety

score of a participant. Participants were then ranked in ascending order, based on

their cognitive test anxiety scores. The median CTA score was calculated; scores

greater than the median were classified as high anxiety while scores less than the

median were classified as low anxiety. A score of at least 72 on the CTA survey was

considered to reflect high anxiety ; the median CTA score in this study fell in that

interval.

Data analysis

Descriptive and inferential statistical techniques were used to analyse the survey

data. These techniques are described below. Gender, matric points, matric maths,

and lecture groupings were used as independent variables; cognitive test anxiety, as

scored on the CTA survey, was used as the dependent variable.

Descriptive and inferential statistics

We considered cross-tabulations and chi-square tests of independence between

the nominal variables (gender, group, and number of modules passed) and anxiety

status. state that there are two main types of chi-square test.The chi-square test for

the goodness of fit applies to the analysis of a single categorical variable, and the

chi-square test for independence or relatedness applies to the analysis of the

relationship between two categorical variables.

We also considered the mean and median of the matric score and matric

mathematics mark broken down by the anxiety status, along with the independent

sample t-test. Before applying the independent sample t-test, we performed the

Kolmogorov Smirnov test to ascertain whether the variables follow a normal

distribution; if normality of the data holds true then the use of a parametric test such

as the independent sample t-test is permissible. At the 5% significance level, we


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rejected the null hypothesis if p-values were less than 0.05 and conclude that the

tested variables do not come from a normal distribution. The implication for this is

that as far as the scores were concerned, we were required to use non-parametric

statistics. Tests such as the Mann-Whitney U test, chi-square, and Kruskal Wallis

test will be used if necessary. However for p-values are greater than 0.05 we will

accept the null hypothesis and we conclude that these variables come from a

normal distribution and we can use parametric techniques such as independent

sample t-test.

Logistic regression

According to the goal of any model building technique is to find the best fitting and

most parsimonious model to describe the relationship between an outcome

(dependent or response) and a set of independent (predictor, explanatory, or

covariate) variables. In the general linear model of which multiple regression is a part

of, we make the crucial assumption that the response and explanatory variables are

all continuous. But what if the explanatory and response variable are not

continuous? One would then usually make use of dummy variables to accommodate

explanatory variables that are categorical, but this can become cumbersome and

tedious. As a result the family of the general linear model was extended to the

generalised linear models to accommodate categorical explanatory variables as

well as continuous and non- continuous explanatory variables. points out that such

a model is part of an increasingly important family of models being used in recent

advancements in the areas of genetics, business, and medicine, just to mention a

few. Logistic regression is a special case of the generalised linear model where the

outcome variable is binary or dichotomous. In the current scientific setting the

outcome variable, y, is the anxiety level which is classified as either high anxiety or

low anxiety.

We now consider the logistic regression or the logit model. states that the logit

model is popular because the coefficients have a simple interpretation in terms of the


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odds ratios, it has desirable sampling properties, and can be easily generalised to

allow for multiple unordered categories for the dependent variable. The logistic

regression uses the logit transformation of the probability, ð, of the event of interest

which is high anxiety, given as:

In the above equation, the âs are regression coefficients, and Xs are a set of

predictors along with the intercept term, â . The âs are typically estimated by the 0

maximum likelihood (ML) method, which is preferred over the weighted least

squares approach. Statistically the Hosmer-Lemeshow test is accepted as being a

test for the goodness of fit between the model and the data. The interpretation of this

test is such that if the p-value in the test is non-significant at the 5% level, this

indicates a good fit of the model to the data; if the p-value is significant, then the

model does not fit the data well.

Thus when we apply the logistic model to our data we have:

logit(ð)=â + â *no. of module passed+ â *matric score + â *matric mathematics 0 1 2 3

mark+ â *Gender+ â *group4 5

describes the odds of an event as the ratio of the expected number of times that an

event will occur to the expected number of times it will not occur. The interpretation of

the results is rendered using the odds ratio, OR. If â is the logistic regression

coefficient estimate for predictor X, then exp(â) is the OR corresponding to X. The

complementary interpretation is the sign of the coefficients: the positive coefficient

shows the increase of the chances for the given category of X relative to its reference

category, and the negative coefficients shows the decrease.

kk XXX bbbbp

pp ++++=÷

ø

öçè

æ

-= ...

1ln)(logit 22110


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parsimonious model. The procedure of stepwise logistic regression involves starting

with a single explanatory variable and then sequentially adding explanatory

variables one at a time in order to assess their impact and influence on the model.

This is done by noting changes in the values of the log-likelihood and noting if its p-

value is less than 0.05. Explanatory variables that are not statistically significant are

excluded from the model whilst the explanatory variables that are significant in their

influence with respect to the responses variable, are retained in the model .

Limitations

Firstly, the survey instrument used was not conducted in the mother tongue of the

students; the instrument was in the language of instruction, namely English.

Language has been shown to affect student performance . Respondents took

between thirty and forty minutes to complete the twenty seven item survey; two to

three times longer than the maximum time suggested for completion by Cassady

and Johnson (2002). The maths lecturer on duty assisted students with

understanding difficult words and questions so as to moderate the effects of the

language.

Secondly, the small sample size is a limitation in this study; this is partly due to

resource constraints. A larger sample size consisting of a greater number of

students across the various campuses of UKZN might strengthen the results of this

study. However, it must be noted that this study was exploratory in nature.

Thirdly, the measurement of several covariates such as race, financial background,

teacher variables, and perseverance levels, may allow for a greater degree of

explanatory power in the model developed in this study.

Finally, this study is based in UKZN; a broader study that extends to additional

universities in the other provinces might provide better estimates of the levels of

anxiety amongst students for a wider regional context. The results of the current

study are limited to UKZN and cannot be generalised to the rest of South Africa.

We carried out a stepwise logistic regression in order to find the best fitting and


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RESULTS

How is cognitive test anxiety distributed among Science Access students?

This question was answered by examining cognitive test anxiety levels against each

of the independent variables described in the methodology section. As described in

Section 3.3 above, the median was used as a cut-off point to classify student anxiety

status. The median cognitive test anxiety score was 75; three units higher than the

lower bound suggested by Cassady (2004) for high anxiety.

Table 1a below revealed, with regards to matric points, that the high anxiety group

scored higher on average than the low anxiety group. The difference in the mean

matric point was 0.449 units suggestive of not a vast difference. This was confirmed

by an independent sample t-test which revealed that there was no significant

difference in the mean matric points between high anxiety and low anxiety groups: t

(75) = 0.769, p >0.05.

Further, with respect to the students' matric mathematics, the low anxiety group

scored higher on average than the high anxiety group. This difference was

statistically significant: t (69) = -4.787, p < 0.05. The Kolmogorov Smirnov test

revealed that the variables being tested followed the normal distribution which

meant that the independent sample t-test was appropriate (see Table 1b).

TABLE 1a: DESCRIPTIVE STATISTICS

Anxiety status Statistic Std. Error

Matric points

Low anxiety

group

Mean

25.351 .4012

Median

26.000

High anxiety

group

Mean

25.800 .4209

Median

26.000

Matric

mathematics

Low anxiety

group

Mean 48.800 1.621

Median 50.000

High anxiety

group

Mean 38.250 1.496

Median 37.000


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TABLE 1b: RESULTS OF THE KOLMOGOROV SMIRNOV TEST

Kolmogorov-Smirnov Z

Asymp. Sig. (2-tailed)

Matric points

1.199

.113

Matric mathematics

mark

.712 .691

The cross tabulation in Table 2 below reveals that there were less females (20%)

with low anxiety than females with high anxiety (32.9%). A substantially higher

percentage of males (30.6%) had low anxiety whilst 16.5% had high anxiety. The

cross tabulation thus reveals that with respect to low anxiety, males (30.6%) were

less anxious than females (20%) whilst with respect to high anxiety, more females

(23.9%) had high anxiety than males (16.5%). The chi-square test statistic was

6.278 (1 degree of freedom) and a p-value=0.012 which is significant at the 5% level

showing a significant relationship between gender and anxiety status.

TABLE 2: GENDER BY ANXIETY STATUS CROSSTABULATION

Anxiety status

Total

Low

anxiety

group

High anxiety

group

GenderFemale

20.0%

32.9%

52.9%

Male 30.6% 16.5% 47.1%

Total 50.6% 49.4% 100.0%


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The cross-tabulation in Table 3 below shows that in lecture group 1 there was a

higher percentage of students with high anxiety (27.1 %) than students with low

anxiety (23.5 %). In lecture group 2 more students had low anxiety (27.1%) than high

anxiety (22.4%). The chi-square test statistic was 0.579 (1 degree of freedom) with a

p-value=0.447 which is not significant at the 5% level showing a non-significant

relationship between the classes/groups and anxiety status.

TABLE 3: LECTURE GROUP BY ANXIETY STATUS

Anxiety status Total

Low anxiety group High anxiety group

Lecture

Group

1

23.5%

27.1%

50.6%

2

27.1%

22.4%

49.4%

Total 50.6%

49.4%

100.0%

The results of the cross-tabulation in Table 4 below show that as the number of

modules passed increased, so did the percentage of low anxiety increase i.e. of the

students who passed 1 module, only 2.4% had low anxiety while in the sample of

students who passed 5 modules, 32.9% had low anxiety. We also see that in the

group of students who passed 2 and 3 modules, 8.2% and 7.1% respectively had

high anxiety, and in the group of students who passed 5 modules 20% had high

anxiety. The chi-square test statistic was 9.930(5 degree of freedom) with a p-

value=0.052 which is non-significant at the 5% level showing a non-significant

relationship between the number of modules passed and anxiety status.


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TABLE 4: NUMBER OF MODULES PASSED BY ANXIETY STATUS

Anxiety status Total

Low

anxiety

group

High

anxiety

group

No.of modules

passed

.00

1.2%

1.2%

1.00

2.4%

2.4%

2.00

2.4%

8.2%

10.6%

3.00

3.5%

7.1%

10.6%

4.00 9.4% 12.9% 22.4%

5.00 32.9% 20.0% 52.9%

Total 50.6% 49.4% 100.0%

The almost significant result between anxiety status and number of modules passed

prompted the researchers to investigate the relationship between cognitive test

anxiety and each of the modules students are assessed on. This is discussed under

the next research question.

Is there a difference in examination performance between the low cognitive

test anxiety group and the high cognitive test anxiety group?

Table 5 indicates that students from the low anxiety test group scored higher, on

average, than their counterparts from the high anxiety test group.

TABLE 5: Descriptive statistics of exam modules by anxiety status

Module

Anxiety status

N Mean

Std. Error Mean

Chemistry

High anxiety group

42 53.833

1.619

Low anxiety group

43 58.395

1.395

Maths

High anxiety group

42 51.905

1.997

Low anxiety group

43 64.930

1.773

PhysicsHigh anxiety group 42 50.429 1.339

Low anxiety group 43 56.023 1.514

BiologyHigh anxiety group 42 66.643 1.271


Scientific

Writing

High anxiety group 42 54.357 .8070



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The results of the Komogorov Smirnov test for normality are illustrated in Table 6.

The Komogorov Smirnov test revealed that the variables being tested followed the

normal distribution which meant that the independent sample t-test could be applied

to the data.

TABLE 6: RESULTS OF THE KOLMOGOROV SMIRNOV TEST

Module

Kolmogorov-

Smirnov Z

Asymp. Sig. (2 -

tailed)

Chemistry .870

.435

Maths

.743

.638

Physics

.863

.445

Biology

1.007

.263

Scientific

Writing

1.239

.093

Table 7 below summarises the results of the t-tests. At the 5% significant level we

find that there are differences in the mean score of the two anxiety groups with

respect to Chemistry, Mathematics, and Physics (p-value < 0.05), and no

differences in the mean score with respect to Biology and Scientific Writing (p-

value > 0.05).


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Module

Levene's Test for

Equality of Variances

F

Sig.

t

df

Sig. (2-

tailed)

Chemistry

Equal variances

assumed

.007

.931

- 2.138

83

.035

Equal variances not

assumed

- 2.134

80.931

.036

Maths

Equal variances

assumed

.473

.494

- 4.883

83

.000

Equal variances not

assumed

- 4.877 81.610 .000

Physics

Equal variances

assumed

.501 .481 - 2.763 83 .007

Equal variances not

assumed

- 2.767

82.010

.007

Biology

Equal variances

assumed

.880

. 351

- 1.007

83

.317

Equal variances not

assumed

- 1.008

82.306

.316

Scientific

Writing

Equal variances

assumed

3.414

.068

- .910

83

.366

Equal variances not

assumed

- .913 76.562 .364

TABLE 7: Results of the independent sample t-test


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What factors are associated with cognitive test anxiety?

The results, see Table 6 and 7 below, reveal that the matric points and the matric

mathematics mark are significant at the 5% level and are associated with high

anxiety. Since these variables are continuous and not categorical there is no

comparison with any levels within the variable and hence the odds ratios are not

interpreted. The matric points and matric mathematics mark are also critical in

preparing students to handle the modules and pressures of tertiary education. The

Hosmer-Lemeshow test was 3.135 (8 degrees of freedom) with a non-significant p-

value of 0.926, implying that the data fits the model well. The critical findings of the

logistic regression show that the matric points and the matric mathematics mark are

key factors associated with student level of anxiety. The variables of gender, group,

and number of modules passed were found not to be influential in the model and

were excluded by the stepwise logistic regression. Several interactions between the

explanatory variables were assessed but none of them were found to be significant

and are thus not present in the model.

Table 6: Logistics regression results

Variable B S.E. Wald Sig. Exp(B)

95% C.I.for

EXP(B)

Matric points .267

.125

4.574

.032*

1.306

1.023

1.668

Matric

mathematics

mark

-.144

.037 14.882

.000*

.865

.804

.931

Constant -.489 2.828 .030 .863 .614

*-significant at the 5% level


132

TABLE 7: Results of the changes in the log-likelihood if the variables are remove

Variable Model Log

Likelihood

Change in -2

Log Likelihood

df Sig. of the

Change

Step 1Matric

mathematic mark

-47.139

18.394

1

.000

Step 2

Matric points

-37.995

5.400

1

.020

Matric

mathematic mark

-46.784

22.978

1

.000

DISCUSSION

The first research question examined the association between cognitive test anxiety

and each of the independent variables (matric points, matric maths, gender, lecture

group, and number of modules passed in the June university examinations). An

independent sample t-test revealed that there was no significant difference in the

mean matric points between the high anxiety and low anxiety groups; however there

was a significant difference in the mean matric maths score between the two groups.

A chi-square test of independence found gender to be significantly associated with

cognitive test anxiety but lecture group and number of modules passed in the June

exam were not significantly related to cognitive test anxiety. This finding suggests

that some student level variables are associated with cognitive test anxiety,

specifically matric maths and gender.

The second research question investigated whether or not academic performance

in the June university examinations differed between the two cognitive test anxiety

groups. Specifically, were there any modules in which the low anxiety group

outperformed their high anxiety counterparts? An independent sample t-test

showed significant difference in mean performance between the two groups in

Chemistry, Mathematics and Physics; no significant differences were found in

Biology or Scientific Communication modules. This suggests that some content or


133

learning areas are more likely to evoke cognitive test anxiety than other learning

areas.

The third research question examined the factors that contribute to cognitive test

anxiety. A stepwise logistic regression model was applied to the data and revealed

matric maths and matric points to be significant predictors of cognitive test anxiety.

Gender, lecture group, and number of modules passed were not significant

predictors in the fitted model. This suggests that students' matric experience is

related to cognitive test anxiety. If we assume matric to be the capstone of student

school experience then this study indicates that the knowledge and learning gained

at school level is associated with student cognitive test anxiety levels. This would

underscore the importance of having access to good quality primary and secondary

school education.

The implications of this study for higher education is that modules which are

mathematical in nature or that require applications of mathematics concepts and

algorithms need to provide support mechanisms to help students manage their

cognitive test anxiety. For example, students may benefit from an emotional

intelligence workshop where anxiety coping strategies can be taught . Counselling

and support groups, where coping strategies are discussed and practised, need to

be provided for highly anxious students . Students may also benefit from study skills

workshops which teach them how to organise their study time . In addition, students

with low matric points or matric maths marks need to be supported with appropriate

academic and psychosocial interventions.

From a methodological perspective, our findings show:

·Depending on the statistical test used, a variable may be significant or not.

For example, in this study the variable gender was statistically significant

when the chi-square test was used. However, it was not significant in the


134

logistic regression model. This might explain the discrepancy with regards

to the relationship between gender and cognitive test anxiety, as alluded to

in the literature review. Similarly, the variable 'matric points' was significant

in the logistic regression model but not significant in the independent

sample t-test.

·Statistical significance can be influenced by the way in which a variable is

operationalised. For example, in this study, one indicator of student

performance was the variable 'number of modules passed' which was

found not to be significantly associated with cognitive test anxiety.

However, other variables such as 'matric maths' and some of the individual

module exam marks which are also indicators of student performance,

were found to be statistically significant.

CONCLUSION AND RECOMMENDATIONS

This study examined the cognitive test anxiety (CTA) of Science Access students;

students who are from historically marginalised communities as a result of

apartheid. During conversations with students and demonstrators during tutorials,

lectures, and consultation sessions, students spoke of 'going blank' during tests and

simply feeling anxious about a test as reasons for their poor performance. The CTA

survey was administered to students and statistically analysed to explore the

extent of students' claims regarding anxiety in relation to their academic

performance.

The analysis revealed significant differences in performance between high and low

anxiety groups of students with regard to their matric mathematics marks and

gender. Statistical difference in performance between the two groups was also

found in modules that had mathematical content or that required students to apply

mathematical ideas. In addition, stepwise logistic regression revealed that students'

matric mathematics marks and matric points were associated with CTA thereby

emphasising the importance of students having access to good quality schooling.


135

The implications for higher education are that mechanisms which support students

in their management of cognitive test anxieties should be identified and provided,

especially to students who study modules with mathematical content and students

with low matric points or low marks in matric maths.

Based on the results of this study, recommendations for further research are:

·Researching techniques and support mechanisms that help students to

cope with and manage their cognitive test anxieties.

·Identifying sources of cognitive test anxiety. For example variables related

to the classroom environment, teacher attributes, and the wider university

environment need to be statistically modelled. Such information can be used

to help higher education model and manage risk behaviours such as drop-

out rates and failure.

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Science access students: An exploration of their cognitive test anxiety