1c assignment

Jane Chapman PGCE Secondary

A CRITIAL ANALYSIS OF THE MATCH OR MISMATCH BETWEEN THE

PERCEIVED AND ACTUAL UNDERSTANDING OF YEAR 7 STUDENTS, STUDYING

PARTICLE SOLUTIONS, WHEN ENGAGING IN SELF-ASSESSMENT LEARNING

ACTIVITIES

Introduction

This study aims to investigate the match or mismatch between the perceived and

actual understanding of students, when engaging in self-assessment. Educators frequently use

self-assessment to assess student understanding in order to identify and respond to their

needs. However, it is important to question how well students know what they know. It has

been illustrated that people often make self-assessment errors, and when they do, they are

often overconfident. An issue which needs to be addressed with this research is how best we

can measure student understanding. In this study, students will be investigated individually,

based on tests, confidence scorings and questionnaires. Interviews will further be used to

examine the effect of self-efficacy on student understanding. The context of this enquiry is a

coeducational school in Cambridgeshire, England. The chosen class is a high-achieving

cohort of 29 students in a Science class. This paper will first give a background on the

previous research done on this topic and then explain the methodological approach taken to

address the current research questions. Later, findings will be analysed and finally,

conclusions will be made, with implications of this study.

1. Literature review

1.1. Self-Assessment for Learning

Effective assessment by schools and teachers not only needs to measure student progress,

but also identify their learning needs and respond to them. Forms of ‘summative’

assessment, such as tests and examinations are a classic way to measure student progress, in

addition to making schools and the education system accountable (Ball, 2003). However, to

be truly effective, there must also be ‘formative’ assessment. In classrooms, this includes the

teacher making frequent and interactive assessments of the students’ understanding in order

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to identify and respond to their needs. This informs future teaching as it allows the teacher to

adapt to the changing needs of the students. Teachers should also involve students actively,

encouraging them to develop skills that better aid their learning.

Formative assessment is known to be highly effective in raising the standards of student

achievement, gains which are ‘among the largest ever reported for educational interventions’

(Black & Wiliam, 1998). In addition, formative assessment methods may also promote

‘greater equity of student outcomes’, as teachers ‘adjust methods to recognise individual,

cultural, and linguistic differences between children’ (CERI, 2008). Furthermore, this type of

assessment also builds students’ ‘learning how to learn’ skills by actively involving students

in the process of teaching and learning, helping them understand their own learning and

building students’ skills for peer- and self-assessment. In primary and secondary schooling,

self-assessment has been shown to improve student communication skills, engage and

empower students, enhance their self-regulation and metacognition, and create better

understandings of the criteria used to evaluate students’ work (Andrade, 2010; Topping,

2003).

Reliability of self-assessments is typically high, demonstrated by a study of 11-12

year old students rating their performance in mathematical problem solving (Ross et al.,

2002) and self-assessments in English (Ross et al., 1999). However, there has been shown to

be less consistency over longer time periods, particularly involving younger children

(Blatchford, 1997). Evidence about the concurrent validity of self-assessments is mixed. In

general, student self-assessments are higher than teacher ratings. Furthermore, a study

comparing student self-assessment to standardised tests found that age moderated the

relationship. Self-assessment was correlated with achievement at age 16 but not at age 7

(Blatchford, 1997). It should be taken into consideration that any form of self-assessment that

takes place in a public space may trigger threats to psychological safety and interpersonal

relationships (Brown & Harris, 2013). Furthermore, many students have doubts about their

ability to assess themselves (Brown et al., 2009) and there is evidence to suggest that school

students are relatively inaccurate assessors (Ross, 2006). One study of 23 Canadian primary

and secondary classrooms found that although students appreciated self-assessment, there

were concerns over possible cheating and inaccuracy (Ross et al., 1998). Additionally, a New

Zealand study of self-assessment reported that students preferred more traditional teacher-

controlled assessments, a belief reinforced by school grading and reporting methods. The

same study also proposed that students in high-stakes environments for educational

assessments such as the UK Key Stage testing may be more likely to resist self-assessment

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because their assessment experiences have not allowed them to appraise their own

evaluations of their work. This is highlighted by the student response: “my teacher’s

judgement matters more than mine” (Brown & Harris, 2013). To improve the accuracy of

self-assessment and improve student confidence in their evaluations, school students need

support, direction and teacher involvement for self-assessment to work effectively (Dunning

et al., 2004).

One commonly used self-assessment practice is the Traffic Light technique, developed

out of the King’s-Medway-Oxfordshire Formative Assessment Project in England. This

popular method involves students holding up a green, amber or red sign to highlight whether

they understand, think they understand but are not quite sure, or do not understand a certain

concept. Teachers would then spend more time with students who held up amber or red

(OECD, 2005). This ‘assessment for learning’ technique can also be used by students to label

their work, indicating how confident they are of their success. However, it is important to

question how well students know what they know. It has been illustrated that people often

make self-assessment errors, and when they do, they are frequently overconfident. For

example, in Hacker et al. (2000), many students predicted they would receive examination

scores greater than 30% higher than their actual scores. This overconfidence effect was

greatest for people with lower abilities. Moreover, the same study reports that higher-scoring

students were more accurate at predicting their examination scores than lower-scoring

students. The reason for this metacognitive inaccuracy is debated. The leading interpretation

is that lower ability students lack awareness of the knowledge that they do and do not possess

(Ehrlinger, 2008). However, a study testing this theory found that low-performing students

were less subjectively confident in their predictions than high-performing students, implying

low-performers are aware of their ineptitude. This literature demonstrates dissociation

between metacognitive ability and awareness of this ability (Miller & Geraci, 2011).

1.2. Self-efficacy

Self-efficacy is one of the essential components of Bandura’s (1977) social cognitive

theory. He identified that behaviour could be affected by self-efficacy theory – the belief that

a person can successfully do whatever is required to achieve a desired outcome. Key factors

which affect a person’s efficacy expectations are; vicarious experiences (seeing other people

doing something successfully), verbal persuasion (being told that you can do something) and

emotional arousal (high levels of anxiety can reduce a person’s self-efficacy). Furthermore,

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contextual factors such as social, situational and temporal circumstances might also affect

expectations of personal efficacy (Weiner, 1972). In the past several decades, studies have

shown that students’ motivation, cognition and actual performance are strongly influenced by

self-efficacy (Sungur, 2007; Usher & Pajares, 2006). In general, students with higher levels

of self-efficacy have been found to set higher goals, adopt flexible and varied learning

strategies, exert greater effort to complete academic tasks and obtain better academic

performance levels (Liem et al., 2008). In contrast, students with low self-efficacy tend to

avoid tasks they deem to be beyond their capabilities (Lin & Tsai, 2013). In the past, when

determining the relationship between self-belief and outcome, there has often been an

incorrect judgement of self-efficacy (Zimmermann, 1996). This has been due to self-efficacy

beliefs not being assessed at the correct level of specificity that corresponds to the specific

task being studied.

General self-efficacy assessments are thought to transform beliefs of self-efficacy into an

indiscriminate personality trait instead of the context-specific judgement Bandura suggests

they are. Bandura (1986) proposed that judgements of self-efficacy should be consistent with

the domain of task being investigated. An example of this would be a mathematics self-

efficacy instrument used to investigate the confidence students had of succeeding in

mathematics courses and comparing this to their performance in maths-related tasks (Pajares,

1996). Furthermore, students with higher self-efficacy often report higher levels of self-

knowledge judgement than students with lower self-efficacy (Gravill et al., 2002) and

students who believe in their learning efficacy develop and sustain their effort needed for

learning. Therefore, self-efficacy contributes to knowledge acquisition and skill development

(Tsai et al., 2011).

Several studies have examined the more specific self-perceived competence in science

education. Evidence highlights that students who feel more efficacious in science

demonstrate higher achievement in this subject (Borman & Overman, 2004). Bandura’s

theory would suggest that this may be due to student persistence, even when tasks are

difficult (Bandura, 1997). The literature also implies that student academic anxiety (Britner &

Pajares, 2006) and gender (i.e. being a girl; Fast et al., 2010) also contribute to students

having a lower science self-efficacy. Interestingly however, another study found no gender

differences in their science self-efficacy (Griggs, et al., 2013). One explanation for these

different findings may be because the latter study controlled for science anxiety, which was

greater among girls. Therefore, once anxiety was controlled, both genders believed

themselves to be similarly efficacious. Student experiences at school also play a role,

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demonstrated by enhanced student self-efficacy through a focus on creating caring,

emotionally supportive learning environments (Zins & Elias, 2006). As mentioned

previously, the judgement of self-efficacy on predicting performance is shown to be

discipline- and situation - specific. However, situational conditions do not establish perceived

self-efficacy, but rather act as performance requirements for the judgement of efficacy

(Bandura, 1997). Research has previously revealed the role of self-efficacy in science

learning and has suggested that self-efficacy mediates people’s interpretation of their

knowledge (Liu et al., 2006).

1.3. Students’ Science Learning Self-Efficacy

As mentioned previously, when considering studies that aim to explore students’ self-

efficacy, the use of an instrument with general self-efficacy items would be insufficient

(Pajares, 1996). It would be more appropriate to instead develop measures that can be

adapted to several contexts. In the case of students’ self-efficacy in science, it should not be

thought of as one global measurement, but should be separated into several distinctive aspects

for more detailed study (Lin & Tsai, 2013). Science education literature has established that

there are several major aspects of science learning. Duschl (2008) has highlighted that

conceptual understanding of scientific knowledge, together with higher-order thinking skills

such as reasoning and critical thinking are of great importance.

The development of conceptual understanding and critical thinking, together with

problem solving ability has been suggested to be promoted by practical work. Furthermore,

practical work has also been proposed to stimulate and maintain students’ interest, attitude,

satisfaction, open-mindedness and curiosity in science, promote aspects of scientific thinking

and the scientific method and also allow students to develop practical abilities (Hofstein,

1988). These all contribute in helping students learn science, learn about science and allow

them to do science (Tsai, 2003). Based on the PISA 2006 survey (OECD, 2007a, b), Finnish

students obtained the highest score in the Scientific Literacy Assessment between students in

all OECD countries. A large-scale study looking at how they succeeded, found that a robust

predictor of the high results in Finland was frequent use of practical work in the classroom

(Lavonen & Laaksonen, 2009). It is also important for students to be literate in science,

meaning ‘to use scientific knowledge, to identify questions and to draw evidence-based

conclusions in order to understand and help make decisions about the natural world’ (OECD,

1999). There are many reasons why everyday science applications should be integrated into

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school science. Firstly, empirical studies have shown that using everyday contexts enhances

student enjoyment (Dlamini, et al., 1996), allows for conceptual development, provides

teachers with an opportunity to address misconceptions (Lubben et al, 1996) and gives

relevance to school science learning (Campbell et al, 1994). Furthermore, incorporating every

day science applications into school science is fundamental to the students’ mastery of

science learning in school (Driver et al., 1994). However, many students view ‘school science

as having little or no relevance to their life-world subcultures’ (Aikenhead, 1996). When

there is no bridging between school science learning and daily experiences, students may

practice ‘cognitive apartheid’, referring to the isolation of knowledge systems relating to

science: one for school science and one for everyday lives (Cobern, 1994). Learning, like

doing science, is a social activity that takes place ‘through communication or interaction with

others where ideas are constructed or shared’ (Vygotsky, 1978). Communication through

discussion, argumentation, reading and writing can promote students’ constructs of

understanding science (Chang et al, 2011) with studies revealing the importance of students’

interpersonal communication with adults and peers on improved learning (Stamovlasis et al,

2005).

As discussed above, there are various features of science literacy and there have been

several successful studies measuring students’ science learning self-efficacy (SLSE) in

conformity with these features (e.g. Baldwin et al, 1999; Lin & Tsai, 2013; Uzuntiryaki &

Capa Aydin, 2009. In the research by Lin and Tsai (2013), several current SLSE instruments

were collected and modified to develop their own validated ‘Science Learning Self-Efficacy’

(SLSE) instrument. This consisted of five distinct domains (‘Conceptual Understanding’,

‘Higher-Order Cognitive Skills’, ‘Practical Work’, ‘Everyday Application’ and ‘Science

Communication’) that conform to the existing notion of science literacy. Furthermore, this

study also investigated the relationship between high school students’ SLSE and their

approaches to learning science. Through correlation analyses, they found that students’ deep

strategies and deep motive were strong predictors of their SLSE. This SLSE instrument has

also been useful in a later cross-cultural study (Lin et al., 2013), and in another, revealing a

significant association between students’ conceptions of learning science and their self-

efficacy (Lin & Tsai, 2013). This study found that students in strong agreement with learning

science as understanding and seeing in a new way are likely to possess a higher science self-

efficacy than students who consider learning science in terms of preparing for tests and

examinations. These studies indicate that this multi-dimensional SLSE instrument is relevant

and valid for advancing current understandings in the line of SLSE research.

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Science educators have explored the relationships between student science learning self-

efficacy and both their conceptions of learning science and approaches to learning science.

However, there does not seem to be any studies exploring student science learning self-

efficacy and their matched or mismatched actual and perceived understanding. The main

purposes of this study were first, to explore the match or mismatch between the perceived and

actual understanding of high school students studying Science. Secondly, this study aimed to

explore the relationship between students’ SLSE and students’ perceptions of their own

understanding. Derived from the research purposes, this study addressed the following

questions:

1. Does students’ perception of their understanding match their actual understanding?

2. Is students’ perception of their understanding influenced by their self-efficacy?

2. Methodology and Methods

2.1. Methodology

A case study approach was taken, one that Denscombe (2010) has characterised as a

‘focus on just one instance of the thing that is to be investigated’. Focussing on individual

instances rather than many may reveal insights about the general by looking at the particular.

An advantage that a case study has over a survey approach is that there is greater opportunity

to explore things in more detail and discover new insights that may not have been possible

with a more superficial approach. Therefore, the complexities of a given situation can also be

exposed. This is useful as relationships and processes within social settings are often

interrelated and interconnected. Furthermore, the case being explored is a ‘naturally

occurring’ phenomenon (Yin, 2009); something that usually already exists, and is not a

situation that has been artificially created for the purpose of research. This gives the case

study another benefit of a natural setting.

The range of potential ‘cases’ is very widespread but for good case-study research, it

is essential for the unit to have distinct boundaries, where the researcher is able to keep it

separate from other similar things of its kind and distinct from its social context. The case

study approach also requires the researcher to select one case from a wider range of examples

of the type of thing that is being explored, based on their distinctive features.

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In addition to the advantages mentioned earlier, the variety of methods used in case

studies results in multiple sources of data. This facilitates validation of the data through

triangulation. The major criticism of case studies is in relation to the credibility of

generalisations made from its findings. Therefore, researchers must highlight the extent to

which the case is similar and dissimilar to others of its type. This approach also often gets

accused of lacking a degree of rigour, and one that relies on qualitative data and interpretative

procedures instead of quantitative data and statistical methods. Another limitation of case

studies is the problem of ‘the observer effect’. This can arise as case studies usually comprise

lengthened involvement over time, potentially causing those being researched, to behave in a

different manner from normal, as they may be aware that they are being observed. This may

be the case in the present study, given my dual role as teacher and researcher.

2.1.1. Details of the case

The present study involved 27 students in a year 7 class (around 11-12 years old)

from a secondary school in the county of Cambridgeshire. Among the participants, 16 were

male and 11 were female, and were from a class with high academic achievement. The

surveyed students came from a variety of villages in the catchment area with different socio-

economic backgrounds. Permission to gather data was provided by the school administration

and students were informed that the data collection process was anonymous and voluntary.

The topic being studied during this research was of ‘Particle Solutions’. Within this,

they explored the properties of solids, liquids and gases and gained a greater understanding of

the ‘Particle Model’. Next, they applied the Particle Model theory to explain every day

phenomena such as gas pressure, expansion, diffusion and contraction and to consider the

motion, forces and closeness of particles to help explain observations obtained during

practical work, such as changing of state. Later lessons aimed at students’ understanding of

what mixtures are and how you can separate them and also their understanding of the process

of dissolving. Furthermore, students had to then use their knowledge about separating

mixtures to obtain a sample of salt from rock salt and understand that salt comes from a

variety of sources and has many uses.

Since students build their own concepts, their constructions of chemical concepts

sometimes differ from the one the teacher holds and has tried to present (Nakhleh, 1992).

These misconceptions differ from commonly accepted scientific understanding and may

interfere with subsequent learning. The scientifically accepted model that matter is made up

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of discrete particles with empty space between them and are in constant motion is a concept

students of all ages have trouble understanding (Novick & Nussbaum, 1981). This study

revealed that over half of the students perceived matter as a continuous medium that was

static. In addition, the authors highlighted that aspects of the particulate model were

differentially accepted by students. For example, the idea that liquefaction of gases involved

the merging of particles was accepted by 70%, whereas only 40% accepted the idea that

particles in a gaseous phase have empty spaces between them. In another study of 300

students aged 15-16, nearly half of the students believed that the properties of the substance

were also properties of the individual atom (Ben-Zvi et al, 1986). This concept of the

particulate nature of matter is an important foundation for understanding many chemical

concepts (Krajcik, 1990). Misconceptions about the concepts of atoms and molecules have

also been revealed by researchers. In a study with 17-18 year old Canadian students, half of

the students in the sample believed: molecules are much larger than in reality; molecules of

the same substance may vary in size; molecules of the same substance can change shapes in

different phases; molecules have different weights in different phases and also that atoms are

alive (Griffiths & Preston, 1989).

2.2. Data Collection Methods

To allow for the examination of actual and perceived understanding of students within

its real-life context, this study compared test results with confidence indicator colours after

every question of the test, together with subject interviews. To study whether students’

perceptions of their understanding was influenced by their self-efficacy, a Science Learning

Self-Efficacy (SLSE) questionnaire was administered. Further data were collected through

subject interviews, student focus groups and teacher interviews. A summary of the research

questions and data sources is given below in Table 1.

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Table 1. Research questions, data sources, and details of when the data was collected

Proposed Methodology

Case study

Title A critical analysis of the match or mismatch between the perceived and actual understanding of year 7 students, studying particle solutions, when engaging in self-assessment learning activities.

Research question: When engaged in self-assessment activities, what influences year 7 students’ perceptions of their own understanding?

Sub questions Data Source Data Source Data Source Data Source

1. Does students’ perception of their understanding match their actual understanding?

Red / amber / green traffic lights immediately after every question

Test results for every question

Subject interviews(Check on those with green + bottom marks and red + top marks.)

-

2. Is students’ perception of their understanding influenced by their self-efficacy?

SLSE questionnaire

Subject interviews Focus groups Teacher interview

When will I collect this data?

1. End of the first two lessons in the sequence.

2. End of the third lesson in the sequence

1. End of the first two lessons in the sequence.

2. During lunchtime, after the last lesson in the topic.

1. During lunchtime, after the last lesson in the topic.

2. During lunchtime, after the sixth lesson in the sequence.

-

2. End of the school term

2.2.1. Assessing Students’ Actual and Perceived Understanding

Two distinct tests were administered over two lessons and each of these tests

consisted of six questions which aimed to assess their understanding of the learning

objectives from the lesson they had previously done (Appendices 3 and 4). Students were

asked to indicate how confident they were that their given answer was correct by using red,

amber and green traffic light colours after each question in the space provided.

Tests are a useful way of collecting evidence about the knowledge and understanding

of students. However, as with all assessment data, the validity of outcomes will strongly

depend upon whether the test items are actually testing the knowledge and understanding

they claim to. Creating tests that are both valid and reliable is known to be difficult (Taber,

2013). For example, contextualised questions which are meant to be less abstract and

unfamiliar to the student, may complicate matters: students have to ‘process’ more

information, the context may illicit ‘everyday’ ways of thinking that do not match academic

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learning and the context may be more familiar to some students that others (causing a

potential gender- and cultural-bias) (Taber, 2003).

To confirm the results of the tests, subject interviews were carried out to further

analyse students who signalled green but had an incorrect answer and those who signalled red

but had a correct answer (a mismatch of actual and perceived understanding). In this case,

‘structured’ interviews were carried out. Structured interviews consist of a pre-determined list

of questions, asked of each respondent to which they are given a limited-option of responses.

The tight control over the format of questions and answers leads itself to the advantage of

‘standardisation’. Furthermore, the selection of pre-coded answers ensures relatively easy

data analysis, lending itself to the collection of quantitative data and which is useful for

‘checking’ data (Taber, 2013).

Self-assessment, as mentioned before, increases student engagement in assessment

tasks and is also a key factor in maintaining student attention. Self-assessment data also has

the strength of providing information that is not easily determined, such as how much effort a

student has made in preparing for a certain task (Ross, 2006). In addition, numerous

researchers have reported a high level of reliability in self-assessment in terms of consistency

across tasks (Fitzgerald et al., 2000) and over short time periods (Chang et al., 2005). These

studies all involved students that had been taught how to evaluate their work effectively.

Limitations of self-assessment appear to be the concern over its validity. The literature

suggests that there are discrepancies between self-assessments and scores on other measures

(Ross, 2006). Furthermore, student self-assessment is generally shown to be higher than

teacher ratings (McEnery & Blanchard, 1999).

2.2.2. Assessing Students’ Self-Efficacy in Learning Science

Unlike tests that aim to measure learning, questionnaires contain questions that all

respondents should be able to respond to (Taber, 2013). There are strengths and limitations of

the different types of items and scales used in questionnaires. Closed questions are simple to

analyse but only investigate which of the offered options respondents chose. There is

opportunity for respondents to give answers that closer matches their own views in open

questions, but these need to be later categorised to be reported in an economic way. The

selection of comments can also raise questions of how representative they are to the actual

data. It has been proposed that the design of questionnaires can have increased validity and

reliability if they do not contain central points as this forces the respondent to decide on how

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they feel. However, if they genuinely have neutral or mixed feelings about the statement, this

may result in false claims being made. Consistency of responses should be probed by

including several similar, or directly opposite items. Another limitation of questionnaires that

consist of many scale-type items is that they are known to be occasionally completed with

little thought. To reduce the risk of this happening, questionnaires should aim to not contain

too many items. Furthermore, they could also contain some statements that are reversed,

encouraging respondents to think more carefully about each item (Taber, 2013).

In general, questionnaires have the advantages of being economical, relatively easy to

arrange and supplying standardised answers. As respondents are posed with identical, pre-

coded questions, there is no scope for variation via face-to-face contact with the added

benefit of the data not being affected by ‘interpersonal factors’. However, there are

disadvantages to pre-coded questions which should be considered. Together with being

frustrating and restricting for the respondents, they could also ‘bias the findings towards the

researcher’s, rather than the respondent’s, way of seeing things’ (Denscombe, 2010).

A 28-item Science Learning Self-Efficacy (SLSE) instrument was adopted to assess the

participants’ self-efficacy in learning science (Lin and Tsai, 2013) (Appendix 2). The items

of the SLSE instrument were presented with bipolar strongly agree/strongly disagree

statements in a four-point Likert scale (4=strongly agree, 3=agree, 2=disagree, 1=strongly

disagree), assessing the dimensions discussed in Table 2.

Table 2. The five dimensions assessed with the SLSE instrument

Distinct Dimension Description of what is assessed in participants’ confidence

Conceptual Understanding

Ability to use fundamental cognitive skills such as science concepts, laws or theories.

Higher-Order Cognitive Skills

Ability to utilize sophisticated cognitive skills including problem-solving, critical thinking or scientific inquiry.

Practical Work Ability to conduct science experiments in laboratory activities.

Everyday Application Ability to apply science concepts and skills in their daily life.

Science Communication Ability to communicate or discuss with classroom peers or others

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2.2.3. Interviews

For an interview to take place there must be consent from the interviewee who agrees

and understands that the material obtained will be used for research purposes. The

interviewee must also agree that their words can be treated ‘on the record’ and ‘for the

record’ unless they specify otherwise and that the agenda for the discussion will be controlled

by the researcher. Interviews are useful for in-depth exploration of complex and subtle

phenomena, such as people’s experiences, opinions, feelings and emotions.

There are several types of research interviews, in addition to the ‘structured’ interview

mentioned previously (in section 2.2.1). With semi-structured interviews, the researcher still

has a set of issues to be considered and questions to be answered but is flexible in the order in

which the topics are addressed. Additionally, there is greater flexibility for the interviewee as

answers are open-ended. This allows the interviewee to develop and elaborate on points

which are of interest to them. With unstructured interviews, the role of researcher is to be as

unobtrusive as possible. Semi-structured and unstructured interviews are on a continuum

scale so it is likely for both to feature in a single interview. Due to the nature of the

interviewee having freedom to ‘speak their mind’, semi- and unstructured interviews are

useful for discovering ideas about complex issues (Denscombe, 2013).

The conducted teacher and student interviews were carried out one-to-one. This type

of interview has the advantages of being easy to arrange and also, the opinions and views

expressed throughout the interview originate from one source. This makes it easy for the

interviewer to match specific ideas with a certain person. However, one-to-one interviews do

have a disadvantage of providing limited opinions and views (Denscombe, 2013).

Additionally, face-to-face interviews involve social cues, such as voice, intonation and body

language from the interviewer which may influence the answers given from the interviewee

(Opdenakker, 2006).

To address the research question ‘Does students’ perception of their understanding

match their actual understanding?’ four students were chosen to interview with regards to

their test papers. Students were selected based on whether they had a mismatch of actual and

perceived understanding. The interview began with structured questions that probed their

understanding of the test questions to double check their responses. The same students were

then interviewed to answer the second research question, ‘Is students’ perception of their

understanding influenced by their self-efficacy?’ To investigate this, semi-structured

questions were asked of the students to explore this question. All interviews were audio

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recorded and there was confirmation and reassurance about the confidentially of the

discussion.

2.2.4. Focus groups

Focus groups contain a small number of people brought together by the researcher to

investigate feelings, perceptions, attitudes and ideas about a certain topic. They are helpful

for exploring the extent to which shared views exist amongst a group of individuals relating

to a specific topic. As the name suggests, focus groups have a ‘focus’ to the session, with a

discussion being based around a topic in which all participants have similar knowledge of.

There is also a particular emphasis on the group’s interaction as means of acquiring

information, in which the moderator’s role is to facilitate this interaction and not to lead the

discussion.

For this research, a group of four students from the class being studied were chosen to

take part in the focus group. As the overall aim of the research was to explore in depth a

particular solution with a view to exploring the specifics (Denscombe, 2010), students were

deliberately chosen to ensure members of the group were likely to hold opposing views on

the topic for discussion. As the students are under the protection of responsible others,

permission was sought from the school organisation and authorisation to conduct the

interview was gained before the interview took place. The prospective interviewees were

contacted in advance and the interview was arranged for the following week, lasting 20

minutes. Semi-structured questions were asked that probed the research question ‘Is students’

perception of their understanding influenced by their self-efficacy?’ and the conversations in

focus groups were audio recorded. These questions covered the themes of self-assessment,

self-efficacy in school and self-efficacy in science. A full list of prompt questions is included

in Appendix 1.

2.3. Analysis of data

2.3.1. Constant comparative method

For the subject interviews, focus groups and the teacher interview, audio recordings

were analysed using the constant comparative method. For this, audio recordings were

listened through, focussing on the questions that guided the research. Parts of the interviews

that I believed to be important were written down and themes that underpinned what people

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were saying were identified. These temporary constructs were used to compare against the

recordings again, and further notes and observations were written down. Temporary

constructs deemed to be unsuitable were deleted, and after another listen, a list of second

order constructs were made that seemed to explain the data (Wilson, 2013). This process

helped to summarise the important themes in the data.

2.3.2. Descriptive statistics

Answers to the test questions were compared to their confidence (red/amber/green)

responses after each question. Test answers were marked either correct or incorrect and were

only compared to red and green responses (i.e. not at all confident and very confident that

their given answer was correct). Amber responses were discounted. A correct answer with a

green response and an incorrect answer with a red response were regarded as a match of

actual and perceived understanding. A correct answer with a red response and an incorrect

answer with a green response were regarded as a mismatch of actual and perceived

understanding. Matched and mismatched understandings were given as percentages of the

students’ responses. The mean and standard deviation for girls’ and boys’ test scores together

with matched versus mismatched responses were calculated.

For the SLSE instrument, scale scores were computed by calculating the mean of the

items in each domain for each individual and the mean scores for boys and girls.

2.3.3. Association statistics

To initially explore the relationship between the students’ match or mismatch of

actual and perceived understanding and SLSE, Pearson correlation analysis of the students’

responses on the test papers and SLSE instrument was undertaken. There are a number of

assumptions that are made with respect to Pearson’s correlation. Included in these are that the

two variables should be measured at the interval or ratio level and that there needs to be a

linear relationship between these two variables. Also, as Pearson’s r is very sensitive to

outliers, these should be minimised or omitted, and finally, the variables should be

approximately normally distributed (Spiegelman, 2010).

15


2.4. Validity and reliability

Validity means that both the methods and data are ‘right’ in reflecting the reality and

truth. Methods used to obtain data should measure suitable indicators of the concept, giving

accurate results. A good level of reliability means that a research instrument will consistently

provide the same data, time and time again. If there were to be any variation, this would be

due to variation in the thing being measured and not due to the volatile nature of the research

instrument itself (Denscombe, 2010).

To ensure validity in what was being said in interviews, interview data was

corroborated with other data sources on the topic. Furthermore, there was often confirmation

on what was meant by the interviewee to avoid misinterpretation. The use of quantitative data

produces numerical data that is independent from the researcher and so should not be

influenced by the researcher. In this study, test papers, confidence indicator colours and

SLSE questionnaires gave standardised data and furthermore, the SLSE instrument was

validated by the method of exploratory factor analysis in the study of Lin and Tsai (2013). As

a check on the qualitative data in this study, there has been an explicit account of the

methods, analysis and decision making showing the readers as much detail as possible the

lines of analysis that led to certain conclusions (Denscombe, 2010). Qualitative data was also

checked for external reliability with other comparable studies.

2.5. Ethics

This educational research followed guidelines set out by the British Educational

Research Association (BERA, 2011), summarised in the table below.

Table 3. Adherence to Ethical Guidelines for Educational Research

Responsibilities to…

What was done in the research to comply with the guidelines

Participants Voluntary Informed Consent All persons involved were treated within an ethic of respect. Participants understood and agreed to their participation, prior to the research getting

underway.Openness and Disclosure Participants’ voluntary informed consent was secured, before research was carried

out, and there was no deception or subterfuge from the researchers.Right to Withdraw Participants were informed that they had the right to withdraw from the research for

any or no reason, and at any time.Children, Vulnerable Young People and Vulnerable Adults In all actions, the best interests of the child were the primary consideration. Children capable of forming their own views were granted the right to express their

16


views freely in all matters affecting them. Researchers ensured that they themselves and any collaborators complied with legal

requirements in relation to working with school children. All necessary steps were taken to reduce the sense of intrusion to the children and to

put them at their ease. Impact of research on the normal working and workloads of participants was

minimised.Incentives Use of incentives to encourage participation was commensurate with good sense and

avoided choices which had undesirable effects. There was acknowledgement that the use of incentives had the potential to create a

bias in sampling or in participant responses.Detriment Arising from Participation in Research Researchers made known to the participants that any unexpected detriment to

participants, which arose during the research, must be brought immediately to their attention or to the attention of their guardians.

Steps were taken to minimize the effects of designs that advantage or are perceived to advantage one group of participants over others.

Privacy There was confidential and anonymous treatment of participants’ data Researchers complied with the legal requirements in relation to the storage and use of

personal data as set down by the Data Protection Act (1998) and any subsequent similar acts.

Disclosure Researchers who judge that the effect of the agreements they have made with

participants, on confidentiality and anonymity, will allow the continuation of illegal behaviour, which has come to light in the course of the research, must making disclosure to the appropriate authorities. If the behaviour is likely to be harmful to the participants or to others, the researchers must also consider disclosure.

At all times the decision to override agreements on confidentiality and anonymity must be taken after careful and thorough deliberation.

Sponsors of Research

Methods Only methods fit for the purpose of the research undertaken were employed. Researchers have communicated the extent to which their data collection and analysis

techniques, and the inferences to be drawn from their findings, are reliable and valid.Publication The researchers have make themselves familiar with the BERA research writing

guidelinesThe Community of Educational Researchers

Misconduct This research was conducted to the highest standards. Subject to any limitations imposed by agreements to protect confidentiality and

anonymity, data and methods amenable to reasonable external scrutiny. There is contribution of critical analysis and constructive criticism.Authorship Academic status or other indicator of seniority has not determined first authorship

Educational Professionals, Policy Makers and the General Public

Researchers will seek to make public the results of their research for the benefit of educational professionals, policy makers and a wider public understanding of educational policy and practice.

Communication of findings, and the practical significance of their research, will be given in a clear, straightforward fashion.

17


3. Analysis of Findings

3.1. Does students’ perception of their understanding match their actual

understanding?

The data collected from test results with confidence indicator colours and subject

interviews presented several interesting findings. Firstly, it was highlighted that overall, girl’s

perceptions of their understanding was more accurate than boys’. Additionally, girls were

more likely to believe that they had an incorrect answer when they were actually correct. This

is in contrast to another finding that demonstrated boys were more likely to believe that they

had a correct answer when they were actually incorrect. Because there is evidence of the

effect of gender in the literature (Fast et al., 2010; Griggs et al., 2013), these findings were

investigated further.

3.1.1. Overall, girls’ perception of their understanding was more accurate than boys’

The class’ mean percentage of matched understanding responses was 71% (±0.18 SD)

with girls having a higher mean percentage of 73% (±0.16 SD) than boys of 70% (±0.20 SD)

(Figs 1 and 2). This demonstrates that pupils’ perceived understanding greatly matched their

actual understanding. This is in contrast to previous studies that found evidence to suggest

school students to be relatively inaccurate assessors (Ross, 2006). A possible explanation for

this may be due to the current study using a high-achieving cohort of students. Indeed, a

similar study reported that higher-scoring students were more accurate at predicting their

examination scores than lower-scoring students (Hacker et al., 2000). A potential theory for

this could be that the higher ability students studied in this research have the awareness of the

knowledge that they do and do not possess.

In general, girls in this study were more accurate at judging whether they did or did

not know the questions asked of them. This is in concordance with other researchers

investigating differences in accuracy of self-perception between male and female students

(Pajares, 1996). Accurate self-perceptions may allow students to more accurately assess their

strategies of problem solving. However, ‘realistic’ self-appraisals may be in danger of

lowering optimism and therefore, lowering levels of effort, perseverance and persistence

(Bandura, 1997). Consequently, just as much attention should be paid to student’s perceived

competence to their actual capability as it is their perceptions that may more accurately

predict motivation and academic choices in the future (Hackett & Betz, 1989). Accuracy in

18


self-assessment by distinguishing strengths and weaknesses is critical for students to make

more effective decisions about where to apply their learning efforts. This will allow students

to take responsibility for their education and improve autonomy in gaining and improving on

their skills and knowledge (Dunning et al, 2004).

Figure 1. Comparing the percentages of matched versus mismatched actual and perceived

understanding between girls and boys

Matched Red + Correct Green + Incorrect0%

10%

20%

30%

40%

50%

60%

70%

80%0.703750000000001

0.03%

0.26625

73%

6%

20%

Comparing Boys' and Girls' Matched and Mismatched Responses

Boys' Girls'

Matched and mismatched responses

Res

pons

es (%

)

Comparing matched responses (i.e. red + incorrect and green + correct) and mismatched responses (i.e. red + correct and green + incorrect) between boys and girls. Boys = dark blue; Girls = light blue.

3.1.2. Girls were more likely to believe that they had an incorrect answer when they

were actually correct

The class’ mean percentage of mismatched understanding responses of red but correct

was very low at 0.04% (±0.09 SD) with girls having a higher mean percentage of 6% (±0.09

SD) than boys of 0.03% (±0.09 SD) (Figs 1 and 2). These mismatched responses were

validated through subject interviews which confirmed both selected girls were sure of their

responses. This highlights that the girls in this study had less confidence of being correct

when compared to the boys.

19


Figure 2. Percentage of student responses with matched and mismatched actual and

perceived understanding.

Aiden Jak

Elvan

Adam

Kayde

Ibrah

im

Ben J

Dejan

Gaea Elly

Alicja

Riley

Emily

0%

20%

40%

60%

80%

100%

120%

Proportion of matched and mismatched student responses

Students

Res

pons

es (%

)

Matched (blue) = correct + green response and incorrect + red response. Mismatched = correct + red response (red) and incorrect + green response (green). Boys = left side; Girls = right side

These results are consistent with other research that found girls, and in particular

gifted girls, to have a general tendency toward underconfidence (Lundeberg et al., 1994).

Furthermore, research shows that female students often have problems with self-confidence

and report greater stress over their competence than male students (Moffat et al., 2004). This

could have negative implications as students who lack confidence in skills they possess are

prone to avoiding tasks in which those skills are required, and more likely to give up in the

face of difficulty. Lent & Hackett (1987) studied the perceived and actual competence of

mathematical skills in college students. They demonstrated that generally, it is the

underestimation of competency and not the lack of capability that is responsible for

avoidance of math-related courses and careers, and this is more likely to be the case with

woman than men. When this is the case, identifying and modifying these perceptions would

prove beneficial.

20


3.1.3. Boys were more likely to believe that they had a correct answer when they were

actually incorrect

The class’ mean percentage of mismatched understanding responses of green but

incorrect was 24% (±0.17 SD) with girls having a lower mean percentage of 20% (±0.15 SD)

than boys of 26% (±0.18 SD) (Figs 1 and 2).

These mismatched responses were validated through subject interviews which

confirmed both selected boys were sure of their responses. Previous findings have shown a

disparity of results. Several studies have reported males to be more likely to overestimate

through self-assessment (Lind et al, 2002; Rees & Shepherd, 2005). Lind et al (2002)

assessed the ability of students to self-assess using a competency-based evaluation and

further found females to underestimate their performance, despite outperforming the male

students. However, another study involving an intervention to improve student understanding

of assessment criteria, found no identifiable difference between male and female self-

assessment (Rust et al., 2010). A possible explanation which could account for a lack of

gender difference may be that exposure of good quality exemplar assignments to students

could have caused underestimation of their own work. This perhaps had more of a

pronounced effect on previously over-confident males.

3.2. Is students’ perception of their understanding influenced by their self-efficacy?

3.2.1. Students scored highest on the ‘Practical Work’ dimension of the SLSE

instrument

The participants’ scores on the Science Learning Self-Efficacy instrument were

calculated. As a result, the classes mean scores and standard deviations of the SLSE

dimensions are shown in Table 4.

Table 4. Classes mean scores and standard deviations of the SLSE instrument

Distinct Dimension Class’ mean score Standard deviationConceptual Understanding 2.99 0.45Higher-Order Cognitive Skills 3.00 0.39Practical Work 3.71 0.41Everyday Application 2.97 0.51Science Communication 3.07 0.55

21


As shown by Table 4 and Figure 3, students scored highest on the ‘Practical Work’

dimension, identical to a result from a previous study by Lin et al (2013) who found (M =

3.44). Furthermore, another study found that one of the most positive predictors of student

science-related self-efficacy was practical work (Lavonen, & Laaksonen, 2009). The same

study also found that self-efficacy related to science was the most powerful predictor of

student performance, a result similar to Valijarvi et al. (2007).

This study asked students in interviews and focus groups a number of questions about

their self-efficacy in science classrooms, and the influence practical work has on this and on

science learning. During interviews, students unanimously agreed that their level of

confidence depended on the environment of the classroom. The following comments were

typical, suggesting that students felt less confident when they felt observed by their

classmates and were put off by the ‘big crowd’:

It depends on who is around…the atmosphere of the class. When it’s really silent you wouldn’t

feel confident. (Student F)

The most anxious thing is answering the questions if you were to put your hand up, because

you might be wrong, and feel anxious that people will laugh at you. (Student E)

As one might expect, students felt more confident and likely to contribute in group discussion

when they were doing smaller group activities, such as with practical work, as these

comments suggest:

It’s not so tense when we’re in groups and doing activities. All the pressure’s off…it’s a

relaxed environment. Everyone is doing their own thing so they’re not concentrating on you.

When you are in groups, everyone feels more confident in commenting so you want to

contribute more. (Student H)

In class if you put your hand up, you know that everyone is hearing and everyone is watching

but when you’re in groups you don’t get that feeling…everything else goes away. (Student E)

It appears that practical work promotes greater confidence in students because they are less

likely to feel judged by the whole class and feel less anxious in the science classroom. In

addition to self-efficacy, practical work was illustrated to increase student enjoyment and

stimulate learning, as these students comment:

22


When you think ‘science’, you think ‘experiments’. When you’re doing the experiments,

you’re learning about it. It explains what you are learning in front of you. (Student G)

People get excited [about practical work]…they enjoy it so much and in the end they realise

they learnt something. When you write the conclusion, it surprises you how much you know…

because you just did one practical. (Student E)

If you’re in groups, you are doing it yourself instead of watching the teacher do it so you can

make sure you know something by doing your own experiment. You can get more involved

and do your own investigations rather than just sitting, listening and writing down. (Student A)

This is in line with previous findings that highlighted practical work stimulates student

interest and curiosity in science, promoting aspects of scientific thinking and allowing

students to develop practical abilities (Hofstein, 1988).

3.2.2. Boys had a higher mean score in every dimension of the SLSE instrument

For each dimension of the SLSE instrument, boys had higher mean scores than the

girls (Table 5 and Figure 3).

Table 5. Boys’ and girls’ mean scores and standard deviations of the SLSE instrument

Distinct Dimension Boys’ mean score Standard deviation

Girls mean score Standard deviation

Conceptual Understanding

3.09 0.46 2.84 0.42

Higher-Order Cognitive Skills

3.04 0.41 2.94 0.38

Practical Work 3.80 0.36 3.60 0.48

Everyday Application 3.11 0.50 2.77 0.49

Science Communication 3.23 0.41 2.83 0.66

This is fitting with studies mentioned previously, stating gender (i.e. being a girl; Fast

et al., 2010) contributed to students having a lower science self-efficacy. This was confirmed

by students doing the focus interviews. For example, one boy commented that the level of his

confidence ‘depends on the topic, really’. However, most girls stated that it was the

relationships in the classroom that was a large predictor of their confidence, as these excerpts

reveal:

23


If you’ve got people around you that you trust, you feel confident. It’s better in forms

because you get to know the people a lot more but if you’re in Science, you don’t really

know them as well so you don’t know what they’re going to say. Sometimes you think that if

you say something it might go around the whole school. (Student E)

Girls are more worried about what people think and what they’re going to say. (Student F)

For girls, some people laugh and you don’t feel comfortable or know why. In Science, girls

worry about themselves because everyone frets about tying their hair up [for practical work]

…you don’t really want other people looking at you with your hair tied up because it makes

you feel awkward. (Student E)

As Science classes in this school are set by prior attainment and not by form, this may

explain the lower levels of self-efficacy for girls in the SLSE instrument. Furthermore, the

worries of girls about their appearance during practical work could account for their lower

mean score in the ‘Practical Work’ dimension of the instrument, in relation to boys. However,

in another interview with a student, she explained that she did not feel worried about what

others thought of her answers and felt quite confident in Science because she felt she

understood quite a lot of it. The following statement highlights this:

I wouldn’t be the only one who didn’t understand… [when asked if she ever worried about

answering questions in class]. I feel pretty confident because most of time, I understand the

things you are teaching us. I feel much more confident in school now because we’re in our last

term…we’ve been here for longer. (Student A)

This student was then asked a number of questions about whether she personally thought

there was a disparity between boys’ and girls’ confidence in science, and whether certain

factors might predict a person’s confidence. She stated that it depended on the type of person

they were, together with their knowledge and enjoyment of science. Furthermore, she thought

that whether the person had siblings or not could be a factor affecting a person’s confidence

as these comments suggests:

Not necessarily. I think it depends on the person and how much they enjoy science and how

much they know. I think if you enjoy it more, you’re more confident because if you enjoy it,

you are more relaxed. (Student A)

24


You could be more confident if you are younger because you know your brother or sister has

done it before and you can ask them if you are unsure…I’ve got an older brother in year 8 and

I know that for Maths or Science, I can ask him if he knows the answers to help me. (Student

A)

Figure 3. Boys’ and Girls’ Mean Scores in the Science Learning Self-Efficacy Instrument

CU HOCS PW EA SC2.5

2.7

2.9

3.1

3.3

3.5

3.7

3.9

Boys' and Girls' Mean Scores of the SLSE Dimensions

Boys'Girls'

Dimension of Science Learning Self-Efficacy

Mea

n Sc

ore

CU = Conceptual Understanding, HOCS = Higher-Order Cognitive Skills, PW = Practical Work, EA = Everyday Application, SC = Science Communication. Boys = dark blue; Girls = light blue

3.2.3. A strong correlation was found between girls’ SLSE score and both matched and

mismatched (false negative) responses

To understand the relationship between the students’ self-efficacy in learning science

and their perceptions of understanding, Pearson correlation analysis based on their responses

to the SLSE and test was performed. As shown in Table 6, the three measures of perceived

understanding factors (i.e. matched, mismatched (red + correct) and mismatched (green +

incorrect) were related to all mean self-efficacy scores of the SLSE instrument (i.e. whole

class, boys and girls), suggesting weak (i.e. the boys factor) to medium (i.e. the whole class

factor) to large (i.e. the girls factor) effect size coefficients (Cohen, 1992).

25


Table 6. Correlation of the students’ science learning self-efficacy and their perceptions of

understanding

Mean Self-Efficacy Score Matched Mismatched(Red + Correct)

Mismatched(Green + Incorrect)

Whole Class 0.13 (weak) -0.42 (medium) 0.08 (weak)Boys -0.04 (weak) -0.13 (weak) 0.12 (weak)Girls 0.57 (large) -0.75 (large) -0.13 (weak)

From these results, the strongest positive correlation was between girls’ mean self-

efficacy score and matched responses (Fig 4). From this graph, it is clear that as girls’ self-

efficacy in science score increases, the percentage of responses that had a match or perceived

and actual understanding also increased. This is similar to the results of previous studies,

whereby students with higher-scoring and higher self-efficacy were more accurate at

predicting their examination scores than lower-scoring students (Hacker et al., 2010).

The strongest negative correlation was between girls’ mean self-efficacy score and

mismatched (red + correct) responses (Fig 5). These findings imply that in general, as girls’

self-efficacy in science increases, they are more likely to know when they do and do not

understand something. Furthermore, as girls’ self-efficacy increases, they are also less likely

to think they are incorrect when they are actually correct.

Figure 4. Correlation of the girls’ science learning self-efficacy and their matched

perceptions of understanding

2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.60%

20%

40%

60%

80%

100%

R² = 0.322364393096376

Girls' Mean SLSE Score vs Matched Responses

SLSE Self-Efficacy Score

Res

pons

es (%

)

26


Figure 5. Correlation of the girls’ science learning self-efficacy and their mismatched (false

negative) perceptions of understanding

2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.60%

5%

10%

15%

20%

25%

30%

R² = 0.566096129434114

Girls' Mean SLSE Score vs Mismatched (False Negat-ive) Responses

SLSE Self-Efficacy Score

Res

pons

es (%

)

4. Conclusions and Implications

The purpose of this study was to investigate whether students’ perception of their

understanding matched their actual understanding and whether students’ perception of their

understanding was influenced by their self-efficacy. In response to the first research question,

the results indicate that overall, girls’ perception of their understanding was more accurate

than boys’. Furthermore, girls were more likely to believe that they had an incorrect answer

when they were actually correct and boys were more likely to believe that they had a correct

answer when they were actually incorrect. In response to the second question, students scored

highest on the ‘Practical Work’ dimension of the SLSE instrument; boys had a higher mean

score in every dimension of the SLSE instrument and a strong correlation was found between

girls’ SLSE score and both matched and mismatched (false negative) responses. It is

important to note that cognitive appraisal of a situation might affect expectations of personal

efficacy. Factors such as social, situational and temporal circumstances all influence the

micro-analysis of perceived coping capabilities that represent self-efficacy. It is not simply

down to personality traits (Bandura, 1977).

27


4.1. Conclusions and their wider significance

Developing reliable and valid assessment tools of student performance that accurately

indicate student learning is difficult. One feedback tool developed by Gardner-Medwin et al.

at University College, London has been to use confidence-based marking (CBM). This

methodology measures a learner’s knowledge quality by determining both the correctness of

the learner’s knowledge and confidence in that knowledge (Gardner-Medwin, 2006). With

this CBM method, students select a confidence rating of low (1), medium (2) or high (3) to a

question; that is, their confidence about their knowledge. If the student’s answer is correct,

they are awarded those marks (i.e. 1, 2 or 3). If the answer is wrong, the marks awarded at

these confidence levels are 0, -2 or -6. The scheme uses negative, graded marking for the

upper two confidence levels with the relative cost of a wrong answer increasing at higher

confidence levels. This graduation ensures that the scoring scheme is properly motivating

(Gardner-Medwin & Gahan, 2003). Assessment by CBM is a simple, valid and reliable

method for challenging students to think discriminately (Barr & Burke, 2013).

4.3. Implications for research

Given that this study appears to be the first to examine the relationship between self-

efficacy and perception of understanding, future research is needed on students from different

grade-levels, schools and geographical areas to generalise beyond this sample. Furthermore,

due to the small sample size, future studies should investigate the present relationship with a

larger cohort to obtain greater reliability and precision (Biau, 2008). Previous research

looking into the link between students’ SLSE and their approaches to learning science found

that students’ deep strategies and deep motive were strong predictors of their SLSE. Future

studies could explore the associations of underlying learning variables, such as conceptions,

approaches, self-efficacy, motivation and outcomes to build a more elaborated model of these

relationships.

4.2. Implications for practice

For students to become better assessors of their understanding, educators should aim

to improve self-efficacy amongst students. In a study by Zimmerman et al. (1996), students

were asked to predict their efficacy before undertaking an assignment or test and then later

graph those judgements alongside their actual scores. Once students could visually see the

dissociation of their predicted and actual score, their accuracy for subsequent self-efficacy

28


judgements improved. Furthermore, this self-evaluating task was also shown to help students

improve their studying methods and academic achievement (Campillo et al., 1999).

Therefore, teachers could not only help students to develop a stronger and more accurate way

of personal self-assessment but also to increase their self-efficacy and promote more

autonomous and independent learners.

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Appendix 1. Prompt questions for interviews

Self-Assessment

Did marking how confident you were in each question change your understanding?

With self-assessment in the classroom, how do you feel about how other people might

think or act towards you?

How accurate do you feel like you were able to assess yourselves?

Do you prefer marking your own work or have the teachers mark it? Why?

When you were marking your test questions with red, amber or green, do you think

you played it safe instead of taking a risk (i.e. put red or amber to be on the safe side)

or do you think you took a risk and were over-confident (i.e. put a green)?

How confident did you have to be, as a percentage, to award yourself green for a

question, as opposed to amber or red?

Self-Efficacy in School

Does your confidence in subjects affect how high you set your goals or how much

effort you put in?

Does your confidence depend on how anxious you are in class?

How confident do you generally feel in school? Is this affected by the people in your

class? Affected by the time of day? Affected by different subjects?

Self-Efficacy in Science

How confident do you generally feel in science lessons?

Do you think self-confidence in science differs between genders? If so, why?

When we do practical work in class, do you think your understanding of science

changes?

Does practical work alter your interest in science?

Do you ever relate school science into your everyday lives?

When you think about if you know something or not, do you feel this is affected by

how confident you are in science?

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Appendix 2. Example Science Learning Self-Efficacy Questionnaire with Answers – James

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Example Science Learning Self-Efficacy Questionnaire with Answers – Alicja

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Appendix 3. Example Test Paper 1 with Answers and Confidence Indicators – James

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Example Test Paper 1 with Answers and Confidence Indicators – Alicja

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Appendix 4. Example Test Paper 2 with Answers and Confidence Indicators – James

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Example Test Paper 2 with Answers and Confidence Indicators – Alicja

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1c assignment

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