pre-university students' engagement towards the learning of ...

PRE-UNIVERSITY STUDENTS’ ENGAGEMENT TOWARDS THE LEARNING OF MATHEMATICS Noradiza Abd Wahid Sultan Hassanal Bolkiah Institute of Education Universiti Brunei Darussalam, Bandar Seri Begawan, Brunei Darussalam [email protected] Masitah Shahrill Sultan Hassanal Bolkiah Institute of Education Universiti Brunei Darussalam, Bandar Seri Begawan, Brunei Darussalam [email protected] ABSTRACT The study of students’ engagement in Mathematics has been long discussed as the topic of many researches in Mathematics learning. In this study, we investigated the factors that contribute to preuniversity students’ engagement towards the learning of Mathematics. How the students engaged themselves in-class and out-of-class learning, how they cultivate their interest in learning and what are the connections under the three constructs that had been found by previous research, namely, Cognitive Engagement, Affective Engagement and Behavioural Engagement. This study followed a qualitative research approach; dimensions under each construct would be identified using several research designs, namely, classroom observation, follow-up interviews and also an instrument, Student Engagement Scale, which consisted of 46 items with 5 Likert-scales. The findings from this study would hopefully benefit educators to modify their teaching in such a way that engaged the students more in their own learning by knowing how to engage themselves towards the subject, Mathematics specifically. Field of Research:

Students’ engagement, constructs, pre-university students.

---------------------------------------------------------------------------------------------------------------------------------1. Introduction Student engagement has an extensive research base (Attard, 2011; Fredricks et al., 2004) and it is a highly complex and multi-faceted construct. Most researches in engagement acknowledge three commonly identified constructs affecting engagement in mathematics, namely, Affective Engagement, Behavioural Engagement and Cognitive Engagement. Students’ curriculum engagement had become a crucial element in the design of school curriculum and classroom learning in particular (Kong et al., 2003). Some researchers defined engagement as a deeper student relationship with classroom work, multi-faceted and operating at behavioural, affective and cognitive levels. In later years, based from the Fare Go Project (Fair Go Team, 2006), they supported the definition of engagement as a deeper student relationship with classroom work where students need to become ‘insiders’ within their classroom to feel they had a place and a say in the operation of their classroom and the learning they were involved with. Behavioural engagement was identified through measures of effort, persistence, attention, and concentration, questioning and communicating (Fredricks et al., 2004). Affective engagement on the other hand, could be considered the beliefs, attitudes and emotions as experienced by students. Aspects of affective engagement had been variously considered as anxiety, interest, and boredom (Connell & Wellborn, 1991; Kong et al., 2003); interest, achievement orientation, anxiety and frustration (Kong et al., 2003). Connell and Wellborn (1991) identified cognitive engagement as the Proceeding of the Social Sciences Research ICSSR 2014 (e-ISBN 978-967-11768-7-0). 9-10 June 2014, Kota Kinabalu, Sabah, MALAYSIA. Organized by http://WorldConferences.net 379

idea of investment, recognition of the value of learning and a willingness to go beyond the minimum requirements. In terms of engagement with mathematics, engagement occurred when students are procedurally engaged within the classroom, participating in tasks and ‘doing’ the mathematics, and hold the view that learning mathematics was worthwhile, valuable and useful both within and beyond classroom (Attard, 2011). 2. Literature Review A framework for conceptualising and measuring engagement in mathematics was developed by Kong et al. (2003) through research and validation, resulting in the identification of significant markers of engagement. These markers were adopted in this study as a framework for investigating, categorising and interpreting student engagement. Dimensions under each construct found by Kong and colleagues are summarised in Table 1.

   

Table 1: Dimensions under each construct based from Kong et al. (2003) Affective engagement Behavioural engagement Interest  Attentiveness  Achievement orientation  Diligence  Anxiety  Time spent on task  Frustration  Non-assigned time spent on task

Cognitive engagement Surface strategy Deep strategy Reliance

Based from the study of Kong and colleagues, the three constructs of student engagement, namely affective, cognitive and behavioural engagement, were further validated with the development of the instrument known as Student Engagement in Mathematics Classroom Scale. According to Kong et al. (2003), the approach to learning was closely related to cognitive engagement. The students demonstrated their engagement by either surface or deep strategy. Some of them might engage themselves in memorising various facts and rules in mathematics while others were involved in understanding the concepts behind the rules. Some others might rely on teacher’s instructions, following these instructions closely, in the hope of attaining desirable learning outcomes. Secondly, results also revealed that interest was a major aspect of affective engagement. Achievement orientation and prior experience of success were closely related to affective engagement while anxiety and frustration were other factors that involved. These affective and cognitive factors reflected in behavior. In the research of Kong et al. (2003), they found that engaged students shown to be attentive, diligent and willing to spend time on in-class and out-of-class mathematics learning. They work diligently on problems, either with a surface or deep approach, though this resulted in anxiety and frustration. Marks (2000) claimed that mathematics influenced student engagement indirectly through its strong positive relationship to authentic work for students at all grade levels, this was especially so for middle school students. Moreover, students consider themselves more teacher dependent in mathematics, where the teacher was the source of knowledge (Stodolsky et al., 1991). Fielding-Wells and Makar (2008) reported findings that suggested interest and frustration were two factors that showed strong practical differences between groups and there exist a very strong inverse relationship between the two. Students found increased interest in mathematical topics in which they were interested, could relate to, perceived to have value, could identify novelty and could experience enjoyment. These findings aligned well with Lepper and Hodell’s (1989) identified sources of intrinsic motivation: challenge (interest, value), curiosity (relatedness, value), fantasy (novelty, enjoyment), and student controlled, which was presented by definition of enquiry. Attard (2011) reported on student engagement claimed that the most powerful influence on engagement in mathematics appeared to be that of their teacher, which had different thoughts to any other researcher’s findings. This influence included the pedagogical repertoires employed by the teacher, and the pedagogical relationship that occurred between the teacher and students. A research by Proceeding of the Social Sciences Research ICSSR 2014 (e-ISBN 978-967-11768-7-0). 9-10 June 2014, Kota Kinabalu, Sabah, MALAYSIA. Organized by http://WorldConferences.net 380

Adams and Sargent (2012), from their multivariate analyses, indicated that particular teaching methods were associated with increased student engagement and decreased stress, encouragement to ask questions and active discussion was associated with higher levels of student engagement and lower levels of student stress. This study followed the framework based on the work of Kong et al. (2003) in identifying the probable variables affecting engagement. It aims to reveal what constitutes engagement, how the construct could be measured, what dimensions combined to result in engagement and how it affects the learning of mathematics. A qualitative grounded research method was used in this study and data was analysed and validated using statistical correlation method to identify any connections between the dimensions identified in engagement in relation to mathematics. 3. Purpose and Objectives of the Study In this study, we investigated upper-age level students (ranged from 16 to 18 years old) who were about to continue their study to higher level of education and investigations were conducted to identify the dimensions that drives them to pursue a mathematical course and what attributed them to have this motivation. As far as is known, there has not been any empirical research on upper-age level student’s engagement in mathematics with validation of construct in a school in Brunei Darussalam (hereafter referred to as Brunei). The school curriculum in Brunei is mainly examinationdriven and typically, undue emphases placed on lecturing, memorisation and preparation for inschool and public examination, unlike the research done in China by Adams and Sargent (2012). Based on researches in the Confucian Heritage Culture (CHC) regions, such as China, researchers discovered that intense examination pressure could result in a degree of student disengagement and superior performance might only be the result of enforced learning (Kong et al., 2003). Thus, there was a pressing need to investigate whether the study would have the same findings as past researches, or it would have different results for the Brunei context. The objectives of this study were to identify if patterns exists in student engagement towards the learning of mathematics, to explore any factors of engagement and how they might give any impact on mathematics learning, to identify some of the relationships that might be present between individual dimensions of affective, behavioural and cognitive engagement, and lastly, to determine how these variables might account for student engagement. The study attempted to answer the following research questions.

1. What is the qualitatively different dimensions upper-age level students experience engagement in mathematics learning?

2. How does student academically engaged in their mathematics learning may contribute to their learning outcome? 4. Methodology This study followed a qualitative grounded research. This research design consisted of classroom observations, follow-up student interviews, validated instrument, known as Student Engagement Scale. Data was analysed using SPSS for the reliability and validity of the instrument. Grounded theory enabled freedom to develop new theory if it differs from past literatures, it offered an explanation for the findings and it enabled us to conceptualise the latent social pattern, and most importantly, data collection could be qualitative data or quantitative data or a mixture of the two. Like any other qualitative research, the analytic procedures in data coding and analysis were based on the method of constant comparison. After noting an event, it was compared to other events with respect to commonalities and differences. We would then identify the key variable (for this study it was the identification of the constructs that occurred in student engagement) that explained what Proceeding of the Social Sciences Research ICSSR 2014 (e-ISBN 978-967-11768-7-0). 9-10 June 2014, Kota Kinabalu, Sabah, MALAYSIA. Organized by http://WorldConferences.net 381

was occurring and further developed an emerging theory. Data analysis was used for comparing the emerging theory with existing research and theories. 4.1 The Sample One college in Brunei was purposely chosen to participate in this study. This school was chosen because it was one of the top schools and well-known for its high ranking in academic achievement in Brunei. As there were two stages in conducting this research, hence two groups were chosen. For classroom observation, a focus group of eight students was chosen comprising of high, medium and low academic achievements. These eights students were observed in a classroom aided with a video recorder. Follow-up semi-structured interviews of these eight students were conducted with four other students from the same class. Pre-university students were chosen to participate in this study because, for some reason, they were at the stage of deciding whether to continue mathematics at a higher level of education, or perhaps, for some, may want to pursue work career related to mathematics. From the observations and interviews, once phenomena emerged, the instrument was developed, and two classes consisting of 30 students altogether participated in answering the questionnaires to validate the construct. 4.2 The Instrumentation The instrument used in this study was modified from the existing instrument based from the work of Kong et al. (2003) and a new instrument, named, Student Engagement Scale. This instrument consisted of 46 items with 5 points Likert-scales arranging from 1-strongly disagree, 2-disagree, 3neutral, 4-agree and 5-strongly agree. The items were developed and constructed accordingly with the identification of the dimensions. There were 18 items under cognitive engagement, 15 items under affective engagement and 11 items under behavioural engagement. All those dimensions were identified from the results of classroom observation and analysed interview transcripts. 4.3 Data Collection Procedure There were two stages in conducting this research. The first stage was on data coding analysis and modification of instrument. The first author observed the lessons to identify any behaviour shown by the focus group students. Behaviour interpretation was carried out based on the observations during the lesson with the use of a checklist prepared earlier comprising of (a) answering the teacher’s questions, (b) asking the teacher questions, (c) listening to the teacher’s explanation, (d) reading teacher’s notes, (e) discussing with classmates, (f) doing exercises, (g) doing other tasks assigned by the teacher (group activities), (h) inappropriate behavior (e.g., gazing out the window, drinking) and (i) others (e.g., preparing for the start of the lesson). Students were observed in terms of their attentiveness in class both listening to teacher’s explanation and doing exercises. The follow-up semi-structured interviews of the eight students with four other students in the same class were conducted after the lesson observations. Results from the interview were then used to identify patterns that exist among students in this study in terms of their engagement towards mathematics. Based on these findings, the dimensions were identified and the instrument could be developed. All interviews were recorded and they were semi-structured as well as open-ended.

4.4 Data Analysis The results from the observation were analysed and the findings were used to identify any dimensions that existed under behavioural engagement and it was only specific to eight students (focus group). The conclusions made were based from the phenomenon that emerged in mathematics classroom. Interview dialogues were transcribed and since these were from the focus Proceeding of the Social Sciences Research ICSSR 2014 (e-ISBN 978-967-11768-7-0). 9-10 June 2014, Kota Kinabalu, Sabah, MALAYSIA. Organized by http://WorldConferences.net 382

group interview, the analysed data would be from the view of the groups. Once all dimensions under each construct (Cognitive, Affective and Behavioural engagement) were identified, then the first author proceeded in modifying the existing instrument from Kong et al. (2003). This instrument was in the form of questionnaires and was given to the class observed and also to a subsequent class. The data from the questionnaires was analysed using Pearson Correlation to identify any relations between each dimension under each construct. The analyses proceeded with which dimensions correlated highly with each other and perhaps indicated which dimensions were strong. 5. Findings and Discussions 5.1 Classroom Observation The first author observed a focus group of eight students in a classroom, and the behaviour of the students identified in the classroom was recorded. It was discovered that students engaged themselves highly in relation to behavioural engagement, comprises of Attentiveness, Time Spent and Diligence. The results revealed that students spent most of their time doing their own work and spent less time in listening to the teacher’s exposition. It was observed that students had less concentration span during teacher’s explanation but put more of their involvement when doing exercises. Furthermore, it was revealed that students with higher engagement spent more than one hour on doing their homework and engaged more by putting their effort on spending out-of-class learning for more than two hours. Results of the recorded behaviours of the focus group of eight students are given in Table 2. Table 2: Behaviours observed from classroom observation consisting of eight focus group students Behaviours/ Student 1 2 3 4 5 6 7 8 Answering the teachers’ question Y Y N N Y N Y N Questioning the teacher N N N N N N N N Listening to teachers’ exposition Y Y N Y Y Y Y Y Reading notes provided Y Y N Y N Y N N Discussing with peers Y N N Y Y N N N Doing exercise given Y Y Y Y Y Y Y Y Doing other task given by the teacher (Group N N N N N N N N activity/homework sheet) Inappropriate behaviour (e.g. gazing the window, N Y N Y N Y N N drinking and looking around) Others (preparing for class) Y Y Y Y Y N N N Note: Y=Yes and N=None 5.2 Interview with the Students Follow-up interviews were carried out with 12 students from the class observed which included the focus group of eight students. The interviews were transcribed and analysed. This finding showed consistency with previous research findings. Table 3 contains the dimensions found under each construct from the analysed interview transcripts in this study.

Table 3: Dimensions found under each construct of student engagement in mathematics Cognitive Engagement Affective Engagement Behavioural Engagement Surface Strategy Interest Attentiveness Deep Strategy Achievement Orientation Diligence Reliance Anxiety Time Spent Proceeding of the Social Sciences Research ICSSR 2014 (e-ISBN 978-967-11768-7-0). 9-10 June 2014, Kota Kinabalu, Sabah, MALAYSIA. Organized by http://WorldConferences.net 383

Interest, Achievement Orientation: The results of the data collected on the students’ interest in mathematics showed that they like mathematical structures in a way that it applies to real-life application. Some students expressed interest in learning in various ways that they defined, in other words, they viewed engagement in multiple of representations and got engaged in their own way. They were fascinated by the applicability of mathematics to various real-life problems, the elegant of method of solving problems and the beauty of geometrical shapes. Comments provided by the students were extensive with a few simply professing enjoyment of mathematics. Student 5: Student 7: Student 3:

It applies to our daily life [activities], [for example] how to measure angle. The most important part of studying is the application of studying, so when you study, you let your mind be free, you visualize the thing and sometimes from that you can understand. I think my main motivation in learning mathematics is the famous physicist because I tend to watch documentaries about famous physicists and they always show the complicated equation, many unknown numbers, and letters which make no sense, but I do want to know how they derive that equation.

Kong et al. (2003) defined achievement oriented students as enjoyment in getting good results in mathematics. From the analysis, the students felt that it’s a must for them to get good results in mathematics as it relates to their other major subject such as Physics, and for presumably, their future career in Engineering. The drive and motivation for their effort was achieving good results, which, in turn, would bring them satisfaction. Some comments provided by the students that enjoyed getting good results and few included their parent’s motivation which makes them to be achievement oriented are presented below: Student 7: Student 4: Student 2: Student 11: Student 12:

Because I want to be an engineer, so Math is part of it. …I have to take math because of also Physics…also my future involving math. To get high marks? It satisfies me actually. I think the thing that motivates me is when I see other people’s result when they do well in math, it motivates me to do better, and it tells me to strive harder to get good results. It is a must because math is the only subject that I can score well and made my mum proud of me.

Anxiety: Most students were affected by anxiety in their learning. Those students afflicted with anxiety often felt tense when doing mathematics tests or even examination. They felt nervous when they encountered difficulties and were usually anxious when they were about to find out their test mark. Some of the students’ response from the interview showed that they felt anxious when they obtained low marks in mathematics and some even mentioned that although they spent hours of revising, they could still get low marks. The findings of this study also showed that students engaged cognitively in their learning. There were three dimensions found under cognitive engagement, which are surface and deep strategy, and reliance. These dimensions are further elaborated below. Surface Strategy and Deep Strategy: In surface strategy, it was evident from the follow-up interviews that students preferred to learn by rote memorisation and practices in learning mathematics. They simply did plenty of practices and referred to teacher’s notes when revising or even learning in class. When asked about the way they learnt mathematics, most of them were saying that they depend most of the time on past year papers in addition to teacher’s notes. They memorised the formula and practiced on the way their teacher taught them. Hence, surface strategy could be defined as learning by memorisation, practice and teacher’s notes. Answering the past year papers were the common answer provided by the students being interviewed. In contrast, students engaged in deep strategy willing to invest their time and effort in understanding mathematical concept, relating to real-life application which in turn would bring them satisfaction. Their motive was to find a deeper Proceeding of the Social Sciences Research ICSSR 2014 (e-ISBN 978-967-11768-7-0). 9-10 June 2014, Kota Kinabalu, Sabah, MALAYSIA. Organized by http://WorldConferences.net 384

explanation on the concept. Some of the responses from them are given below. Note: Responses from Students 1, 4 and 7 show Surface Strategy, and Deep Strategy for Students 7 and 4. Student 1: Student 4: Student 10:

Student 7:

Student 4:

I re-read the notes, and practice more on past year questions. I also depend on the teacher’s notes, but at the same time, I’m not into depending it at all, because sometimes teacher’s note, there are missing [of] something. First, I need to recap on the lesson before I proceed to do the past year paper… I try to do it from the examples first and then I proceed to past year questions. [I] like doing exercise and not teaching the same thing over and over again because we had studied it before… Understanding, like the formulas and all those stuff you have to understand, where the formula come from and how these thing work or something. Mathematics is about understanding. …I got to understand the concepts of math and how to apply it in real-life, I get to know a bit better, it’s more interesting.

Reliance: Reliance was defined as learning by depending on teachers or parents or friends as their source of motivation. It was found that students were highly dependable on their teacher and sometimes their peers. Students were most comfortable to follow their teacher’s instruction and would learn what their teacher taught them in class. Students might consult their teacher whenever they had difficulties with their classwork or even homework. Some comments provided by the students were: Student 1: Student 4:

Student 11:

I think I actually depend on the teacher’s note, because it’s how we work. I also depend on the teacher’s notes, but at the same time, I’m not into depending it at all, because sometimes teacher’s note, there are missing [of] something, so if, like I couldn’t understand what the teacher’s talking about, I would like to ask my teacher or my other former teachers and maybe if I have time at home, I probably search in the internet. I think math’s class is very interesting because the teacher, she makes it easier to learn math and just like what she said, she gives us a lot of practices by giving us homework from the past year paper and then discussing in class.

Students also engaged behaviourally in their learning and one of the most challenging hurdles to overcome was that the student’s own self-perception and their behaviour towards learning. Diligence: Diligence referred to the extent in which it measured the student’s investment in learning of mathematics and appreciate the value of the subject. Students with high diligence would study very hard to achieve their learning target or to avoid failure. They would strive harder so as not to let failure come ahead. From what was observed from the interview, students would diligently spend hours of studying just for one question and try their best to solve it. Student 7:

Sometimes I spent a lot of hours to spend on one question, I try to solve it first and if I can’t, the next day I’ll ask the teacher. I try first and not leave it… I try not to get help from others, do it yourself first because if you spend your time and get the answer, the satisfaction is ‘hmmph’ (satisfying).

Time Spent: Students who set their learning goal would never fear to invest time in their learning toward this subject specifically. From the analyzed interview transcript, most students spent hours of studying on homework alone, and some spent another extra hours on revising, and on past year questions. Some students were willing to invest extra time in school for discussion or group study. They were observed to be willing to spend time on out-of-class learning just for their study. The instrument, Student Engagement Scale, was tested for its reliability. The reliability index, Cronbach alpha was evaluated in the finalised scale and given in parentheses in Table 4. Table 4: Reliability index (Cronbach alpha) of the subscales of the student engagement scale Proceeding of the Social Sciences Research ICSSR 2014 (e-ISBN 978-967-11768-7-0). 9-10 June 2014, Kota Kinabalu, Sabah, MALAYSIA. Organized by http://WorldConferences.net 385

Cognitive Engagement Surface strategy (0.826) Deep strategy (0.710) Reliance (0.824) Note: NA is Not Applicable

Subscales (Cronbach alpha) Affective Engagement Interest (0.930) Achievement Orientation (0.846) Anxiety (0.921)

Behavioural Engagement Attentiveness (0.714) Diligence (0.871) Time Spent (NA)

It was proven that the internal consistency reliability indices were generally high. The mean for this reliability is 0.830. Interest had a high value of reliability (0.930) which showed that it was the main dimension of student engagement in their mathematics learning. These findings were quite encouraging for the researcher to explore on high level students’ engagement towards mathematics. According to Slavin (1999), he stated that success in the early grades did not guarantee success in later schooling, but failure in the early grades virtually ensured failure in later schooling. Consequently, the results in Table 5 show the correlation coefficients of all the constructs among various subscales that have been calculated. Table 5: The correlation coefficients of behavioural engagement with cognitive engagement and affective engagement Behavioural Cognitive engagement Affective engagement engagement Surface Deep Reliance Interest Achievement Anxiety strategy strategy orientation Attentiveness -0.272 0.410* 0.210 0.558** 0.134 0.247 Diligence -0.155 0.427* 0.122 0.334 0.024 -0.178 Note: **correlation is significant at the 0.01 level (2-tailed) *correlation is significant at the 0.05 level (2-tailed)

Based on the results of correlation coefficients, it was revealed that the students’ behavioural engagement was closely related to cognitive and affective engagement. Attentiveness and diligence were proved to be closely related to deep strategy. Students with deep strategy were closely correlated to be highly attentive and working hard on mathematics problems without easily giving up. On the other hand, attentiveness and diligence were negatively correlated with surface strategy, therefore learning by practicing and memorising did not assure students to be attentive and invest much time on studying the mathematical concept. Interest correlate highly with attentiveness and this showed that interested students paid their attention listening to teacher’s explanation and completing school work. Diligence correlate negatively with anxiety as those who worked hard in achieving success would never fear or worry to try. The correlation coefficients among the subscales of cognitive engagement and affective engagement were also calculated. The results are displayed in Table 6. The results revealed that deep strategy significantly correlated with interest. This showed that students who had high interest in mathematics would try to understand the mathematical concept deeply. However, results showed that interest correlate negatively with surface strategy due to perhaps uninterested students in mathematics would only try to memorise the formula without deeply understand how it applies to real-life applications. Reliance and surface strategy did not significantly correlated with anxiety, perhaps, because those who relied on their teacher and learned by practice did not show any worries or were anxious when faced with difficulties. Achievement orientation correlated positively with all the subscales under cognitive engagement, therefore students who were satisfied and striving hard to get good marks would try in as many ways in order to achieve their goal. Table 6: The correlation coefficients between cognitive and affective engagements Cognitive Engagement Affective Engagement Interest Achievement Orientation Reliance 0.307 0.206 Surface strategy -0.146 0.209

Anxiety -0.076 -0.070

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Deep strategy

0.492**

0.125

-0.337

Note: **correlation is significant at the 0.01 level (2-tailed) *correlation is significant at the 0.05 level (2-tailed)

6. Conclusions, Implication and Recommendations The main purpose of this study was to identify what contributed to student engagement and the outcome intended from engaged students. Engaging students in our classrooms is a critical component to help them succeed as a learner in school and in life. In Brunei context, its classroom culture was observed where the students refrained themselves from asking their teacher questions in the classroom. Therefore it was a great challenge to the teachers in this country to engage students in mathematics class or to give engaging activities. As this study was grounded, we concluded that most students learnt by surface strategy but of course, some might also engaged using deep strategy where they preferred to understand mathematical concept and applied it into real-life from what they understood. Students were intrinsically motivated when they experienced enjoyment and satisfaction gained from deeper understanding. On top of that, students depended on their teacher and parents as a set of motivation for them to maintain their grades. It was a great concern that students should have interest in learning because it correlated highly with the other two constructs; behavioural and cognitive engagements. Those students who had high interest in learning might then lead to the motivation to get good results for future career. Due to putting in a lot of effort in order to achieve good results, some might feel pressured and worried to face failure or an unexpected grade (which may be lower than what they expected). This may happen if the students felt that they could not control their learning in mathematics, and if such feelings continued, they may eventually disengage by giving up mathematics altogether. Those students who were attentive in class showed that they work diligently to get the correct answer, and of course they were willing to spend more time on certain tasks given both in-class and out-of-class learning. These students would gain enjoyment from their understanding of mathematics and were more likely to persist and might even be faced with challenges and difficulties. The implication of this study would be very helpful to educators and learners in reaffirming the belief that engagement was a necessity for student success. Educators may want to modify their teaching and improve on classroom interactions as it may give student’s self-confidence in mathematics learning and thus, increase student’s self-belief and goals to further studies and continue undertaking mathematics to higher education. As this study was grounded in nature, it appeared that student’s enthusiasm for mathematics depended on their interest, personal goal and attitudes. In addition, the findings of this research may also be applied into classroom practice of cooperative learning in order to provide positive classroom culture rich with interactions among teachers and peers and thus may produce a fun learning environment. It is recommended that future research will try to incorporate gender and the teacher’s behaviour as the factors of student engagement in school. Follow-up study from this current research will be helpful if the instrument is to be used in subsequent researches, and to incorporate several secondary schools in Brunei to check on the consistency of the findings from this study. It will hopefully give insight for teachers to consider their pedagogical teaching and student learning so as to meet the standard of education in Brunei to promote learning and produce educated and highly skilled people as measured by the highest international standards. 7. Scope and Limitation The scope of this study only measured one college which involved two classes of lower six preuniversity levels. Any findings from this study cannot be used to generalise the whole of Brunei or the entire population of pre-university students in Brunei. Furthermore, one class was chosen for


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