chapter 4: results and discussion: 4.1...

Results and Discussion

CHAPTER 4:

Results and Discussion:

4.1 Introduction

The focus of this chapter is statistical analysis and presentation of data on

major findings based on the questions related to research and also discussions

related to findings. Precisely, the study sought to establish whether there existed

any relationship between motivational beliefs (self-efficacy, intrinsic value, and

test anxiety) and self-regulated learning strategies (cognitive strategy use and

self-regulation) and academic achievement of school students.

The data was collected as per the methodology explained in the previous

chapter. The obtained data was sorted out and entered in order to create a

computer spreadsheet for analysis of the entire data. Later, the scoring was done

and consequently, the data was analyzed using various relevant statistical tools.

By use of Statistical Package for Social Sciences (SPSS), different statistical

measures were computed. The results obtained by tests are the main focus of

presentation in this chapter.

4.2 Research question 1: Is there a relationship between motivational

beliefs components and self-regulated learning components?

To answer the first question of this study, between each component of

motivational beliefs; _ self-efficacy, intrinsic value, test anxiety with two

components of self-regulated learning strategy; _ cognitive strategy used and

self-regulation relationships were established. Pearson correlation was

conducted to find out whether significant relationships existed between the stated

variables.

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In the following table (4.1) the descriptive statistics for all variables is given. The

descriptive statistics include the Mean, SD, and Number of students in each

variable.

Table 4.1: Means and standard deviations (Std. D) of motivation and self-regulated learning components

Variable Mean Std. D N

Motivation components

Self-efficacy 5.1950 0.87080 1020

Intrinsic value 5.7529 0.77182 1020

Test anxiety 3.6110 1.51022 1020

Self-regulated learning components

Cognitive strategy 5.2958 0.78636 1020 Self-regulation 5.033 0.93657 1020

Table 4.2 presents the relationship between motivational beliefs and self-

regulated learning components, which are as shown:

Table 4. 2: Statistical Correlations of Motivational and Self-Regulated Learning components

Variable Self-efficacy

Intrinsic value

test anxiety

cognitive strategy

self-regulation

1. Self-efficacy __ __ –– –– ––

2. Intrinsic value 0.417** –– –– –– ––

3. Test anxiety -0 .276** -0.143** –– –– ––

4. Cognitive strategy 0.489** 0.533** -0.176** –– ––

5. Self-regulation 0. 468** 0.463** _0.323** 0.604** –– Note: N=1020.**

P <0.01.

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4.2.1 The first sub-question of the study aimed at finding out whether any

correlation existed between self-efficacy as a motivational component and self-

regulated learning components.

From the table above, self-efficacy had significant correlation with cognitive

strategy (r=0. 489, p<0.01), and self-regulation (r=0. 468, p<0. 01) and these

correlations were positive.

As present in this table self-efficacy had a significant correlation with other

components of motivational beliefs. Pearson correlation between self-efficacy

and intrinsic value (r=0.417, p<0.01) was positively significant and with Test

anxiety (r=-0.276, p<0.01) was negatively significant. As this table showed that

self-efficacy had a higher correlation with self-regulated learning components

than other components of motivational beliefs.

Discussion:

In the present discussion correlation between motivational beliefs

components and self-regulated learning components will be discussed and the

size of the correlation will be described as low correlation (0.10 – 0.29),

moderate (0.30 - 0.49), high (0.50 - 0.60) and very high (0.70 __ 0.99) (Ciarrochi,

et al., 2001). Factors of motivational beliefs and self-regulated learning differ

significantly across relationship.

The results provide an empirical base and elaboration of the theoretical

linkage between individual differences in students’ motivational orientation and

their cognition engagement and self-regulation in classroom settings. As table

4.2 showed, the motivational components (self-efficacy, intrinsic value, and test

anxiety) were significantly related to self-regulated learning components

(cognitive strategy use and self-regulation). Relationship between self-efficacy

and cognitive strategy and self-regulation was positive and moderately

significant, and these relationships were independent of intrinsic value and test

anxiety. Our findings suggest that self-efficacy plays a facilitating role in relation

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to cognitive engagement. Improving self-efficacy leads to increased use of

cognitive strategies and thereby higher performance, and those students need

to have both the 'will' and the 'skill' to be successful in classrooms. Self-efficacy

had significant and moderate correlation with intrinsic value as another

components of motivational beliefs (r=0.417, p<0.01), and also had significant

but low and negative correlation with test anxiety (r=-0.276, P<0.01).

As the result showed, self-efficacy as a motivational beliefs component

had a higher correlation with self-regulated learning components than other

components of motivational beliefs. This finding supported relationship between

motivation and cognition. These results revealed that students who believed

they were capable (high self-efficacy) used more cognitive strategy, were more

self- regulated and persisted more at difficult task, also they were more

motivated to learn, and their anxiety was low.

It is important to help students develop a positive self-image of their

academic capabilities by assigning appropriate task where they can experience

success and so experience less anxiety. This could be achieved by

encouraging the students at every step of the way by helping them view

themselves as successful individuals. The finding of this study was supported

by Zimmerman and Martinez-Pons (1990) that they investigated the relation

between gifted students’ use of various regulatory strategies and grade level,

and level of self-efficacy for mathematical and verbal task.

4.2.2 The second sub-question of the study sought to find out whether any

correlation existed between intrinsic value and self-regulated leaning

components.

Intrinsic-value as other component of motivational beliefs had high

significant correlation with cognitive strategy (r= 0.533, p<0.01) and self-

regulation (r=0.463, p<0.01). Intrinsic value also had a negatively significant

correlation with test anxiety (r= -0.143, p<0.01). The findings of correlation

between intrinsic value indicated, whenever students had higher interest in the

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task and they felt it was meaningful and important for them they used more

cognitive strategies and persistence in difficult tasks and their test anxiety was

less.

Discussion:

As shown in table 4.2, intrinsic value as other components of motivational

beliefs also was very strongly related to use of cognitive strategy (r= 0.533,

p<0.01) and self-regulation (r=0.463, p<0.01), independent of self-efficacy and

test anxiety. Students who were motivated to learn (not just to get good grades)

and believed that their school work was interesting and important had more

cognitive engagement. Moreover, they made efforts to learn and comprehend the

material. In addition, these students were more likely to be self-regulating and to

report that they persisted on their academic work. Our findings indicated that the

students who chose to become cognitively engaged and self-regulating were

those who were interested in the work and valued the tasks they worked in their

classroom. Accordingly, students’ intrinsic value and motivation to learn were

important components to be considered in our model of how students came to

use different cognitive strategies and became self-regulating learners.

For instance, Eccles and Wigfield (1992) have found that students who

consider skill in mathematics to be valuable reported that they would take

additional maths courses in the future when compared to students who did not

value the material in maths. With respect to students’ effort or level of cognitive

engagement, Wolter and Pintrich (1998) found that middle school students who

expressed greater valuing of the material in a subject area reported using more

cognitive and self-regulatory strategies to that subject area. The current study

agrees with Wolter and Pintrich research.

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4.2.3 The third sub-question sought to find out whether there was any

correlation between test anxiety and self-regulated strategy components.

As presented in Table 4.1, there was negative significant correlation

between test anxiety and other components. Therefore this variable had an

inverse relationship with other variables. As noted, correlation between test

anxiety with cognitive strategies (r=-0.176, p<0.01), and self-regulation (r=-0.323,

p<0.01) was negative. It was concluded that when the students had a fear of

failure or had test anxiety, they used less cognitive strategy and self-regulatory

strategies.

Also, correlation between cognitive strategies and self-regulation was high

and positively significant (r=0.604, p<0.01). It means, students who used

cognitive strategy to learn, used comprehension, monitoring and effort

management and were persistent at a task.

Discussions:

This study concluded that test anxiety is a negative component and

whenever students had test anxiety, they were ineffective and inefficient

learners and often did not use appropriate cognitive strategies for achievement.

This finding is in agreement with Sarason’s (1980) finding where experience of

anxiety has been shown to impede cognition.

One of the reasons for test anxiety in students may be related to low or

lack of confidence in them. The lack of confidence is probably the math-anxious

learner’s greatest obstacle. It can be created when teachers place too much

emphasis on memorizing formulas and applying rules. It can result when

teachers fail to realize the critical connection between students’ academic

performance and their feelings about themselves and the subject being

studied.

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Our findings also showed that both components (cognitive strategy and

self-regulation) were highly correlated (r=0.604) at 0.01 percent level of

significance. This suggests that students must be able to understand not only

the “what” of cognitive strategies, but also how and when to use strategies

appropriately. The teachers have an important role in coaching the self-

regulatory learning of their students. Findings from this study support the work

of investigators who reported significant relations between self-efficacy, other

motivational constructs and cognitive engagement such as Pintrich and De

Groot (1990).

4.3 Research question 2: Do motivational belief components (self-efficacy,

intrinsic values, and test anxiety) influence academic achievement?

To find out whether motivational beliefs components had any effect on

academic achievement, One-way analysis of variance (ANOVA) was used.

Firstly for each component, students were divided into three categories such

as: low, medium and high. Any mean score higher than 5.51 was considered

high in each variable and a mean between 4.6 to 5.5 was considered medium

and a mean score lower than 4.5 was considered low for students in each

variable.

4.3: Critical Value of the F Distributiondf 0.05 level 0.01 level2, 1017 3.00 4.61

4.3.1 The first sub-question of the second question sought to find out whether

self-efficacy as a motivational beliefs component influenced academic

achievement.

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There were significant differences between three levels of self-efficacy and their

academic achievement (see table 4.5).

Table 4.4 reflected descriptive statistics of three levels of self-efficacy of

students and their academic achievement:

Table 4. 4: Means, Standard Deviations and Number of three level of self-efficacy of students and their academic achievement

N Mean Std. Deviation95% Confidence Interval for

Mean

Lower Bound Upper BoundLow 210 54.7653 18.91389 52.1923 57.3383medium 406 61.0781 18.41314 59.2816 62.8745high 404 70.0215 18.12379 68.2489 71.7941Total 1020 63.3207 19.31116 62.1342 64.5072

Table 4.5: Analysis of Variance in self-efficacy levels of students and their academic achievement

Sum of

Squares df Mean Square F Sig.Between Groups 35552.842 2 17776.421 52.485 .000Within Groups 344453.47

8 1017 338.696

Total 380006.321 1019

From the table 4.5 related to self-efficacy, the F value (52.485) is greater

than the degrees of freedom at 0.01 percent level of significance since the critical

point is 4.61. As such, it is held there is a significant difference between three

levels of self-efficacy in their academic achievement. As table 4.4 showed

whenever students had higher self-efficacy, their academic achievement

increased (see figure 4.1). The results from Post Hoc Test also showed that

there were significant differences between three levels of self-efficacy of students

and their academic achievement. This study showed that self-efficacy as a

motivational belief component influenced academic achievement of the students.

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Figure 4.1: Mean of academic achievement of three levels of self-efficacy of students

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Discussion:The findings from this study strengthen Bandura’s (1986) contention that

self-efficacy beliefs play an influential role in human factors. Our findings also

support the work of prior investigators who reported a significant connection

between self-efficacy beliefs and related academic outcomes. Hence, self-

efficacy as motivational belief influenced academic achievement of the

students. Comparison of performance of high, medium and low levels of self-

efficacy (see table 4.4) showed that according to increased self-efficacy in

school students, their academic achievement increased. The present study

agrees with Collins (1982) that reported that ability was related to performance

but, regardless of ability level, children with high self-efficacy completed more

problems correctly and reworked more of the ones they missed.

The obtained F value was highly significant, and also there was no

overlapping between lower bound and upper bound of three levels of self-

efficacy. Result of this study paralleled with most of the researches on self-

efficacy that showed this variable was in fact, more predictive of academic

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performance of the students as shown by Pintrich and De Groot (1990);

Pajares and Graham (1999); Bandura (1997); Stajkovic and Luthans (1998).

However, these researchers used path analysis to show this relation.

According Bandura (1986:25) among other personal factors, individuals

possess self-beliefs that enable people to exercise a measure of control over

their thought, feelings and actions that “what people think believe and feel

affects how they behave”. Of all self-beliefs, self-efficacy beliefs strongly

influence the choice people make, the effort they expend and how long they

apprehend to the task at hand. So the beliefs that people hold about their

abilities powerfully influence the way in which they will behave.

In school, for example, the beliefs that students develop about their

academic capabilities help determine what they do with the knowledge and

skills they possess. Moreover, self-efficacy beliefs are critical determinants of

how well knowledge and skills are acquired in the first place. The findings of

this study perfectly showed this view.

These findings suggest that teachers have a high responsibility to foster

positive but accurate self-efficacy in their students. Self-efficacy is not fostered

by providing inaccurate or effusive praise to students in the absence of specific

task accomplishments. This type of praise is meaningless and invalid and may

foster inaccurate beliefs in students who think they are capable of some task,

such as reading, when in fact they are not very good readers (Pintrich &

Schunk,2002). As noticed in table 4.4 students in this study estimate accurately

their ability in maths class, because their academic achievement confirmed

these beliefs. Although Bandura (1986) argued that some overestimate of

capability is useful because it increases effort and persistence.

Our findings also suggest the importance of maintaining self-efficacy level

over time. As discussed earlier, specific self-efficacy is a state, not trait. In other

words, self-efficacy can definitely be enhanced through training. In fact,

developing self-efficacy in trainees may be a solution to the long-standing

problem of transferring training to the job.

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4.3.2 The second sub-question sought to find out whether intrinsic value of

students influences their academic achievement?

Table 4.6 presents the means, standard deviations and numbers of three levels

of intrinsic value of students and their academic achievements, which are as

shown below:

Table 4.6: Mean, Standard Deviations and Number of three level of intrinsic value of students and their academic achievement


Mean

Lower Bound Upper Boundlow 61 59.9105 21.14992 54.4937 65.3272medium 252 59.3681 19.59007 56.9377 61.7985high 707 65.0238 18.82315 63.6339 66.4137Total 1020 63.3207 19.31116 62.1342 64.5072

Table 4.7 presents the significant differences between three levels of intrinsic

value of students and their academic achievement. The findings are shown in

table below:

Table 4.7: Analysis of Variance in intrinsic value levels of students and their academic achievement

Sum of


1 1017 367.069

Total 380006.321 1019

The test of differences between group means in intrinsic value levels of

students as another component of motivational beliefs has showed significant

differences. The F value (9.122) was greater than the critical value 4.61 with 2

and 1017 degrees of freedom at 0.01 levels of significance. Hence, there was

significant difference between three levels of intrinsic value and students’

academic achievement. The results showed that students with higher interest in

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the task showed better academic achievement (see figure 4.2). Multiple

comparisons (Post Hoc Test) showed that there was significant difference

between students with medium level of intrinsic value and students with high

level of intrinsic value. Significant differences did not exist between low and

medium level of intrinsic value.

Figure4.2: Mean of academic achievement of three levels of intrinsic value of students

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levels of intrinsic value

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Discussion:

This finding indicated that students with high level of intrinsic value

influence their academic achievement, but in low and moderate level this

component was ineffective because there was overlap between mean of

academic achievement of students with low level and medium level of intrinsic

value. On the other words, students who had high interest in the task and

believed task was important for them had high academic achievement. The

results of this study indicated that the students would be interested in the

special task, but for real engagement of them and getting value goals in the

tasks, it is important that students should have high interest in the task and they

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should think that the tasks were very useful and important for them. Ordinary

interest and goals do not lead to good performance.

It is clear that maths subject is difficult for most of the students in high

school, but if students have utility value, it can lead to better engagement of

them. According to Wigfield (1994), utility value means how the task relates to

future goals. While students may not enjoy an activity, they may value a later

reward or outcome it produces. The activity must be integral to their vision of

their future. Because goals can play a key role in attaining later outcomes,

Educators and parents should help students see beyond the immediate activity

to the long-term benefits it procures. Teachers need to be able to answer the

common query, “Why do we have to study this subject?”

This finding agrees with the work of Tuckman and Abry (1998) and

disagrees with Eccles (1983), Wolter and Pintrich (1998) who found that value

components, did not have an influence on student achievement in maths, but

were closely tied to students’ choice of future maths course. This study’s finding

suggests that intrinsic value is an important component of students’

involvement about becoming cognitively engaged in their classroom academic

work. That is not especially surprising when an individual is intrinsically

motivated, they want to improve their skills for their own sake, and then they

are better engaged in their tasks. Intrinsic value results from the enjoyment, an

activity produces for the participants (Wigfield, 1994). When students enjoy

scholastic tasks, they are intrinsically motivated to do well. Both interest and

personal relevance produce high intrinsic value for a student. In addition, the

results imply that it is important for teachers to socialize students’ intrinsic value

for schoolwork, not only because it will necessarily lead to higher grades or

scores on academic assignments or standardized achievement tests directly,

but also because it may lead to more cognitive engagement in the day-to-day

work of the classroom.

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4.3.3 The third sub-question sought to find out whether test anxiety influenced

academic achievement?

Table 4.8 showed descriptive statistics of three levels of test anxiety and their

academic achievement, which are as shown:

Table 4.8: Mean, Standard Deviations and Number of three level of test anxiety of students and their academic achievement


Mean

Lower Bound Upper BoundLow 742 66.3892 18.62990 65.0465 67.7319Medium 175 57.3819 18.03284 54.6914 60.0723high 103 51.3057 19.35616 47.5228 55.0887Total 1020 63.3207 19.31116 62.1342 64.5072

Table 4.9 presents the significant differences between three levels of test

anxiety of students and their academic achievement, the findings are as shown in

the table below:

Table 4.9: Analysis of Variance in test anxiety levels of students and their academic achievement

Sum of


6 1017 346.095

Total 380006.321 1019

Test anxiety of the students as affect component of motivational beliefs

was found to be negatively related to students’ performance. The obtained F

value (40.491) was greater than the critical value 4.61 at 0.01 level of

significance. The findings from descriptive result showed that, increased test

anxiety of the students, decreased their academic achievement (see figure 4.3).

Multiple comparisons of the three levels of test anxiety of students and their

academic achievement showed that there were significant differences between

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academic achievement of students in all groups of test anxiety. However,

results of these three components showed, adaptive motivational beliefs such

as self-efficacy and intrinsic value were positively related to academic

performance, while maladaptive motivational beliefs such as anxiety were

negatively related with marks of the students.

Figure4.3: Mean of academic achievement of three levels of test anxiety of students

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Discussion:

The finding of this study implies that there was negative relationship

between test anxiety and academic achievement. The findings indicated that

low test anxiety students had significantly better academic achievement than

medium and high levels of students with test anxiety. The present finding

resembled with those reported by McDonald and Angus (2001) that a degree of

arousal or anxiety would be seen as beneficial for performance. Without any

fear of failure or encouragement to perform well on the test, a child is unlikely to

put adequate effort into preparation or is not sufficiently motivated when

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actually taking the test, and so will not perform to their fullest potential. The

finding of this study agrees with most of the studies in this field such as:

Ramachandran, 1990; Prins et al., 1994; Pintrich & De Groot, 1990; Turner et

al., 1993; Sarason, 1963.

Bandura (1997) in his theory mentioned that a stressful situation often

elicits emotional arousal and this arousal can serve to trigger off a perception of

low efficacy. For example, a student with test anxiety may experience his

increasing heart rate when an exam script is placed in front of him. He is

confronting a situation in which he will not be successful. In this study, High

anxious students performed poorly in mathematic subject. Our findings agree

with Benjamin et al. (1981), he reported, that high anxious students were very

ineffective and inefficient learners.

Some of the exam anxiety maybe related to preparedness of the students

taking the exam. Specially, poor preparation would seem to lead to higher

anxiety since a greater chance of a poor performance is likely to exist. Similarly,

individuals prone to high levels of test anxiety will at times be expected to erect

barrier to a good performance, such as studying less, etc (Baumeister &

Scher,1988, quoted by Chinta, 2005). Even though such “self-handicapping”

activities may see contradictory, they can minimize the negativity associated

with a poor exam performance since the student would be able to appeal to

these self-erected handicaps as reasons for the poor performances to shift

attention away from many ego-centric factors such as competence.

Furthermore, highly exam anxious individuals more readily generalize

from a single exam failure. Namely, exam failure is equated with personal

failure. Such individuals can be expected to have difficulty recuperating from an

initial failure to meet personal or social expectations. Instead of focusing on

succeeding on subsequent exam, they often posses more negatively exam

related thoughts regarding their previous failure. Thoughts which will impair

their performances on subsequent exams. Lower grades on mid term exam,

therefore, appear to be likely to increase test anxiety about the final.

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4.4 Research question 3: Do self-regulated learning components

(cognitive strategy use, self-regulation) influence academic achievement?

To answer this question also Analysis of variance was used.

4.4.1 The first sub-question sought to find out whether cognitive strategy

influenced academic achievement?

Table 4.10 showed the means, standard deviations and number of

students of three levels of cognitive strategy and their academic achievement.

The findings are summarized as shown:

Table 4.10: Mean, Standard Deviations and Number of three level of cognitive strategy of students and their academic achievement Cognitive strategy

N Mean Std. Deviation 95% Confidence Interval for Mean

Lower Bound Upper Bound

Low 161 58.5253 19.64372 55.4679 61.5827

medium 422 63.2274 18.95138 61.4141 65.0408high 437 65.1775 19.26692 63.3660 66.9889Total 1020 63.3207 19.31116 62.1342 64.5072

To establish whether cognitive strategy influenced students’ academic

achievement computations were made and the findings are reflected in table

below:Table 4.11: Analysis of Variance in cognitive strategy levels of students and

their academic achievement

Sum of Squares df Mean Square F Sig.

Between Groups 5212.655 2 2606.328 7.072 .001Within Groups 374793.66

5 1017 368.529

Total 380006.321 1019

Cognitive strategy as a component of self-regulated learning strategy was

found to be significantly influential in students’ academic achievement. By use

of one-way Analysis of Variance (ANOVA), a comparison was made between

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three levels of cognitive strategy as related to students’ academic achievement.

The findings indicated that the obtained F Value (7.072) was greater than the

degrees of freedom (df) at 0.01level of significance since the critical point was

4.61. This meant that significant differences existed between three levels of

students in cognitive strategy use. Comparisons of these three groups showed

that there were significant differences between students with low level of

cognitive strategy as compared to students with medium and high levels, but

there was no significant difference between academic achievement of students

with medium and high level of cognitive strategy use (see figure 4.4). The

results showed that students who used more cognitive strategies in maths class

(high level and medium level), were better in their performance than other

counterparts (low level of them).

Figure4.4: Mean of academic achievement of three levels of cognitive strategy of students

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Discussion:

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Findings related to cognitive strategy showed that students who used

cognitive strategy moderately had academic achievement similar to students

with high level of cognitive strategy use. It revealed that cognitive strategy was

not strong variable to influence academic achievement as compared to self-

efficacy and test anxiety. This also suggests that students need to know some

strategies to challenge the task and even knowledge about these strategies in a

usual manner is sufficient for high academic achievement in maths class.

It is not surprising because this study was related to maths subject and in

mathematics. Students did not use strategies such as rehearsal strategy. As

mentioned this strategy involves reciting and it is best used for simple tasks.

As Pintrich and Schunk (1996) mentioned rehearsal has been identified as a

surface processing strategy because it is associated with short-term retention.

So, students in maths subject needed to use deep processing strategies and

rehearsal strategies were not very useful in maths class. Although other

strategies like elaboration and organization have been characterized as deep

processing strategies. But in this study, cognitive strategies included these

three strategies and the significant findings between academic achievement of

low level and high level of students in cognitive strategy might be related to use

of deep strategies by them.

The findings of this study suggest that to maximise learning in maths

class, the instruction needs to focus learner attention on critical features of

information; provide supports for using storage strategies such as analogies,

example clear identifications, and well-organized presentations; and

incorporate opportunities for learner to respond, on the basis of their

understanding of material in order to determine if it had been correctly stored.

One can see that under this system, the learner’s preexisting storage system

(prior knowledge) would have a storage impact on how new information would

be stored. So, maths teachers needs to sequence learning properly so that new

subject matter is related to one previously presented. Maths teacher have to

use some strategies that they relate to deeper understanding, such as

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strategies for making information more meaningful for the learner, strategies for

emphasizing how material is organized strategies for directing student attention

to key point and etc.

4.4.2 The second sub-question was related to the influence of self-

regulation on students’ academic achievement.

Table 4.12 showed descriptive statistics of three levels of self-regulation of

students and their academic achievement:

Table 4.12: Mean, Standard Deviations and Number of three level of self-regulation of students and their academic achievement N Mean Std. Deviation 95% Confidence Interval for

Mean Lower Bound Upper Bound

Low 269 56.4947 19.62397 54.1390 58.8504medium 420 61.8574 19.38565 59.9980 63.7167high 331 70.7249 16.30753 68.9616 72.4881Total 1020 63.3207 19.31116 62.1342 64.5072

Table 4.13 presents the influence of self-regulation of students on their

academic achievement, which are as shown below:

Table 4.13: Analysis of Variance in self-regulation levels of students and their academic achievement

Sum of


6 1017 342.603

Total 380006.321 1019

The obtained F value (46.087) was greater than critical value 4.61 at 0.01

level of significance. As such the study held that significant differences existed in

three levels of self-regulation and their academic achievement. The results

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showed that whenever students used more self-regulated strategies they showed

better academic achievement (see figure 4.5). Multiple comparisons of these

three levels of self-regulation showed that there were significant differences

between academic achievement of three groups of students in use of self-

regulatory strategies

Figure4.5: Mean of academic achievement of three levels of self-regulation of students

Discussion:

As shown in table 4.13, there was significant difference between academic

achievement of students with three levels of self-regulation (F= 46.087,

p<0.01). As noticed this difference was very high. In other words, level of self-

regulation of the students influences their academic achievement. Therefore it

was held that students who used self-regulated strategies and effort

management, had significantly better performance on mathematics than

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students who used moderate and low level of these strategies. This result

suggests that this component is a very important one to improve students’

performance. It revealed that students have to know about how and when to

use these strategies and they have to know how to create and structure

favorable learning environment. This result also revealed that students must be

able to manage and regulate their time and their study environments, monitor

their effort, learn from peers, seek help and support from peers and instructors

to have better performance and this point is very important in maths class.

These resource management strategies enable students to manage their

environment and the available resources. The findings for the cognitive

variables provide valid data on academic performance in actual classroom

tasks of a general model of self-regulated learning.

This study therefore concluded that students who were more cognitively

engaged in trying to learn by memorizing, organizing, and transforming

classroom material through the use of rehearsal, elaboration, and

organizational cognitive strategies performed better than students who did not

use these strategies. These findings agree with most of the studies about

cognitive strategy and self-regulation such as Pintrich & De Groot, 1990;

Schunk & Zimmerman, 1994; Zimmerman & Martinez-Pons, 1990; Tuckman,

1993. Further research needs to be done about variables which can predict

students’ performance. Students need to learn these strategies for better

performance. In particular, it has highlighted that, besides advocating direct

strategies for enhancing academic performance, teacher structuring of related

motivational processes in the school and classroom could further support such

outcomes.

4.5 Research question 4: Do motivational beliefs components differ in boys

and girls students?

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To establish precisely through statistical analysis whether a difference based

on gender existed, a difference between means of variables in motivational

beliefs components of boys and girls was calculated through the students’t-test.

4.14: t- Critical Values

0.05 level 0.01 level Two tailed test 1.96 2.58

4.5.1 The first sub-research question of the first question test was to find out

whether significant differences existed between boys and girls in self-efficacy.

The findings were as reflected below:

Table 4.15: Gender difference in self-efficacy

Variable Gender N Mean df t sigSelf- efficacyEqual Variances assumed

Boys

Girls

518

502

5.18

5.21

1018 -0.807 0.420

The findings from table 4.15 indicated that the calculated t (0.807) was less than

the tabulated t (1.96) hence there was no significant difference between boys

and girls in self-efficacy (P>0.05). It means that girl students and boy students

had same self-confidence in maths subject. Although the findings showed that

there were no significant differences between boys and girls in this variable, the

results indicated that female students had greater self-efficacy than males.

Discussion:

The data indicated that although there were no significant differences

between boys and girls in self-efficacy variable, the comparison between mean

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score of boys (M=5.18) and girls (M=5.21), showed that girls were a little more

self-efficacious than boys.

According to Wigfield and Eccles (2002), gender differences in

achievement have diminished during the last few decades. This has also been

confirmed by the findings of this study. However, some of the gender

differences that exist still stem from socio-cultural status. As noticed, this study

was done on English medium schools, and in these schools, there are facilities

and opportunities for both boys and girls students and also the families have

almost better socio-economic status than families in other regional schools. So

in these schools gender difference gradually disappear.

One of the other interpretations of this result may be related to feedback to

the girls. According to Schunk and Lilly (1984) the motivation of students

enhances when feedback is given to students. The evidence of motivation was

derived from clear performance based on feedback. It turned out that girls are

sensitive than boys and they express more feelings related to learning. A

possibility exists that after obtaining feedback from their teacher, girls reacted

better than boys and checked their answers as well as did more practice on

their tasks.

In the whole, it seems that India is one of the largest democratic countries

in the world which provides equal opportunities for girls and boys. This research

finding is in agreement with Pajares and Graham (1999); Middleton and

Midgley (1996); Fouad and Smith (1996); Lopez and Lent (1992); Busch

(1995), but disagrees with some of the research findings about gender

differences in self-efficacy such as (Miller et al, 1996; Pajares, 1996; Wigfield

and Eccles, 1992; Pintrich & De Groot, 1990) which demonstrated that boys

reported higher mathematics self-efficacy than did girls.

Of course some of these researches were done on different subjects and

different classes. For example the findings of Pintrich and De Groot was about

English class and Science and also this study was done in western countries,

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so these different variables can interfere in the results such as culture and

parents supporting.

The findings of the present study were different from Wigfield et al. (1991)

that they showed, middle school years have been identified as the time during

which the gap between girls’ and boys’ self-perception of ability emerged, and

during these years boys are more self-confident than girls in studies. However,

the aforementioned findings are not supported by the present study. These

different results may be attributed to different cultures in the two studies. In

India, families support their children in education even up to adulthood and

hence the students experience fewer problems at puberty due to the stated

support. In western countries, during puberty, parents can not control their

children and the gap between boys and girls in these countries is high.

4.5.2 The second sub-question was related to gender difference in intrinsic

value of the students.

The findings from table 4.16 indicated that the calculated t (0.654) was

less than the tabulated t (1.96) hence there was no significant difference

between boys and girls in intrinsic value (p>0.05). This results have shown that

both girl and boy students had same interest and common goals in the maths

class. The findings were as reflected below:

Table 4.16: Gender difference in intrinsic value

Variable Gender N Mean df t sigIntrinsic valueEqual Variances assumed

Boys

Girls

518

502

5.74

5.78

1018 -0.654 0.513

Discussion:In spite of no difference between boys and girls in this variable, Mean

score of girls (5.78) was higher than boys (5.74). The result showed that girls

students were more interested the task than other counterparts. The findings

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paralleled those of Green et al, (1999), Pajares and Miller (1994), Russilo and

Arias (2004) and differ from the findings of Fenema and Sherman,1977,1978;

Eccles et al.(1983), that showed boys reported higher perceived value of

maths.

One of the different findings between the present study and other research

findings such as Fennema and Sherman (1977) might be related to different

samples. The samples of studies in these researchers were students in tenth

and eleventh graders. Boy students during these grade show growing logical

thinking and they can design the plan for their future. But in the current study,

the samples were selected from eighth grade. In these ages, girl students enter

puberty sooner than boys and their thinking is grown up than boys. So it is not

surprising for Fennema and Shermans’ study that boy students had higher

perceived value of maths.

Most of the research findings from western countries are obviously

different from the ones from the East. Children in western countries have

unfavorable early exposure to unacceptable social practices compared to their

counterparts in the Eastern countries. The early exposure affects their level of

maturity as well their studies. In western countries, adolescence stage is

shorter and during this period students join societal roles early than it is in

eastern societies. Children in eastern countries have a longer period during

their adolescence and at this time they tend to take responsibility seriously as

they become innovative in resolving of social problems. This is particularly

observed in the girl students who perceive the usefulness of maths for better

future and career prospects.

Parents in Eastern countries support their children to do well and accept

responsibility for their tasks; which is contrary to the western countries in that

most of the children are free from warm parental support. In India, parents

support their children be it boys or girls and this has enabled many women to

become proficient in many activities as well as in joining professions that were

previously considered men’s domain.

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Parents of English Medium school usually belonging to higher income

group, who maintain democratic values in their family life.

According to Multon et al. (1991), perception of self-efficacy and task

value often are correlated positively, also according to social cognitive theory,

the perceived importance of a task is the result of the outcome expectation an

individual has for a particular task and is related to self-efficacy judgments in

much the same way as outcome expectations. Bandura (1986) argued that

because beliefs in part determine, people generally value those tasks they feel

capable of accomplishing and do not place as much value on those for which

they have little confidence to perform. It is not unusual, then, that expectations

and perceived importance should be related, though the relationship is often

complex. Present study, found no gender differences in self-efficacy beliefs

and so, the students did not differ in intrinsic value.

4.5.3 The third sub-question of the study was related to gender difference in

test anxiety of the students.

The findings from gender difference on test anxiety as other motivational belief

component showed that there was no significant difference in test anxiety

across gender. Table 4.17 presents this results, which are as shown below:

Table 4.17: Gender difference in test anxiety

Variable Gender N Mean df t sigTest anxietyEqual Variances assumed

Boys

Girls

518

502

3.54

3.68

1018 -1.526 0.127

The findings from table 4.17 indicated that the calculated t (1.526) was

less than the tabulated t (1.96) hence there was no significant difference

between boys and girls in test anxiety (P>0.05). Specifically, results indicated

that female students showed test anxiety to a greater extent than boys and they

were more anxious than males as shown in Table 4.17.

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Discussion:

Fear of failure is found to be a major characteristic of test anxiety of

students (Zeidner, 1998). Females are found more stressed, as they perceive

failure in examinations similar to physical danger, pain and taunts from others.

The perceived pressure for achievement, and the related probability of failure

induce in them test anxiety. Parents, teachers and peers tend to evoke

expectations of academic demands and put pressure directly and indirectly as

well.

Another interpretation of this result may be related to biological aspect of

girls during this age. With the onset of puberty there is a sudden disruption in

the life of an adolescent. Psychoanalysts believe that the threatening influence,

of the powerful drive of sex makes erratic adolescent behavior and fills his/her

mind with anxiety as to what he should do about it. Girls enter this stage two or

three years earlier than boys (Sharma & Sharma, 2002 quoted by khosravi,

2005). By eighth standard, most of the boys’ student had not entered puberty

stage, while girls had. One of the reasons that girls were higher on anxiety than

boys, could be menstrual cycle, its attendant fear and shame as well as other

changes in the girls’ psychological body thereby enhancing anxiety in girls. This

finding is consistent with that of Pajares and Graham (1999), nonetheless, it

differs from the findings of Pajares and Miller (1994), Pajares and Kranzler

(1995), reported a higher maths test anxiety for boys.

As Pomerantz et al. (2002) indicated that girls are more vulnerable than

boys to internal distress. They are also more concerned about their perceptions

of their competence in school and worry over performance in school. Also girls

are prone to internal distress and they are more concerned with failure.

Some gender differences such as gender disparities that are visible are

caused by cultural expectations and norms. But fortunately this is changing in

urban Indian societies, although it is exists in many develop countries. In our

findings, boys and girls had similar motivational beliefs, these important

similarities in boys and girls may also be caused by numerous aspects of

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formal schooling that are generally common across Indian societies and appear

to exclude overt gender typing. For example, boys and girls appear to receive

similar messages about what it takes to do well at school and these

communicated contingencies are similar across the contexts under study.

Moreover, many aspects of individualized school-related experiences of

children (e.g., feed back regarding effort and luck) are also similar and not

pervaded by gender stereotyping.

On the whole the introductory review of earlier research and this study

suggests that gender differences in motivation are not stable. This ought not to

be considered remarkable. Gender (as compared to sex) is a social artifact and

is therefore likely to change in terms of its implications over time and place. It is

also clear from a wide range of motivational research that motivational levels

and styles are dependent on time and place. The presence or absence of

gender differences in motivation, and the direction of any such differences, are

likely to be dependent on myriad of local and broad cultural circumstances.

Rather than looking to determine in a definitive manner the nature of

motivational difference as a function of gender, the role of research ought to be

the mapping of variation between gender groups. Attempts should be made to

mark out the parameters of contexts, which produce particular patterns of

difference.

4.6 Research question 5: Do self-regulated learning components differ in boys

and girls students?

By use of t-test, a comparison was made between means of boys and girls in

each component.

4.6.1 The first sub-question of this study sought to find out whether

significant difference existed between girls and boys students in cognitive

strategy.

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The use of cognitive strategy showed differences in boys and girls. As shown

below:

Table 4.18: Gender differences in cognitive strategy

Variable Gender N Mean df t sigCognitive strategyEqual Variances assumed

Boys

Girls

518

502

5.21

5.38

1018 -3.40 0.001

The findings from table 4.18 indicated that the calculated t (3.40) was greater

than the tabulated t (2.58) at 0.01 percent level of significance, hence there was

a significant difference between boys and girls in cognitive strategy use (p<0.01).

The findings showed that girl students were significantly better in cognitive

strategy use and made greater use of rehearsal, elaboration and organizational

strategies in their maths class.

Discussion:To answer the question related to gender difference in cognitive strategy

indicated that girl students clearly used more cognitive strategy in the

mathematics classroom than boys.

According to Jain and Arora (1995), performance of girls increased with

higher percentage of female teacher in schools. It was also noticed that out of

the 33 teachers in this study, only two teachers were male and the remaining

were females. It is probable that the effect of the teachers’ sex on the pupil led

to these results. It is possible that when girls are taught by female teachers

especially in maths, they are likely to do better owing to cordial relationship and

a possibility of perceiving such teachers as their role models. This may further

create interest and make them more engaged. Our findings paralleled the

findings of (Peklaj & Pecjak, 2002; Pokey and Blumenfeld, 1990; Wolter &

Pintrich, 1998; Zimmerman and Martinez-Pons (1990); Ablard and Lipschultz,

1998; Russillo and Arias, 2000).

According to Russilo and Arias (2000), girls showed greater responsibility

for their academic failure, together with greater use of significant strategies.

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This fact is associated with the girls, because they use more of cognitive

strategies and self-regulation.

Although some research mentioned explanation for gender differences

favoring girls in use of cognitive strategy in maths class is that, a mathematics

class is typically thought of as being an empirical, problem solving, and

analytical school subject. At its best, maths is just that. However, maths classes

at middle school level have been traditionally taught with methods characteristic

of the language arts, methods that transform maths into little more than a

reading class. Too often, the bulk of instruction is delivered through a lecture

format, with student assignment consisting of reading, memorizing vocabulary,

and answering comprehension questions (National Research Council, quoted

by Pajares, 1996). This type of instruction, with dependence on using only

textbook and just writing numbers on blackboard without any explaining

writting, remove the conceptual linkage with mathematics instruction. But the

finding of this study does not agree to this point. Because if language art is a

domain in which girls excel, and middle school maths is taught primarily with

methods more characteristic of language arts than maths, then it would have

been observed in the most of the researches. Most of the researcher such as

mentioned above believed that girl students used more cognitive strategy than

boys.

Another possible explanation might be related to this point that cognitive

strategy and self-regulation were closely related to each other (see table 4.2).

When girl students used cognitive strategies to learn, they can regulate their

behavior and also can effort management. There is reciprocal relationship

between these two variables. So, self-regulation also influenced cognitive

strategy used. The findings revealed that girl students used more cognitive

and self-regulated strategies.

4.6.2 The second sub-question in the fifth criterion sought to find out whether

self-regulation differs in boys and girls students?

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The findings were as shown below:

Table 4.19 : Gender difference in self-regulation

Variable Gender N Mean df t sigSelf-regulationEqual Variances assumed

Boys

Girls

518

502

4.92

5.14

1018 -3.90 0.000

The findings of the table above indicated that the calculated t (3.90) was greater

than the tabulated t (2.58) at 0.01 percent level of significance hence there was

significant difference in self-regulation based on gender. The findings showed

that girl students were significantly better in self-regulation and meta-cognitive

strategy use (p<0.01). The conclusion drawn was that girls used more self-

regulated strategy and persisted more at difficult or uninteresting academic tasks.

The above findings are also supported by the figure below:Figure 4.6: Gender difference in motivational and self-regulated learning

components

Discussion:

131

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self-e

fficac

y

intrin

sic va

lue

test a

nxiet

y

cogn

itive s

trateg

y

self-r

egula

tion

Sex

Mea

n malefemale


The findings related to self-regulation showed that there was significant

difference between boys and girls in self-regulation at 0.01 percent level of

significance. This study concludes that girl students used more self-regulated

strategies and they were more persistent on their difficult task, and also were

better in effort management. In other words, girl students were more self-

regulated, and used more of meta-cognitive strategies, because they were

focused on planning, monitoring, and controlling their cognition. Our results

showed that girl students knew better their difficulties in mathematical subject.

Girls also had stronger self-efficacy for self-regulation. Students’

judgments of their capability to use various self-regulated learning strategies

have been found to predict achievement in mathematics (Pajares, 1996;

Pajares et al., 1999). Students’ confidence in their self-regulatory strategy have

also been linked to greater strategy use, higher intrinsic motivation, more

adaptive attribution, and academic achievement (Pintrich &DeGroot,1990). It is

thus not surprising that the greater maths self-efficacy of the girls in this present

sample were accompanied by higher self-efficacy for self-regulation.

One of the other interpretations is related to the position that the girls have

in most of the developing countries such as India. After the advent of industrial

revolution and the consequent technological changes and development,

education and socio-economic necessities led to the change in the attitude of

women, socio-economic status and her role in the society. But still there are

differences. Most of the opportunities favor men more than women. Girl

students in a middle school have to perceive these discrepancies and work

hard at overcoming them. This has been evident where girls have excelled in

their education, demonstrated high use of their skills, abilities and leadership

qualities.

As noted by Slavin (2006) there are no significant differences in

intellectual ability between girls and boys. That is true. But in adolescence, girls

enter puberty sooner than boys, which leads to some differences between the

genders within their puberty years. Girls’ thoughts change and they get

mentally mature earlier than boys and are psychologically better able to deal

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with their problems. Therefore, girls understand their future better and they try

harder than boys.

4.7 Research question 6: Is their an influence of parents’ education on their

children’s’ motivational beliefs components?

By use of one-way analysis of variance (ANOVA), a comparison was

made between each variable: self-efficacy, intrinsic value and test anxiety. The

researcher classified education of mothers and fathers into six categories. As

shown in table 4.20:

Table 4.20: Education of Parents as categorized

Code Education of Parents

1 School certificate, SSC

2 Junior college passed 10 + 2

3 General degree: Arts, Commerce

4 Professional degree: B. Ed., Eng.

5 Post graduate

6 Not given

Code number six was assigned for students who didn’t know their parents

education and didn’t answer the item (see Table 4.20 above).

Table 4.21 and 4.22 presents the means, standard deviations and number

of students in each categorize:4.21: Mean, Std. D and N of students of each category of motivational components based on fathers’ education

GroupsDescriptive data SE.S IN.V.S TE.AN.S

1.00 Mean 4.8401 5.6653 4.1555 N 82 82 82 Std. Deviation .92058 .63613 1.43407

133


2.00 Mean 5.0881 5.6947 3.9212 N 111 111 111 Std. Deviation .84302 .73374 1.422223.00 Mean 5.1448 5.7127 3.7770 N 333 333 333 Std. Deviation .93266 .86211 1.461104.00 Mean 5.4078 5.9011 3.1882 N 255 255 255 Std. Deviation .81680 .89006 1.465105.00 Mean 5.2853 5.7731 3.4449 N 118 118 118 Std. Deviation .80576 .83646 1.540466.00 Mean 5.1350 5.7098 3.5537 N 121 121 121 Std. Deviation .74317 .70527 1.60734Total Mean 5.1950 5.7607 3.6110 N 1020 1020 1020 Std. Deviation .87080 .82221 1.51022

Table 4.22: Mean, Std.D and N of students of each category of motivational components based on mothers’ education

Groups Descriptive data SE.S IN.V.S TE.AN.S

1.00 Mean 5.0507 5.7173 4.0000 N 114 114 114 Std. Deviation .80384 .64419 1.461152.00 Mean 5.1491 5.7271 3.9759 N 114 114 114 Std. Deviation .94325 .90559 1.525583.00 Mean 5.1857 5.7367 3.6068 N 365 365 365 Std. Deviation .90609 .84297 1.508524.00 Mean 5.3771 5.9580 3.1949 N 127 127 127 Std. Deviation .84818 1.04014 1.464845.00 Mean 5.3160 5.8044 3.3073 N 96 96 96 Std. Deviation .85206 .78844 1.381976.00 Mean 5.1476 5.7032 3.5993 N 204 204 204 Std. Deviation .80627 .66186 1.53197Total Mean 5.1950 5.7607 3.6110 N 1020 1020 1020 Std. Deviation .87080 .82221 1.51022

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Table 4.23 and 4.24 show significant differences existed in variables (except

intrinsic value) based on fathers and mothers’ education.

Table 4.23: Analysis of variance of motivational components based on fathers’ education

SS df MS F SigSelf-efficacy Between Group Within Group Total

25.38747.31772.7

510141019

5.0770.737

6.88** .000

Intrinsic value Between Group Within Group Total

7.354681.52688.87

510141019

1.4710.672

2.18 .053

Test anxiety Between Group Within Group Total

93.392230.72324.1

510141019

18.6792.200

8.49** .000

*p<0.05 **p<0.01 Table 4.24: Analysis of variance of motivational components based on mothers’ education

SS df MS F SigSelf-efficacy Between Group Within Group Total

8.71763.98772.7

510141019

1.740.753

2.31* .042

Intrinsic value Between Group Within Group Total

6.35682.51688.87

510141019

1.270.632

1.88 .094

Test anxiety Between Group Within Group Total

63.32260.82324.11

510141019

12.662.23

5.67** .000

*p<0.05 **p<0.01

4.25: Critical Value of the F Distributiondf 0.05 level 0.01 level5, 1014 2.21 3.02

4.7.1 The first sub-question of this study sought to find out whether parents’

education influences self-efficacy of students.

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Findings from table 4.23 indicated that the obtained F value for self-

efficacy (6.88) was greater than the degree of freedom (df) at 0.01 percent level

of significance since the critical point was 3.02. Hence, there was significant

difference between students related to fathers’ education in self-efficacy. Based

on mothers’ education, the obtained F value for self-efficacy (2.31) was greater

than the critical value 2.21 with 5 and 1014 degrees of freedom at 0.05 percent

level of significance. This study therefore concluded that there was significant

difference between self-efficacy of students based on their parents’ education.

Discussion:The finding revealed that the influence of fathers’ education (F= 6.88,

p<0.01) on students’ self-efficacy was higher than mothers’ education (F= 2.31,

p<0.05). The finding of the present study is consistent with Hortacsu (1995)

who mentioned that level of fathers’ education was related to child perceptions

of efficacy.

Obviously, development of perceived efficacy starts during early childhood

and initial efficacy experiences are centered in the family. As mentioned by

Bandura (1997), Parents and other caregivers provide experiences that

differentially influence self-efficacy. Home variables that help children interact

effectively with the environment, influence cognitive development and self-

efficacy. Schunk and Pajares (2002) believed that Initial self-efficacy sources

were centered in the family, but the influence was bi-directional. Parents who

provided an environment that stimulated curiosity and allowed for mastery

experiences helped build children's self-beliefs. In turn, children who displayed

more curiosity and exploratory activities promoted parental responsiveness.

When environments are rich in interesting activities that arouse children's

curiosity and offer moderate challenges, children are motivated to work on the

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activities and learn new information and skills. Home environments vary greatly.

Some contain many resources that stimulate children's thinking; parents may

heavily invest in their children's cognitive development and spend time with

them on learning. Other homes do not have these resources and adults may

devote little time to children's education.

Parents, who provide a warm, responsive and supportive home

environment, encourage exploration, stimulate curiosity, and provide play and

learning materials, accelerate their children's intellectual development (Meece,

1997).

Parents also are key providers of self-efficacy information. Parents who

arrange for varied mastery experiences develop more self-efficacious

youngsters than do parents who arrange fewer opportunities (Bandura, 1997).

Such experiences occur in homes enriched with activities and in which children

have freedom to explore. With respect to vicarious sources, parents who teach

children ways to cope with difficulties and model persistence, their efforts

strengthen children's efficacy. With development, the role of peers becomes

increasingly important. Parents who steer their children toward efficacious

peers provide vicarious boosts in self-efficacy. Homes also are prime sources

of persuasive information. Parents who encourage their youngsters to try

different activities and support their efforts, help to develop children who feel

more capable of meeting challenges (Bandura, 1997). Self-efficacy suffers in

homes where new activities are not encouraged. So, parents who had a higher

level of education, provide children with challenging tasks and meaningful

activities that can be mastered, and help ensure the development of a robust

sense of self-worth and self-confidence.

4.7.2 Second sub-question was related to the influence of parents’ education

on intrinsic value of students.

As table 4.23 has shown, the F value for intrinsic value was (2.18), that is

less than the degrees of freedom (df) at 5 percent level, hence it is not

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significant at 0.05 level based on fathers’ education. Analysis of variance based

on mothers’ education showed that the obtained F value for intrinsic value

(1.88) was less than the degrees of freedom (df) at 5 percent level of

significance since the critical value was 2.21. As such, the study held that there

was no significant difference on intrinsic value of the students based on

mothers’ education. Naturally the conclusion was that the parents’ education

did not influence students’ interest in the task.

Discussion:Although, this motivational component (intrinsic value) is very important to

students’ reasons for doing a task, it is a possibility that the parents’

expectations led to extrinsic value in their children. Some parents encourage

their children’s to get good marks and sometimes they promise them to buy this

or that if they try to be the best than others in the class. These parents

unconsciously foster extrinsic value for their children. Furthermore, many

typical classroom contexts, especially at the secondary and college levels, tend

to emphasize extrinsic value or performance goals. According to Ryan and

Deci (2000b) extrinsic motivation are powerful forces in children’s life and often

can be used effectively to engage children in different learning activities. There

is concern that an over reliance on them can interfere with children’s intrinsic

motivation under certain conditions. So findings of this study showed that

parents’ education could not influence students’ interest in the task. Home

environment has impact on children’s interest and their attraction for learning

but the result of this study did not show this importance.

Children may be more strongly motivated in one particular area such as

mathematics than they are in another area like reading. There are likely to be

individual differences in such patterns, but regarding interest, elementary and

middle school age children say that they are most interested in social and

sports activities, and less so in mathematics and reading (Wigfield et al.,

1997). So it may be possible that during middle school age, students attract to

own interest and parents less influenced on them.

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Available evidence has shown that value of the task for learners does not

depend only on home factors, but it is influenced by many other factors such as

school factors and teacher’s characteristics.

4.7.3 The third sub-question was related to influence of parents’ education on

students’ test anxiety.

The obtained F value (8.49) was greater than the critical value 3.02 at

0.01 percent level of significance. Hence, there was significant difference in test

anxiety of students based on fathers’ education. This study therefore concluded

that fathers’ education influenced test anxiety of the students.

The findings related to mothers’ education indicated that the obtained F

value (5.67) was greater than the degrees of freedom (df) at 0.01 percent level

of significance since the critical value was 3.02. As such, the study held that

there was significant difference on test anxiety of the students based on

mothers’ education. It means, mothers’ education influenced test anxiety of the

students. This study therefore concluded that there were negatively relation

between test anxiety of the students and their parents’ level of education. As

parents’ education increased, test anxiety of their children decreased.

Comparison of Mean scores of students based on fathers’ education (see

table 4.21) and mothers’ education (see table 4.22) indicated that students who

belonged to parents with higher levels of education, had higher self-efficacy,

and intrinsic value and they had lower test anxiety.

To determine which groups had differences that were close to each other

or varied very much, Post Hoc Tests were computed. The findings showed that

in Self-efficacy variable, based on fathers’ education, number categories had

significant differences. The differences related to categories are explained

below:

Self-efficacy: The findings indicated that there were significant differences

in students with their fathers’ education as School certificate [SSC] when

compared with all students whose fathers’ education was General degree,

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Professional degree and Post graduation. Students whoes fathers’ education

was Junior college or 10 + 2, had significant differences with students with their

fathers’ education as Professional degree: B. Ed., Eng, etc. Students with their

fathers’ education as graduation had significant difference with students whose

fathers’ education was School certificate and Professional degree. Students

with their fathers’ education as Professional degree had significant difference

with students whose fathers’ education was School certificate, Junior college

and General degree. The final significant difference was found between

students whose fathers’ education was Post graduation and students with

parents’ education up to School certificate. The other aspects regarding

education of parents and the three components of motivational beliefs are

provided in appendix B, owing to their details.

Discussion:Comparison of mean scores of students related to test anxiety with

different levels of parents’ education showed that parents with high level of

education had children with lower anxiety. Obviously, parents with high level of

education can better understand and know feelings of their children, so they

can guide them perfectly and support them to face challenge at the time of

anxieties. Also, child’s anxieties, interest and feeling of confidence are inversely

related to unfair system maintenance, meaning that when rules and boundaries

are clear, stable and predictable, levels of anxiety are lower, while highly

inconsistent parental behavior may increase anxiety. Children who perceive the

rules in their family as ambivalent may feel uneasy and see their parents as

supporting or protecting them less. Parents who are aware of the importance of

education or they have high level of education, have clear rules to cultivate

them in their children.

As children enter into adolescence, their family relationships and

particularly the interactions with parents undergo significant changes. Most

adolescents report difficulty in communicating with parents. Conflicts with family

members operates as one of the powerful stressors on adolescents as they are

inclined to interact with parents now much less than earlier, and disclose less

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information to them. The perceived parental control in Indian adolescence has

found to very with age, the religious group, parents’ professions, etc. Girls

reported more areas of co-operation than conflicts with parents (Ojha, 1988,

quoted Khosravi, 2005). Conflicts between parents and adolescents are

stressful and have a negative bearing on adolescent functioning. Parents who

know and perceive the growing stage of the children can provide a calm

environment for them.

4.8 Research question 7: Is their an influence of parents’ education on

their children’s self-regulated learning components?

To answer this question also, One-way Analysis of Variance was used.

The researcher classified education of mothers and fathers into six categories

as mentioned for previous question. According to these components, the

influence of parents’ education on academic achievement was also evaluated.

Table 4.26 and 4.27 presents the means, standard deviations and number of

students in each categorize:

4.26: Mean, Std. D and N of students of each category of self-regulated components and academic achievement based on fathers’ education

Groups Descriptive data

COG. St S. Reg ACAD.ACH

1.00 Mean 5.1116 4.6599 51.8067 N 82 82 82 Std. Deviation .89610 .82971 17.306072.00 Mean 5.1712 4.7267 58.8186 N 111 111 111 Std. Deviation .81181 .95285 18.101023.00 Mean 5.2439 5.0087 62.1315 N 333 333 333 Std. Deviation .77142 .91385 19.721244.00 Mean 5.4404 5.3059 70.9633 N 255 255 255

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Std. Deviation .73976 .87526 17.306895.00 Mean 5.3924 5.0395 61.4542 N 118 118 118 Std. Deviation .75425 1.01654 19.487346.00 Mean 5.2784 5.0560 64.3288 N 121 121 121 Std. Deviation .80398 .93262 18.47397Total Mean 5.2958 5.0334 63.3312 N 1020 1020 1020 Std. Deviation .78636 .93657 19.30872

4.27: Mean, Std. D and N of students of each category of self-regulated components and academic achievement based on mothers’ education

Groups Descriptive data COG. St S. Reg ACAD.ACH1.00 Mean 5.1673 4.8090 55.8882 N 114 114 114 Std. Deviation .80367 .81093 20.087092.00 Mean 5.2389 4.9396 58.6193 N 114 114 114 Std. Deviation .77785 .94259 19.970523.00 Mean 5.3313 5.0636 65.2427 N 365 365 365 Std. Deviation .77899 .92379 18.754434.00 Mean 5.4930 5.2388 67.3383 N 127 127 127 Std. Deviation .75961 .99932 18.915945.00 Mean 5.3373 5.1667 65.3980 N 96 96 96 Std. Deviation .74474 1.01336 20.047936.00 Mean 5.1934 4.9668 63.2363 N 204 204 204 Std. Deviation .80795 .91527 17.93833Total Mean 5.2958 5.0334 63.3312 N 1020 1020 1020 Std. Deviation .78636 .93657 19.30872

Table 4.28 and 4.29 show significant differences existed in variables

(cognitive strategy, self-regulation and also academic achievement) based on

fathers and mothers’ education.

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Table 4. 28: Analysis of variance of self-regulated components and academic achievement based on fathers’ education

SS df MS F SigCognitive S Between Group Within Group Total

11.87618.23630.1

510141019

2.3750.610

3.89** .002

Self-regulation Between Group Within Group Total

41.08852.75893.83

510141019

8.2160.841

9.77** .000

Academic Between Group achievement Within Group Total

29019.8350890.7379910.5

510141019

5803.96346.04

16.77** .000

*p<0.05 **p<0.01 Table 4.29: Analysis of variance of self-regulated components and academic achievement based on mothers’ education

SS df MS F SigCognitive S Between Group Within Group Total

9.95620.14630.1

510141019

1.990.61

3.25** .006

Self-regulation Between Group Within Group Total

15.05878.78893.83

510141019

3.010.86

3.47** .004

Academic Between Group achievement Within Group Total

12631.13367279.42379910.55

510141019

2526.22362.2

6.97** .000

*p<0.05 **p<0.01

4.8.1 The first sub-question of seventh question sought to find out whether

there was an influence of parents’ education on their children’s cognitive

strategy use?

The findings based on fathers’ education indicated that the obtained F

value (3.89) was greater than the degrees of freedom (df) at 0.01 percent level

of significance since the critical value was 3.02. As such, the study held that

there was significant difference in cognitive strategy use of the students as

related to their fathers’ education. As such, this study concluded that fathers’

education influenced children’s’ cognitive strategy use.

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The findings from mothers’ education showed that the obtained F value

(3.25) in cognitive strategy was evidently greater than the critical value 3.02 at

0.01 percent level of significance. Hence there was significant difference in

cognitive strategy use of students related to their mothers’ education. This

study concluded that parents’ education influenced cognitive strategy of their

children.

4.8.2 The second sub-question was sought to find out whether there was an

influence of parents’ education on their children’s self-regulation.

The findings from the analysis of the variance based on fathers’ education

showed that the obtained F value (9.77) was greater than the degrees of

freedom (df) at 0.01 percent level of significance since the critical value was

3.02. Hence there was significant difference in self-regulation of the students

related to their fathers’ education.

The obtained F value (3.47) from the mothers’ education was greater than

the critical point 3.02 at 0.01 percent level of significance. This study concluded

that parents’ education was a determining factor in the use of self-regulation

strategy of their children.

The findings related to academic achievement of the students, also

showed that the obtained F value in fathers’ education was (16.77) and F value

for mothers’ education was (6.97), so, F value for this variable were greater

than the degree of freedom (df) at 0.01 percent level of significant since the

critical value is 3.02. Hence there was significant difference between the

students in their academic achievement based on parents’ education.

Mean scores of students for cognitive strategies and self-regulation and

academic achievement in different levels of their parents’ education showed

that students who had parents with higher levels of education, used more

cognitive strategy and self-regulatory strategies and had better academic

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achievement than other students (see table 4.26 and 4.27).The difference

between groups of students with different levels of parents’ education was

found to be significant as calculated by Post Hoc Test ( see appendix B, owing

to their details).

Discussion:

The findings related to cognitive strategies and self-regulation indicated

that parents’ education highly influenced on cognitive strategy use and self-

regulation to their children. As expected parents who had high education, were

aware of the importance of education and provided facilities for learning of their

children. These parents managed time and place for their children’s study and

encouraged their children for better learning. These findings have accorded

with the findings of Ganzach (2000); Stich & McDonald (1990) which showed

mothers’ education influenced their children’s cognitive skills and success in

schools. This study also showed that both fathers and mothers’ education had

important roles in cognitive and meta-cognitive strategy use by their children.

When students grow up in the educated families they can learn some skills and

have motivation to learn, they will have better performance in their subject

matters.

The present finding indicated that parents’ education was highly related

to academic performance of their children. This result is similar to those

reported by Govinda and Varghese (1993); Sexena et al., (1996); World Bank

(1997); Varghese (1995); Schukala et al. (1994). Furthermore, the finding is in

contrast to the finding of some researchers who believed home factors were not

important in students’ achievement in India such as Heyman and Loxley (1982).

The finding of Heyneman and Loxley’s (1982) dramatic conclusion was

that in India school factors have by far the most predominant influence on

students’ achievement and that home factors count for little. This study favored

the conclusion that home background such as parents’ education is an

important influence on children’s performance and cognitive processes.

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Although this study did not search the influence of teachers on students’

motivation and cognition, but on per other researchers obviously school factors

such as teachers’ behavior is a very important factor for establishment of

motivation and cognitive components. Parents and teachers who provide

children with challenging tasks and meaningful activities that can be mastered,

and who chaperone these efforts with support and encouragement, help ensure

the development of a robust sense of self-worth and of self-confidence. Early

mastery experiences are predictive of children’s cognitive development and

there is evidence to suggest they work independently of critical variables such

as socio economic status.

Findings of this study revealed that parents’ education as a home factor

was an important factor in motivation, cognition and academic achievement of

the students.

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chapter 4: results and discussion: 4.1...

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