Types Of Language Learning Strategies Education Essay

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This chapter focuses on the findings obtained from the data collected through the survey. Respondents of the survey were the ADFP and ACTP students of the American Degree Programme in INTEC, UiTM Shah Alam. The data collected were analyzed using the SPSS software package version 16.0. The findings are presented based on the research questions in chapter 1:

What are the learning strategies used by the respondents?

What is the level of college self-efficacy among the respondents?

What is the level of academic achievement among the respondents?

What is the relationship between learning strategies and self efficacy on

academic achievement?

What is the contribution of each variant of independent variable towards

academic achievement?

Table 4.1

Demographic Background of respondents according to gender and

ethnicity (n=285)

Respondents Profile Frequency (n) Percentage (%)


Male 162 56.8

Female 123 43.2


Malay 138 48.4

Chinese 91 31.9

Indian 31 10.9

Others 25 8.8

Total 285 100

Table 4.1 presents the demographic data of the respondents involved in this study. More male students participated in the study with a percentage of 56.8% compared to 43.2% who were female students. On another category of ethnicity, Malay students were the main respondents in this study with a percentage of 48.4% while Chinese students comprised about 31.9% of the total sample. Another 10.9% of the respondents are of Indian ethnicity while the final 8.8% are of other ethnics.

Table 4.2

Descriptive Analysis of Types of Language Learning Strategies

Types of Language Learning Mean Std. Deviation


Memory Strategies 2.8612 0.5866

Cognitive Strategies 3.4639 0.4853

Compensation Strategies 3.4515 0.6241

Metacognitive Strategies 3.5789 0.6301

Affective Strategies 2.8117 0.6833

Social Strategies 3.6439 0.6924

Table 4.2 presents the data on the types of language learning strategies used by the respondents. The findings show that most respondents use Social Strategies (M= 3.6439, SD= 0.692411) followed by Metacognitive Strategies (M= 3.5789, SD= 0.63011) and finally Cognitive Strategies (M= 3.4639, SD= 0.48529).

From the findings, it can be inferred that the respondents benefit the most from using social strategies, metacognitive strategies and cognitive strategies in their process of language learning. This means that in terms of using social strategies, the respondents learn language best through asking questions in class, cooperating with others who are proficient in the language and empathizing with others for example, through developing cultural understanding. In other words, these respondents learn best when socializing with others in the target language.

The findings also revealed that the respondents who uses metacognitive strategies. This means that respondents employing metacognitive strategies tend to center their learning for example linking new knowledge with what they already know, arranging and planning their learning and self evaluating themselves in their learning progress. In short, these learners plan out their learning progress and relate their new knowledge to previous schemata.

Respondents practicing cognitive strategies in learning the target language tend to use practices for example using formulas and patterns or focus on the main idea of a message when reading a text. These learners are also prone to do a lot of analysis and make reasoning for example by analyzing expressions and finally create structure in terms of either receiving input or output for example taking notes.

Table 4.3

Descriptive Analysis of Domains of College Self Efficacy

Domains of Mean Std. Deviation

College Self Efficacy

Course Self Efficacy 6.9464 1.3234

Roommate Self Efficacy 7.6044 1.2662

Social Self Efficacy 6.8097 1.3726

The findings in table 4.3 shows that respondents have high self efficacy when dealing with roommate self efficacy (M= 7.6044, SD= 1.2662) followed by course self efficacy (M= 6.9464, SD= 1.3234) and social self efficacy (M= 6.8097, SD= 1.3726). The findings indicate that the respondents are more confident in associating with their roommates and completing task related to their studies. However social wise, the findings shows that the respondents are less confident about themselves socializing in major faculty events or in their interpersonal skills with others such as making new friends.

Table 4.4

Distribution and Percentage of Respondents' Cumulative Grade Point Average (CGPA)

Cumulative Grade Frequency (n) Percent (%)

Point Average (CGPA)

Low (< 2.49) 2 7

Moderate (2.50 - 3.49) 217 76.1

High (3.50 - 4.00) 66 23.2

Total 285 100

Table 4.4 reports on the level of academic achievement of the respondents. From the data, it shows that a majority of the respondents have average academic achievement with a percentage of 76.1% ranging from 2.50 - 3.49. 23.2% of respondents have high CCPA ranging from 3.50 - 4.00. The remaining 7% have low academic achievement ranging from less than 2.49. This findings show that the majority of respondents from the American Degree Programme have moderate range of CGPA.

Table 4.5

Correlation Matrix between Types of Language Learning Strategies on Academic Achievement

Language Learning Strategies

Memory Strategies -0.236**

Cognitive Strategies 0.098

Compensation Strategies 0.082

Metacognitive Strategies 0.092

Affective Strategies -0.324**

Social Strategies 0.130*

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

Table 4.5 shows the relationship of language learning strategies on academic achievement. By using Pearson Correlation to determine strength of the relationship between the independent variables and academic achievement, it was found there are three strategies that show correlation with academic achievement which are associated with academic achievement. Those language learning strategies are Memory Strategies, Affective Strategies and Social Strategies.

The relationship between Memory Strategies, Affective Strategies and academic achievement shows a negative and very weak relationship with their r and p values (r= -0.236 p= 0.000, r= -0.324 p= 0.000) respectively. This suggests that the more the respondents use both Memory and Affective Strategies in their language learning, the lower their academic achievement would be. On another note, Social Strategies indicate a positive but very weak correlation with respondents' academic achievement with its r and p value at r= 0.130, p= 0.029. This suggests that the more respondents use Social Strategies in their language learning, the better they perform academically.

Table 4.6

Correlation Matrix between Domains of College Self Efficacy on Academic Achievement

College Self-Efficacy

Course Self Efficacy 0.226**

Roommate Self Efficacy -0.031

Social Self Efficacy 0.151*

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

Table 4.6 reports on the correlation on domains of college self efficacy with respondents' academic achievement. Both Course Self Efficacy and Social Self Efficacy show that there is a positive yet weak and very weak relationship between the two variables on academic achievement with their r and p values (r= 0.226 p= 0.000, r= 0.151 p= 0.011) respectively. This result suggests that similar of Social Strategies indicating that the higher the respondents' self efficacy in terms of Course and Social, the better the respondents would perform academically.

Table 4.7

An analysis of Multiple Regression on Academic Achievement

To determine the contribution of each independent variable towards academic achievement, the ENTER method of multiple regression analysis was employed. To identify the predictors of academic achievement, the subscales from each domains' multiple linear regression was proposed. The nine subscale predictors are Memory Strategies (x1), Cognitive Strategies (x2), Compensation Strategies (x3), Metacognitive Strategies (x4), Affective Strategies (x5), Social Strategies (x6), Course Self Efficacy (x7), Roommate Self Efficacy (x8) and Social Self Efficacy (x9). The equation of the proposed multiple linear regression model are as follows (equation 1):



b0 + b1x1 + b2x2 + b3x3 + b4x4 + b5x5 + b6x6 + b7x7 + b8x8 + b9x9 + e

Equation 1


b0 = Intercept

b1-4 = Slopes (Estimates of Coefficients)

Y1 = Academic Achievement

x1 = Memory Strategies

x2 = Cognitive Strategies

x3 = Compensation Strategies

x4 = Metacognitive Strategies

x5 = Affective Strategies

x6 = Social Strategies

x7 = Course Self Efficacy

x8 = Roommate Self Efficacy

x9 = Social Self Efficacy

e = Random Error

Variables Un-Standard Standard t Sig. (p)

Coefficients Coefficients


(Constant) 3.105 17.655 0.000

Memory -0.153 -0.270 -4.354 0.000


Cognitive 0.049 0.071 1.001 0.318


Compensation 0.021 0.040 0.730 0.466


Metacognitive 0.058 0.111 1.589 0.113


Affective -0.159 -0.328 -5.609 0.000


Social 0.063 0.132 2.080 0.038


Course Self 0.059 0.237 3.806 0.000


Roommate Self -0.027 -0.102 -1.697 0.091


Social Self 0.016 0.066 0.998 0.319


F Statistic = 11.191

Adjusted R-squared = 0.244

R2 = 0.268

Based on the ENTER method which is presented in Table 4.7, the results show that there are two predictor variables that were found significant towards academic achievement. The two predictors are Affective Strategies (x5) and Course Self Efficacy (x7) with their t and p values respectively (t= -5.609 p= 0.000, t= 3.806 p= 0.000). In order to seek the relative importance of both predictors in predicting academic achievement, the standardized regression between coefficients were also shown in Table 4.7. Standardized coefficients play an important role for comparative purposes as the values of the different variables have been converted to the same scale.

In this multiple regression, both dependent and independent variables were standardized to have a mean of 0 and a standard deviation of 1. Thus, when an independent variable gives a high beta coefficient, there is an indication that the variable is highly important in contributing to the prediction of the criterion variable. Hence, based on the values reported in the table, the highest beta coefficient was derived from Affective Strategies with a value of -0.328. This indicates that Affective Strategies was the strongest contributor to the overall equation. This variable was followed by Course Self Efficacy with a beta coefficient of 0.237.

To conclude, the multiple regression model for academic achievement in standard score units is presented as following:



3.105 + 0.159x5 + 0.059x7 + e

Equation 2


Y1 = Academic Achievements

x5 = Affective Strategies

x7 = Course Self Efficacy

e = Random Error

Table 4.7 also shows the coefficient of determination where R-squared, is the value that indicates the percentage of the total variation of dependent variables that are explained by the independent variable. Thus, as presented in Table 4.7, the total amount of variance of criterion variable that is predictable from the two predictors are 26.8%, and the adjusted R-square change of 24.4%.

The adjusted R-square gives a better estimation of the true population value, thus the contribution of the predictor variables towards the variance in the criterion variable in this study are reported based on the adjusted R-square value. Therefore, the overall regression model has been successful in explaining approximately 24.4% of the adjusted variance in academic achievements.

In short, only two variables were found to be significantly linked to academic achievements at a significant level of 0.05. Those two variables are as reported which are Affective Strategies and Course Self Efficacy. Both Affective Strategies and Course Self Efficacy were found to have a significant relationship with academic achievement. Thus based on the multiple regression analysis, the results show that Affective Strategies and Course Self Efficacy account for 24.4% which explains the variance of academic achievement.