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investigating the level of teachers’ knowledge and performance, and whether a link exists between English teachers’ knowledge and performance. It also aimed to predict teachers’ performance through their knowledge. This is examined through the data provided by the knowledge and performance questionnaires.
4.2 Data Analysis
After the data were collected with the aid of two questionnaires, the next phase of the research was to analyze those data. In the data analysis phase the researcher used some techniques. Teachers responded to knowledge and performances questionnaires ranged from strongly disagree to strongly agree which were scored from 1 to 5. The responses of participants to each questionnaire were tallied up and a comprehensive list of all responses of the participants was compiled. Then descriptive statistics (frequencies and percentages) were computed for each of the categories. The correlation was also considered as descriptive statistical procedure and used to examine existing relationship between two variables (knowledge and performance). Using the regression, the researcher could also predict the amount of variance in the dependent variable (teachers’ performance) attributable to a number of independent variable (teachers’ knowledge).
4.2.1Testing the Research Hypotheses
4.2.1.1 Hypothesis 1
H1: The language teachers are not well-equipped at knowledge level in Iran.
The first research question focused on the level of knowledge in Iranian foreign language teachers.
According to the data in Table 4.1, the mean of English teachers’ knowledge score is 3.27, its SD is .22, its SE is .03; its minimum score is 2.91 and its maximum score is 3.69.
Table 4.1
Descriptive Statistics: Distribution of English Teachers’ Knowledge Score
Mean
N
Std. Deviation
Std. Error Mean
95% CI for M
Lower Upper
3.27
30
.22
.037
3.19 3.35
As all the descriptive statistics of English teachers’ knowledge scores are frequencies, the two-way chi-square analysis is needed to test the normality of the frequency by comparing the observed frequency with the expected or theoretical frequency.
Table4.2
Contingency Table for the Knowledge Two-Way Chi-square Analysis
Yates’ Chi-square
Chi-square
Degree of
freedom
P-value
Sum
Strongly agree
Agree
Neither agree nor disagree
Disagree
Strongly disagree
289.84
294.05
8
1050
169
460
226
161
32
Teachers’ Answers
Based on Table 4.2, the knowledge chi-square is 294.05 and Yates’ Chi-square is 289.84; the knowledge chi-square is more than Yates’ Chi-square where the degree of freedom is 8and P-value is 0. It means that the observed statistic is greater than the critical statistic. So the observed knowledge frequencies deviate sufficiently from what would be expected by chance alone and is statistically significant at the probability level of (P= 0).
Therefore, the knowledge chi-square is more than Yates’ Chi-square (df = 8, P= 0), it means that the observed statistic is greater than the critical statistic and there is 100% probability that the observed knowledge frequencies were due to factors other than chance and the first null hypothesis is rejected. Teachers’ knowledge is well above the average and the language teachers are well-equipped at knowledge level in Iran.
4.2.1.2 Hypothesis 2
H2: The language teachers are not well-equipped at performance level in Iran.
The second hypothesis is focused on the level of performance in Iranian foreign language teachers.
According to the Table 4.3, the mean of English teachers’ performance score is 3.73, its Standard Deviation is .45, its Standard Error is .06; its minimum score is 3.09 and its maximum score is 4.73.
Table 4.3
Descriptive Statistics: Distribution of English Teachers’ Performance Score
Mean
N
Std. Deviation
Std. Error Mean
95% CI for M
Lower Upper
3.73
52
.45
.06
3.60 3.85
As all the descriptive statistics of English teachers’ knowledge scores are frequencies, the two-way chi-square analysis is needed to test the normality of the frequency by comparing the observed frequency with the expected or theoretical frequency.
Table 4. 4
Contingency Table for the Performance Two-Way Chi-Square Analysis
Yates’ Chi-square
Chi-square
Degree of
freedom
P-value
Sum
Strongly agree
Agree
Neither agree nor disagree
Disagree
Strongly disagree
900.54
906.33
8
1820
506
936
322
32
19
Teachers’ Answers
According to the table 4.4, the performance chi-square is 906.33 and Yates’ Chi-square is 900.54; the performance chi-square is more than Yates’ Chi-square where the degree of freedom is 8 and P-value is 0. It means that the observed statistic is greater than the critical statistic. So the observed performance frequencies deviate sufficiently from what would be expected by chance alone and is statistically significant at the probability level of (P= 0).
So, there is 100% probability that the observed performance frequencies were due to factors other than chance and teachers’ performance is well above the average. Therefore, the second null hypothesis is rejected. In other words, the language teachers are well-equipped at performance level in Iran.
4.2.1.3 Hypothesis 3
H3: There is not any relationship between teachers’ knowledge and performance level.
In this study the researcher interested in determining the degree of relationship between pairs of two variables (teachers’ knowledge and teachers’ performance). First 70 pairs of teachers’ scores on knowledge and performance were collected through the questionnaires. Their mean scores and standard deviation were calculated as Table 4.5.
Table 4.5
The Correlation Coefficient between Teachers’ Knowledge and Performance
Variables
Alpha coefficient for Reliability
Mean
Standard Deviation
Minimum
Maximum
Teachers’ Knowledge
.69
3.27
.22
2.91
3.69
Teachers’ Performance
.72
3.73
.45
3.09
4.73
Totals
1.41
7.00
.67
6.00
8.42
With the help of SPSS, the researcher correlated between items: r = .51(n = 70, p? .01). The amount of correlation coefficient is.51 and appears the positive correlation between the variables. It means that if teachers’ knowledge increases, the teachers’ performance will increase and vice verse. So the third null hypothesis is rejected and there is the relationship between teachers’ knowledge and performance level.
4.2.1.4 Hypothesis 4
H4: The teachers’ knowledge cannot predict their performance.
Table 4.6 presented the regression analysis result. The column one showed that .26 of the variance in teachers’ performance was predicted by the teachers’ knowledge (F= 62.375, P .001) .
Table 4.6
Model of Regression predicting Teachers’ performance Component
Model of Regression
Teachers’ performance
R
R Square
Adjusted R Square
Std. Error of
the Estimate
Teachers’ knowledge
62.375
.51
.260
.262
.4325
The R-square of teachers’ knowledge is 0.260 which indicates that 26 percent of the teachers’ performance can be explained by variability in teachers’ knowledge. The relation between them is positive and teachers’ knowledge is a factor which is important in predicting the teachers’ performance. Based on the results, the fourth null hypothesis is rejected and the teachers’ knowledge can predict their performance.
4.3 Discussion
4.3.1 Comparing the participants’ responses to knowledge questionnaire
The participants answered to 30 items of knowledge questionnaire. Mean scores for each of the rank categories were reported for both knowledge and performance in Appendix B. High scores of the participants’ responses to knowledge questionnaire are3.67, 3.68, and 3.67.(See Table 4.7)
Table 4.7
Statistics of Items 8,10, and 11
Score
(f)
Item8
N=70
Percent of answers to item 8
(f)
Item 10
N=70
Percent of answers to item10
(f)
Item 11
N=70
Percent of answers to item 11
1
1
1.50
2
3
2
8
11.42
6
8.57
10
14
3
16
22.8
17
24.3
12
17
4
36
51.52
40
57
33
47
5
10
14.3
6
8.5
13
19
M = 3.67 SE = .07
SD = .91 V = .83
M = 3.68 SE = .06
SD = .83 V = .69
M = 3.67 SE = .07
SD = .91 V = .83
The high mean scores are related to items 8, 10,and 11of principle 3 of INTASC model.
Principle #3: The teacher understands how students differ in their approaches to learning and creates instructional opportunities that are adapted to diverse learners.
According to findings of study we can conclude that language teachers recognize that many different factors may influence students’ approaches to language learning, including differences in learning styles and multiple intelligences; exceptional learning needs; cultural, linguistic, prior learning experiences; and personal interests, needs, and goals. Language teachers take this diversity into account when planning instruction for all learners.
Language teachers understand that students bring different linguistic and cultural backgrounds and experiences to the language classroom that influence their learning of the target language and its cultures. Language teachers are aware of a variety of student approaches to learning languages. They know that some students learn

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