Chapter 4

116

CHAPTER 4

ANALYSIS AND INTERPRETATION OF DATA

4.1 INTRODUCTION

Statistical methods involved in carrying out a study include planning, designing,

collecting data, analyzing, drawing meaningful interpretation and reporting of the research

findings. The statistical analysis gives meaning to the meaningless numbers, thereby

breathing life into a lifeless data. Analysis means the categorizing, ordering, manipulating,

summarizing of data to obtain answers to research questions. The purpose of analysis is

used to reduce data to negligible and interpretable form so that the relations of research

problem s can be studied and tested.

Interpretation takes the result of analysis, makes inference pertinent to the research

relations studied and draw conclusions about these relations.

Interpretation answers the following questions:

i. What inferences and conclusions can be drawn from the results?

ii. To what extent, the results are significant and match to the objectives of the study.

iii. Can the results solve the problem in hand?

iv. Are they able to open and new avenues of research in the field?

All these things clarify that interpretation of results is not a mechanical process, it

requires careful, logical, and critical thinking and evaluative power on the part of researcher.
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In the present study the data collected were analysed by using

1. Descriptive Analysis

2. Differential Analysis

3. Correlation Analysis

4. Regression Analysis

4.2 DESCRIPTIVE STATISTICS

Descriptive statistics provides information about the nature of particular group of

individuals. It describes the characteristics of group without drawing inferences about the

population and it also helps to describe, show or summarize data in a meaningful way.

Table 4.1 show the mean scores of digital literacy, computational thinking, and

professional commitment of primary school teachers.

Table 4.1

Mean scores of digital literacy, computational thinking, and professional commitment of

primary school teachers.

S.No. Variables Mean Score

1. Digital literacy 165.35
2. Computational Thinking 99.37
3. Professional commitment 139.01

Digital literacy - (Low= <90; Moderate= > 91 &< 180; High= ≥ 181)

Computational Thinking - (Low= ≤ 54; Moderate= > 55 &< 108; High= ≥ 109)

Professional commitment- (Low= ≤ 70; Moderate= > 71 &< 140; High= ≥ 141)
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The above table 4.1 reveals that the mean value of primary school teachers of

Coimbatore has moderate level of digital literacy (165.35), computational thinking (99.37)

and professional commitment (139.01).

Table 4.2 shows the level of digital literacy, computational thinking, and professional

commitment of primary school teachers

Table 4.2

Level of digital literacy, computational thinking, professional commitment of primary

school teachers

Variables Low Moderate High

N % N % N %
Digital literacy
21 2.75% 519 68.19% 221 29.04%

Computational Thinking 13 1.70% 557 73.19% 191 25.13%

Professional Commitment 16 2.10% 329 43.23% 416 54.66%

The above table 4.2 reveals that 2.75% of primary school teachers have low level,

68.19% of them have moderate level, and 29.04% of teachers have high level of digital

literacy. Also 1.70% of teachers have low level, 73.19% have moderate level, and 25.13%

have high level of computational thinking. This table further reveals that 2.10% of

teachers have low level, 43.23% have moderate level, and 54.66% have high level of

professional commitment.
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Level of Digital literacy,Computational

thinking,Professional commitment among
Primary school teachers
90

80
73.19
68.19
70

60 54.66

50
43.23
40
29.04
30 25.13

20

10
2.75 1.7 2.1
0
Digital literacy Com.Thinking Professional
commitment

low Moderate high

Fig 4.1 Bar Diagram shows the level of digital literacy, computational thinking, and

professional commitment of primary school teachers in percentage.

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Table 4.3 shows that the Percentage of low, moderate and high level in digital

literacy and its dimensions of the primary school teachers

Table 4.3

Percentage of low, moderate and high level in digital literacy and its dimensions of the

primary school teachers

Variables Low Moderate High

Dimensions of Digital %
N % N % N
literacy

Comprehension 26 3.41% 297 39.02% 438 57.55%

Interdependence 22 2.89% 620 81.4% 119 15.63%

Curation 22 2.89% 488 64.12% 251 32.98%

The table 4.3 shows percentage of low, moderate and high level in digital literacy

and its dimensions of the primary school teachers. According to the table, 26 (3.41%)

teachers have low level, 297 (39.02%) teachers are moderate and 438 (57.55%) teachers

have high level in comprehension of digital literacy. The above table further reveals that

22(2.89%) teachers have low level, 620(81.4%) teachers are moderate and 119(15.63%)

teachers have high level in Interdependence of digital literacy,22(2.89%) teachers have low

level, 488(64.12%) teachers are moderate and 251 (32.98%) teachers have high level in

curation of digital literacy.

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Table 4.4 shows that the Percentage of low, moderate and high level in computational

thinking and its dimensions of the primary school teachers

Table 4.4

Percentage of low, moderate and high level in computational thinking and its dimensions

of the primary school teachers

Variables Low Moderate High

Dimensions of %
N % N % N
Computational thinking

Abstraction 18 2.36% 524 68.8% 219 28.77%

Analysis 12 1.57% 434 57.03 315 41.39%

The table 4.4 shows percentage of low, moderate and high level in computational

thinking and its dimensions of the primary school teachers. According to the table,

18 (2.36%) teachers have low level, 524 (68.8%) teachers are moderate and 219 (28.77%)

teachers have high level in abstraction of computational thinking. The above table further

reveals that 12 (1.57%) teachers have low level, 434 (57.03%) teachers are moderate and

315(41.39%) teachers have high level in analysis of computational thinking.

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Table 4.5 shows that the Percentage of low, moderate and high level in professional

commitment and its dimensions of the primary school teachers

Table 4.5

Percentage of low, moderate and high level in professional commitment and its dimensions

of the primary school teachers

Variables Low Moderate High

Dimensions of Professional
N % N % N %
Commitment

Commitment to Students 21 2.75% 350 45.99% 390 51.24%

Commitment to Profession 18 2.36% 349 45.86% 394 51.77%

Commitment to Achieving
17 2.23% 368 48.35% 377 49.54%
Excellence

The table 4.5 shows percentage of low, moderate and high level in professional

commitment and its dimensions of the primary school teachers. According to the table,

21(2.75%) teachers have low level, 350 (45.99%) teachers are moderate and 390 (51.24%)

teachers have high level in commitment to student of Professional Commitment. The above

table further reveals that 18(2.36%) teachers have low level,349(45.86%) teachers are

moderate and 394(51.77%) teachers have high level in commitment to profession of

professional commitment,17(2.23%) teachers have low level, 368(48.35%) teachers are

moderate and 377 (49.54%) teachers have high level in commitment to achieving

excellence of professional commitment.

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4.3 INFERENTIAL STATISTICS

Inferential analysis is used to draw and assess the reliability of conclusions about

a population based on data collected from a sample of the population. Because inferential

analysis does not sample everyone in a population, the results will always be uncertain.

4.3.1 HYPOTHESIS TESTING

H0- 1-(i) There is no significant difference between male and female of primary

school teachers in digital literacy and its dimensions

Table 4.6

Significant difference between male and female of primary school teachers in digital

literacy and its dimensions of primary school teachers

t- p-
Dimension Variables N Mean Std Remark
value value
Male 140 46.27 7.213
Comprehension 2.352 0.019 S
Female 620 44.62 8.650
Male 140 82.25 13.448
Interdependence 2.728 0.007
Female 620 78.71 15.666 S
Male 140 40.89 6.270
Curation 0.373 0.709 NS
Female 620 41.12 8.704
Over all Male 140 169.41 24.293
2.081 0.038 S
Digital literacy Female 620 164.44 30.283
(S-Significant, NS-Not Significant, Significant at 0.05 level)

It is inferred from the table 4.6 that the calculated t-values for dimensions of digital

literacy such as comprehension and Interdependence are (2.35) and (2.72) respectively,

which are greater than the table value (1.96) at 0.05 level of significance. Hence there is
 124

a significant difference between male and female teachers in dimensions of digital literacy

such as comprehension and Interdependence. Also, the calculated t- value (0.373) for

dimension of digital literacy such as curation is less than the table value (1.96) at 0.05 level

of significance. Hence there is no significant difference between male and female teachers

in dimension of digital literacy such as curation.

The above table also reveals that the calculated t-value for overall digital literacy is

(2.08) which is greater than the table value (1.96) at 0.05 level of significance. Hence the

formulated null hypothesis H0 1 is rejected and that there is a significant difference

between male and female teachers in digital literacy.

H0-1 (ii) There is no significant difference between undergraduate and postgraduate

of primary school teachers in digital literacy and its dimensions

Table 4.7

Significant difference between undergraduate and postgraduate of primary school

teachers in digital literacy and its dimensions

t- p-
Dimension Variables N Mean Std Remark
value value
UG 293 44.09 7.615
Comprehension 2.244 0.025 S
PG 468 45.45 8.852
UG 293 77.19 14.806
Interdependence 3.140 0.002 S
PG 468 80.72 15.502
UG 293 39.85 7.784
Curation 3.299 0.001 S
PG 468 41.84 8.533
Over all UG 293 161.12 27.499
3.240 0.001 S
Digital literacy PG 468 168.00 30.103
(S-Significant, NS-Not Significant, Significant at 0.05 level)
 125

It is inferred from the table 4.7 that the calculated t-values for dimensions of digital

literacy such as comprehension, Interdependence and curation are (2.24), (3.14) and (3.29)

respectively, which are greater than the table value (1.96) at 0.05 level of significance.

Hence there is a significant difference between undergraduate and post graduate teachers

in dimensions of digital literacy.

The above table also reveals that the calculated t-value for overall digital literacy is

(3.24) which is greater than the table value (1.96) at 0.05 level of significance. Hence the

formulated null hypothesis H0 1 (ii) is rejected and that there is a significant difference

between undergraduate and postgraduate teachers in digital literacy and its dimensions.

H0-1(iii)There is no significant difference between arts and science of primary

school teachers in digital literacy and its dimensions

Table 4.8

Significant difference between arts and science of primary school teachers in digital

literacy and its dimensions

t- p-
Dimension Variables N Mean Std Remark
value value
Arts 395 44.11 8.955
Comprehension 2.803 0.005 S
Science 366 45.81 7.713
Arts 395 78.48 15.84
Interdependence 1.650 0.099 NS
Science 366 80.31 14.709
Arts 395 40.91 8.592
Curation 0.570 0.569 NS
Science 366 41.25 7.989
Over all Arts 395 163.49 30.426
1.825 0.068 NS
Digital literacy Science 366 167.36 27.941
(S-Significant, NS-Not Significant, Significant at 0.05 level)
 126

It is inferred from the table 4.8 that the calculated t-values for dimension of digital

literacy such as comprehension (2.80) which is greater than the table value (1.96) at 0.05

level of significance. Hence there is a significant difference between arts and science

teachers in dimension of digital literacy such as comprehension. Also, the calculated

t- value for dimension of digital literacy such as Interdependence and curation are (1.65)

and (0.57) respectively, which are less than the table value (1.96) at 0.05 level of

significance. Hence there is no significant difference between arts and science teachers in

dimension of digital literacy such as Interdependence and curation.

The above table also reveals that the calculated t-value for overall digital literacy is

(0.068) which is less than the table value (1.96) at 0.05 level of significance. Hence the

formulated null hypothesis H0 1 (iii) is accepted and that there is no significant difference

between arts and science teachers in digital literacy.

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H0 1 (iv) There is no significant difference in digital literacy and its dimensions of

primary school teachers with respect to their online teaching experience

Table 4.9

Significant difference in digital literacy and its dimensions of primary school teachers with

respect to their online teaching experience

t- p-
Variable Dimension Category N Mean Std Remark
value value
Yes 559 45.52 8.500
Comprehension 3.337 0.001 S
No 202 43.29 7.982
Yes 559 80.04 15.856
Interdependence 2.177 0.030 S
Online No 202 77.49 13.611
Teaching
Experience Yes 559 41.66 8.566
Curation 3.529 0.000 S
No 202 39.44 7.306
Over all Yes 559 167.20 30.235
3.133 0.002 S
Digital literacy No 202 160.22 25.935
(S-Significant, NS-Not Significant, Significant at 0.05 level)

It is inferred from the table 4.9 that the calculated t-values for dimension of digital

literacy such as comprehension, Interdependence and curation are (3.337), (2.177and

(3.529) respectively, which are greater than the table value (1.96) at 0.05 level of

significance. Hence there is a significant difference in digital literacy and its dimensions of

primary school teachers with respect to their online teaching experience.

The above table also reveals that the calculated t-value for overall digital literacy is

(3.133) which is greater than the table value (1.96) at 0.05 level of significance. Hence the

formulated null hypothesis H0 1 (iv) is rejected and that there is a significant difference in

digital literacy and its dimensions of primary school teachers with respect to their online

teaching experience.
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H0 2 (i) There is no significant difference among primary school teachers’ digital literacy and its dimensions with respect to

their years of teaching experience

Table 4.10

Significant difference among primary school teachers’ digital literacy and its dimensions with respect to their offline teaching experience

Sum of Mean
Variable Dimension Groups df F-value Sig Remark
squares square
Between groups 72.523 2 36.262
Comprehension Within groups 53785.356 758 0.511 0.600 NS
70.957
Total 53857.879 760
Between groups 2449.452 2 1224.726
Interdependence Within groups 176031.89 758 5.274 0.005 S
Offline 232.232
Total 178481.34 760
Teaching
Between groups 151.273 2
Experience 75.637
Curation Within groups 52250.895 758 1.097 0.334 NS
Total 52402.168 760 68.933
Between groups 4815.188 2 2407.594
Over all
Within groups 647718.13 758 2.818 0.060 NS
Digital literacy 854.509
Total 652533.32 760
(S-Significant, NS-Not Significant, Significant at 0.05 level)
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It is inferred from the above table 4.10 that the calculated F-values for dimension

of digital literacy such as comprehension and curation are (0.600) and (0.334) respectively,

which are less than the critical F-distribution value (3.00) at 0.05 level of significance.

Hence there is no significant difference among primary school teachers in dimensions of

digital literacy such as comprehension and curation with respect to their years of teaching

experience. Also, the calculated critical F-distribution value for dimension of digital

literacy such as Interdependence (5.274) is greater than the critical F-distribution value

(3.00) at 0.05 level of significance. Hence there is a significant difference in dimension of

digital literacy such as Interdependence of primary school teachers with respect to their

years of teaching experience.

The above table also reveals that the calculated F-value for overall digital literacy

is (2.818) which is less than the table value (3.00) at 0.05 level of significance. Hence

the formulated null hypothesis H0 2 (i) is accepted and that there is no significant difference

among primary school teachers’ digital literacy with respect to their years of teaching experience.

Table 4.10 (a)

scheffe’s post hoc test for difference among primary school teachers’ digital literacy and

its dimensions with respect to their offline teaching experience

Offline Teaching Experience Mean

5- Difference Sig Remark
Dimension 0-5yrs above 10 yrs (I-J)
10yrs
80.56 75.97 4.593 0.005 S
Interdependence 75.97 79.26 3.295 0.166 NS
80.56 79.26 1.298 0.662 NS
(S-Significant, NS-Not Significant, Significant at 0.05 level)
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The above scheffe’s post hoc analysis reveals that the mean difference between

teachers have 0-5yrs and 5-10yrs of experience is 4.593 for dimension of digital literacy

such as Interdependence and it is significant at 0.05 level of significance. And also,

the other two combinations did not show any significant difference. Hence it is concluded

that, primary school teachers are significantly differed in digital literacy and its dimensions

with respect to their off line teaching experience.

While comparing the mean scores, primary school teachers have 0-5years of

experience (80.56) are better than the teachers having 5-10years (75.97) and above 10years

(79.26) in dimension of digital literacy such as Interdependence.

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H0 2(ii) There is no significant difference among primary school teachers’ digital literacy and its dimensions with respect to their locality.

Table 4.11

Significant difference among primary school teachers’ digital literacy and its dimensions with respect to their locality

Sum of Mean F-
Variable Dimension Groups df Sig Remark
squares square value
Between groups 531.646 2 265.823
Comprehension Within groups 53326.233 758 3.779 0.023
70.351 S
Total 53857.879 760
Between groups 770.910 2 385.455
Interdependence Within groups 177710.43 758 1.644 0.194 NS
234.446
Total 178481.34 760
Locality
Between groups 354.084 2
Curation 177.042
Within groups 52048.084 758 2.578 0.077 NS
Total 52402.168 760 68.665
Between groups 4354.413 2 2177.207
Over all NS
Within groups 648178.90 758 2.546 0.079
Digital literacy 855.117
Total 652533.32 760
(S-Significant, NS-Not Significant, Significant at 0.05 level)
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It is inferred from the above table 4.11 that the calculated F-values for dimension

of digital literacy such as Interdependence and curation are (1.644) and (2.578)

respectively, which are less than the critical F-distribution value (3.00) at 0.05 level of

significance. Hence there is no significant difference among primary school teachers in

dimension of digital literacy such as Interdependence and curation with respect to their

locality. Also, the calculated critical F-distribution value for dimension of digital literacy

such as comprehension (3.779) is greater than the critical F-distribution value (3.00) at 0.05

level of significance. Hence there is a significant difference in dimension for digital literacy

such as comprehension among primary school teachers with respect to their locality.

The above table also reveals that the calculated F-value for overall digital literacy

is (2.546) which is less than the table value (3.00) at 0.05 level of significance. Hence the

formulated null hypothesis H0 2 (ii) is accepted and that there is no significant difference

among primary school teachers’ digital literacy with respect to their locality.

Table 4.11 (a)

scheffe’s post hoc test for difference among primary school teachers’ digital literacy and

its dimensions with respect to their locality

Locality Mean
Difference Sig Remark
Dimension Rural Urban Semi-Urban (I-J)

44.14 45.80 1.667 0.037 S

Comprehension 45.80 44.11 1.698 0.245 NS

44.14 44.11 0.031 1.000 NS

(S-Significant, NS-Not Significant, Significant at 0.05 level)

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The above scheffe’s post hoc analysis reveals that the mean difference between

teachers from rural and urban is 1.667 for dimension of digital literacy such as

comprehension and it is significant at 0.05 level of significance. And also, the other two

combinations did not show any significant difference. Hence it is concluded that, primary

school teachers are significantly differed in dimension of digital literacy such as

comprehension with respect to their locality.

While comparing the mean scores, primary school teachers from urban (45.14)

are better than the teachers from rural (44.14) and semi urban (44.11) in digital literacy and

its dimensions.
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H0- 2(iii) There is no significant difference among primary school teachers’ digital literacy and its dimensions with respect to their type

of school

Table 4.12

Significant difference among primary school teachers’ digital literacy and its dimensions with respect to their type of school

Sum of Mean F-
Variable Dimension Groups df Sig Remark
Squares Square Value
Between groups 1408.456 2 704.228
Comprehension Within groups 52449.423 758 10.178 0.000
69.194 S
Total 52449.423 760
Between groups 2133.863 2 1066.932
Interdependence Within groups 176347.48 758 4.586 0.010 S
232.648
Type of Total 178481.34 760
School Between groups 530.008 2 265.004
Curation
Within groups 51872.161 758 3.872 0.021 S
68.433
Total 52402.168 760
Between groups 9658.703 2 4829.352
Over all
Within groups 642874.61 758 5.694 0.004 S
Digital literacy 848.120
Total 652533.32 760
(S-Significant, NS-Not Significant, Significant at 0.05 level)
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It is inferred from the above table 4.12 that the calculated F-values for dimensions

of digital literacy are (10.178), (4.586), (3.872), respectively, which are greater than the

critical F-distribution value (3.00) at 0.05 level of significance. Hence there is a significant

difference among primary school teachers in dimensions of digital literacy with respect to

their type of school.

The above table also reveals that the calculated F-value for digital literacy is (5.694)

which is greater than the table value (3.00) at 0.05 level of significance. Hence the

formulated null hypothesis H0 2 (iii) is rejected and that there is a significant difference

among primary school teachers’ digital literacy with respect to their type of school.

Table 4.12 (a)

scheffe’s post hoc test for difference among primary school teachers’ digital literacy and

its dimensions with respect to their type of school

Nature of School Mean

Difference Sig Remark
Dimension Govt Aided Self-finance (I-J)
43.12 43.63 0.511 0.900 NS
Comprehension 43.63 46.02 2.388 0.071 NS
43.12 46.02 2.899 0.000 S
76.92 82.03 5.111 0.043 S
Interdependence 82.03 80.11 1.914 0.602 NS
76.92 80.11 3.197 0.037 S
39.80 42.08 2.276 0.120 NS
Curation 42.08 41.52 0.558 0.863 NS
39.80 41.52 1.718 0.039 S
159.83 167.69 7.859 0.130 NS
Total 167.69 167.65 0.048 1.000 NS
159.83 167.65 7.810 0.005 S
(S-Significant, NS-Not Significant, Significant at 0.05 level)
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The above scheffe’s post hoc analysis reveals that the mean difference between

government school teachers and self- finance school teachers is (2.899) for comprehension

of digital literacy and it is significant at 0.05 level of significance.

The mean difference between government school teachers and aided school

teachers is (5.111), also government school teachers and self-finance school teachers is

(3.197) for Interdependence of digital literacy and it is significant at 0.05 level.

The mean difference between government school teachers and self-finance school

teachers is (1.718) for curation of digital literacy and it is significant at 0.05 level.

The scheffe’s post hoc analysis also reveal that the mean difference between

government school teachers and self-finance school teachers is (7.810) for digital literacy

and it is significant at 0.05 level.

While comparing the mean scores of digital literacy and its dimensions, self-finance

school teachers (46.02), (80.11) (41.52) (167.65) and aided school teachers (43.63),

(82.03), (42.08), (167.69) are better than the government school teachers (43.12), (76.92),

(39.80), (159.83). Hence it is concluded that self-finance school teachers and aided school

teachers are significantly differed at digital literacy and its dimensions.

H0 3(i) There is no significant difference between male and female of primary school

teachers in computational thinking and its dimensions

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H0 3(i) There is no significant difference between male and female of primary school

teachers in computational thinking and its dimensions

Table 4.13

Significant difference between male and female of primary school teachers in

computational thinking in and its dimensions

t- p-
Dimension Variables N Mean Std Remark
value value

Male 141 48.23 7.361

Abstraction 0.576 0.565 NS
Female 620 48.65 8.951

Male 141 51.25 7.134

Analysis 0.781 0.435 NS
Female 620 50.70 8.830

Over all Male 141 99.48 13.516

Computational 0.100 0.921 NS
thinking Female 620 99.35 17.082

(S-Significant, NS-Not Significant, Significant at 0.05 level)

It is inferred from the table 4.13 that the calculated t-values for dimension of

computational thinking such as abstraction and analysis are (0.576) and (0.781)

respectively, which are less than the table value (1.96) at 0.05 level of significance. Hence

there is no significant difference between male and female teachers in dimensions of

computational thinking.

The above table also reveals that the calculated t-value for overall computational

thinking is (0.100) which is less than the table value (1.96) at 0.05 level of significance.

Hence the formulated null hypothesis H0 3 (i) is accepted and that there is no significant

difference between male and female teachers in computational thinking.

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H0 3 (ii)There is no significant difference between UG and PG teachers of primary school

teachers in computational thinking and its dimensions

Table 4.14

Significant difference between UG and PG teachers in of primary school teachers

computational thinking and its dimensions

t- p-
Dimension Variables N Mean Std Remark
value value

UG 293 47.77 8.210

Abstraction 2.062 0.040 S
PG 468 49.07 8.926

UG 293 50.27 7.976

Analysis 1.392 0.164 NS
PG 468 51.14 8.866

Overall UG 293 98.04 15.407

Computational 1.812 0.070 NS
thinking PG 468 100.21 17.068

(S-Significant, NS-Not Significant, Significant at 0.05 level)

It is inferred from the table 4.14 that the calculated t-values for dimension of

computational thinking such as abstraction (2.062) which is greater than the table value

(1.96) at 0.05 level of significance and that there is a significant difference in dimension of

computational thinking such as abstraction with respect to their educational qualification.

Also, the calculated t-value for dimension of computational thinking such as analysis is

(1.392) which is less than the table value (1.96) at 0.05 level of significance. Hence there

is no significant difference in dimension of computational thinking such as analysis of

primary school teachers with respect to their educational qualification.

 139

The above table also reveals that the calculated t-value for overall computational

thinking is (1.812) which is less than the table value (1.96) at 0.05 level of significance.

Hence the formulated null hypothesis H0 3 (ii) is accepted and that there is no significant

difference between UG and PG teachers in computational thinking.

H0-3(iii) There is no significant difference between arts and science subjects of primary

school teachers in computational thinking and its dimensions

Table 4.15

Significant difference between arts and science subjects of primary school teachers in

computational thinking and its dimensions

t- p-
Dimension Variables N Mean Std Remark
value value

Arts 393 48.18 8.807

Abstraction 1.274 0.203 NS
Science 368 48.98 8.525

Arts 393 50.13 8.525

Analysis 2.259 0.024 S
Science 368 51.52 8.443

Overall Arts 393 98.31 16.659

Computational 1.842 0.06 NS
thinking Science 368 100.51 16.216

(S-Significant, NS-Not Significant, Significant at 0.05 level)

It is inferred from the table 4.15 that the calculated t-values for dimension of

computational thinking such as abstraction (1.274) which is less than the table value (1.96)

at 0.05 level of significance and that there is no significant difference in dimension

of computational thinking such as abstraction with respect to their subject. Also, the

calculated t-value for dimension of computational thinking such as analysis is (2.259)

 140

which is greater than the table value (1.96) at 0.05 level of significance. Hence there is

a significant difference in dimension of computational thinking such as analysis of primary

school teachers with respect to their subject.

The above table also reveals that the calculated t-value for overall computational

thinking is (1.842) which is less than the table value (1.96) at 0.05 level of significance.

Hence the formulated null hypothesis H0 3 (iii) is accepted and that there is no significant

difference between arts and science subjects in computational thinking of primary

school teachers.

H0 3(iv)There is no significant difference in computational thinking and its dimensions

of primary school teachers with respect to online teaching experience

Table 4.16

Significant difference in computational thinking and its dimensions of primary school

teachers with respect to online teaching experience

t- p-
Variable Dimension Category N Mean Std Remark
value value

Yes 559 48.37 8.693

Abstraction 4.408 0.000 S
No 202 46.35 8.246

Online Yes 559 57.29 8.620

Teaching Analysis 2.705 0.007 S
Experience No 202 49.45 8.181

Overall Yes 559 100.67 16.587

Computational 3.732 0.000 S
thinking No 202 95.80 15.640

(S-Significant, NS-Not Significant, Significant at 0.05 level)

 141

It is inferred from the table 4.16 that the calculated t-values for dimension of

computational thinking such as abstraction and analysis are (4.408) and (2.705)

respectively, which are greater than the table value (1.96) at 0.05 level of significance.

Hence there is a significant difference in dimensions of computational thinking of primary

school teachers with respect to their online teaching experience.

The above table also reveals that the calculated t-value for overall computational

thinking is (3.732) which is greater than the table value (1.96) at 0.05 level of significance.

Hence the formulated null hypothesis H0 3 (iv) is rejected and that there is a significant

difference in computational thinking of primary school teachers with respect to their online

teaching experience.
 142

H0 4(i) There is no significant difference among primary school teachers computational thinking and its dimensions with respect to

their years of teaching experience

Table 4.17

Significant difference among primary school teacher’s computational thinking and its dimensions with respect to their years of teaching

experience

Sum of Mean F-
Variable Category groups df Sig Remark
Squares Square Value
Between groups 501.005 2 250.502
Abstraction Within groups 56695.484 758 3.349 0.036 S
74.796
Total 57196.489 760
Between groups 311.344 2 155.672
Teaching
Analysis Within groups 55106.482 758 2.141 0.118 NS
Experience 72.700
Total 55417.827 760
Between groups 1596.688 2 798.344
Overall
Computational Within groups 204607.57 758 2.958 0.053 NS
thinking 269.931
Total 206204.26 760
(S-Significant, NS-Not Significant, Significant at 0.05 level)
 143

It is inferred from the above table 4.17 that the calculated F-values for dimension

of computational thinking such as Abstraction is (3.349) which is greater than the critical

F-distribution value (3.00) at 0.05 level of significance. Hence there is significant

difference in dimension of computational thinking such as abstraction of primary school

teachers with respect to their years of teaching experience. Also, the calculated critical

F-distribution value for dimension of computational thinking such as analysis (2.141) is

less than the critical F-distribution value (3.00) at 0.05 level of significance. Hence there

is no significant difference in dimension of computational thinking such as analysis of

primary school teachers with respect to their years of teaching experience.

The above table also reveals that the calculated F-value for overall computational

thinking is (2.958) which is less than the table value (3.00) at 0.05 level of significance.

Hence the formulated null hypothesis H0 3 (i) is accepted and that there is no significant

difference among primary school teachers’ computational thinking with respect to their

years of teaching experience.

Table 4.17(a)

scheffe’s post hoc test for difference among primary school teachers’ computational

thinking and its dimensions with respect to their years of teaching experience

Years of Teaching Experience Mean

More than Difference Sig Remark
Dimension 0-5yrs 5-10yrs
10yrs (I-J)
49.04 47.00 2.040 0.039 S
Abstraction 47.00 48.82 1.822 0.118 NS
49.04 48.82 0.218 0.965 NS
Overall 100.17 96.56 3.607 0.060 NS
Computational 96.56 99.97 3.414 0.188 NS
thinking 100.17 99.97 0.193 0.992 NS
(S-Significant, NS-Not Significant, Significant at 0.05 level)
 144

The above scheffe’s post hoc analysis reveals that the mean difference between

teachers have 0-5yrs and 5-10yrs of experience is (2.040) for dimension in computational

thinking such as abstraction and it is significant at 0.05 level of significance. And also,

the other two combinations did not show any significant difference. Hence it is concluded

that, primary school teachers are significantly differed in computational thinking and its

dimension such as abstraction with respect to their offline teaching experience.

While comparing the mean scores, primary school teachers have 0-5years of

experience (49.04) are better than the teachers having 5-10years of experience (47.00) and

above 10years of experience (48.82) in computational thinking and its dimensions.

 145

H0 4(ii) There is no significant difference in computational thinking and its dimensions of

primary school teachers with respect to their locality

Table 4.18

Significant difference among primary school teacher’s computational thinking and its

dimensions with respect to their locality

Sum of Mean F-
Variable Category groups df Sig Remark
Squares Square Value
Between
287.887 2 143.944
groups
Abstraction Within 1.958 0.142 NS
55641.690 758
groups 73.503
Total 55929.578 760
Between
127.360 2 63.680
groups
Locality Analysis Within 0.873 0.418 NS
55290.466 758
groups 72.943
Total 55417.827 760
Between
704.534 2 352.267
Overall groups
Computational Within 1.299 0.273 NS
205499.73 758
thinking groups 271.108
Total 206204.26 760
(S-Significant, NS-Not Significant, Significant at 0.05 level)

It is inferred from the above table 4.18 that the calculated F-values for dimension

of computational thinking such as Abstraction and Analysis is (1.958) and (0.873) which

is less than the critical F-distribution value (3.00) at 0.05 level of significance. Hence there

is no significant difference in dimension of computational thinking such as abstraction

and analysis of primary school teachers with respect to their locality.

The above table also reveals that the calculated F-value for overall computational

thinking is (1.299) which is less than the table value (3.00) at 0.05 level of significance.

Hence the formulated null hypothesis H0 4 (ii) is accepted and that there is no significant

difference among primary school teachers’ computational thinking with respect to their locality.
 146

H0 4(iii) There is no significant difference in computational thinking and its dimensions of primary school teachers with respect to their

type of school

Table 4.19

Significant difference among primary school teacher’s computational thinking and its dimensions with respect to their type of school

Sum of Mean F-
Variable Category groups df Sig Remark
Squares Square Value

Between groups 1148.524 2 574.262

Abstraction Within groups 56047.964 758 7.766 0.000 S

73.942
Total 57196.489 760

Between groups 655.246 2 327.623

Type of
Analysis Within groups 54762.580 758 4.535 0.011 S
School 72.246
Total 55417.827 760

Between groups 3523.628 2 1761.81

Overall
Computational Within groups 202680.63 758 6.589 0.001 S
thinking 267.389
Total 206204.26 760

(S-Significant, NS-Not Significant, Significant at 0.05 level)

 147

It is inferred from the above table 4.19 that the calculated F-values for dimensions

of computational thinking are (7.766), (4.535), respectively, which are greater than

the critical F-distribution value (3.00) at 0.05 level of significance. Hence there is

a significant difference among primary school teachers in dimensions of computational

thinking with respect to their type of school.

The above table also reveals that the calculated F-value for computational thinking

is (6.589) which is greater than the table value (3.00) at 0.05 level of significance. Hence

the formulated null hypothesis H0 4 (iii) is rejected and that there is a significant difference

among primary school teachers’ computational thinking with respect to their type of school.

Table 4.19 (a)

scheffe’s post hoc test for difference among primary school teachers’ computational

thinking and its dimensions with respect to their type of school

Type of School Mean

Difference Sig Remark
Dimension Govt Aided Self-finance (I-J)
46.69 49.24 2.552 0.093 NS
Abstraction 49.24 49.39 0.151 0.990 NS
46.69 49.39 1.569 0.001 S
49.40 50.93 1.529 0.417 S
Analysis
50.93 51.47 0.543 0.882 NS
49.40 51.47 2.072 0.011 S
96.09 100.17 4.081 0.186 NS
Overall
Computational 100.17 100.86 0.695 0.946 NS
thinking
96.09 100.86 4.776 0.002 S
(S-Significant, NS-Not Significant, Significant at 0.05 level)
 148

The above scheffe’s post hoc analysis reveals that the mean difference between

government school teachers and self-finance school teachers is (1.569) for dimension of

computational thinking such as abstraction and it is significant at 0.05 level of significance.

The mean difference between government school teachers and aided school

teachers is (1.529), also government school teachers and self-finance school teachers

is (2.072) for dimension of computational thinking such as analysis and it is significant

at 0.05 level.

The scheffe’s post hoc analysis also reveal that the mean difference between

government school teachers and self-finance school teachers is (4.776) for computational

thinking and it is significant at 0.05 level.

While comparing the mean scores of computational thinking and its dimensions,

self-finance school teachers (49.39), (51.47) (100.86) and aided school teachers (49.24),

(50.93), (100.86), are better than the government school teachers (46.69), (49.40), (96.09).

Hence it is concluded that private school teachers and aided school teachers are

significantly differed at computational thinking and its dimensions.

 149

H0 5 (i) There is no significant difference between male and female teachers of primary

school teachers in professional commitment and its dimensions

Table 4.20

Significant difference between male and female teachers of primary school teachers in

professional commitment and its dimensions

t- p-
Dimension Variables N Mean Std Remark
value value
Commitment to Male 141 39.26 6.501
0.703 0.483 NS
students Female 620 39.69 6.920
Commitment to Male 141 35.61 5.811
0.552 0.582 NS
Profession Female 620 35.91 6.195
Commitment to Male 141 63.18 10.602
Achieving 0.452 0.651 NS
Excellence Female 620 63.63 10.859
Overall Male 141 138.05 21.441
Professional 0.587 0.558 NS
Commitment Female 620 139.23 22.494

(S-Significant, NS-Not Significant, Significant at 0.05 level)

It is inferred from the table 4.20 that the calculated t-values for dimensions

of professional commitment such as commitment to students, commitment to profession,

commitment to achieving excellence are (0.703), (0.552) and (0.452) respectively, which

are less than the table value (1.96) at 0.05 level of significance. Hence there is no significant

difference between male and female teachers in professional commitment and its dimensions.

The above table also reveals that the calculated t-value for overall professional

commitment is (0.587) which is less than the table value (1.96) at 0.05 level of significance.

Hence the formulated null hypothesis H0 5 (i) is accepted and that there is no significant

difference between male and female teachers in professional commitment.

 150

H0 5 (ii) There is no significant difference between UG and PG of primary school teachers

in professional commitment and its dimensions

Table 4.21

Significant difference between UG and PG of primary school teachers in professional

commitment and its dimensions

t- p-
Dimension Variables N Mean Std Remark
value value

UG 293 39.59 6.710

Commitment to
0.053 0.957 NS
students PG 468 39.62 6.930

UG 293 35.45 6.039

Commitment to
1.456 0.146 NS
Profession PG 468 36.41 6.167

Commitment to UG 293 62.71 10.165

Achieving 1.730 0.084 NS
Excellence PG 468 64.07 11.168

Overall UG 293 137.75 21.659

Professional 1.247 0.213 NS
Commitment PG 468 139.80 22.669

(S-Significant, NS-Not Significant, Significant at 0.05 level)

It is inferred from the table 4.21 that the calculated t-values for dimensions of

professional commitment such as commitment to students, commitment to profession,

commitment to achieving excellence are (0.053), (1.456) and (1.730) respectively, which

are less than the table value (1.96) at 0.05 level of significance. Hence there is no significant

difference between UG and PG teachers in professional commitment and its dimensions.

The above table also reveals that the calculated t-value for overall professional

commitment is (1.247) which is less than the table value (1.96) at 0.05 level of significance.
 151

Hence the formulated null hypothesis H0 5 (ii) is accepted and that there is no

significant difference in professional commitment and its dimensions with respect to

educational qualification.

H0 5 (iii)There is no significant difference between arts and science subject of primary

school teachers in professional commitment and its dimensions

Table 4.22

Significant difference between arts and science subject of primary school teachers in

professional commitment and its dimensions

t- p-
Dimension Variables N Mean Std Remark
value value
Commitment to Arts 393 39.54 7.056
0.237 0.813 NS
students Science 367 39.66 6.615
Commitment to Arts 393 35.75 6.179
0.481 0.631 NS
Profession Science 367 35.96 6.073
Commitment to Arts 393 63.03 11.306
Achieving 1.349 0.178 NS
Excellence Science 367 64.08 10.227
Overall Arts 393 138.32 22.972
Professional 0.857 0.391 NS
Commitment science 367 139.70 21.550

(S-Significant, NS-Not Significant, Significant at 0.05 level)

It is inferred from the table 4.22 that the calculated t-value for dimensions of

professional commitment such as commitment to students, commitment to profession,

commitment to achieving excellence are (0.237), (0.481) and (1.349) respectively, which

are less than the table value (1.96) at 0.05 level of significance. Hence there is no significant

difference in primary school teachers in professional commitment and its dimensions with

respect to subject.
 152

The above table also reveals that the calculated t-value for overall professional

commitment is (0.847) which is less than the table value (1.96) at 0.05 level of significance.

Hence the formulated null hypothesis H0 5 (iii) is accepted and that there is no significant

difference in professional commitment and its dimensions with respect to their subject.

H0 5 (iv) There is no significant difference in professional commitment and its dimensions of

primary school teachers with respect to their online teaching experience

Table 4.23

Significant difference in Professional commitment and its dimensions of primary school

teachers with respect to online teaching experience

t- p-
Variable Dimension Category N Mean Std Remark
value value

Yes 412 40.04 6.928

Commitment
3.037 0.003
to students S
No 349 38.40 6.465

Yes 412 36.32 5.943

Commitment
3.337 0.001
to Profession S
Online No 349 34.59 6.439
Teaching
Experience Commitment Yes 412 64.61 10.356
to Achieving 4.339 0.000
Excellence No 349 60.63 11.487 S

Overall Yes 412 140.97 21.764

Professional 3.968 0.000 S
Commitment No 349 133.63 22.891

(S-Significant, NS-Not Significant, Significant at 0.05 level)

It is inferred from the table 4.23 that the calculated t-values for dimensions of

professional commitment such as commitment to students, commitment to profession,

commitment to achieving excellence are (3.037), (3.337) and (4.339) respectively, which

are greater than the table value (1.96) at 0.05 level of significance. Hence there is a
 153

significant difference in professional commitment and its dimensions of primary school

teachers with respect to their online teaching experience.

The above table also reveals that the calculated t-value for overall professional

commitment is (3.968) which is greater than the table value (1.96) at 0.05 level of

significance. Hence the formulated null hypothesis H0 5 (iv) is rejected and that there is

a significant difference in professional commitment and its dimensions of primary school

teachers with respect to online teaching experience.

 154

H0 6 (i) There is no significant difference among primary school teachers’ professional commitment and its dimensions with respect to

their years of teaching experience

Table 4.24

Significant difference among primary school teachers’ professional commitment and its dimensions with respect to their years of

teaching experience

Sum of Mean F-
Variables Category groups df Sig Remark
Squares Square Value
Between groups 23.321 2 11.661
Commitment to
Within groups 35513.278 758 0.249 0.780 NS
students 46.913
Total 35536.599 760
Between groups 144.885 2 72.442
Commitment to
Within groups 28329.314 758 1.936 0.145 NS
Profession 37.423
Teaching Total 28474.199 760
Experience Between groups 421.647 2 210.824
Commitment to
Achieving Within groups 88177.463 758 1.810 0.164 NS
Excellence 116.483
Total 88599.111 760
Overall Between groups 1218.763 2 609.382
Professional Within groups 375977.04 758 1.227 0.294 NS
Commitment 496.667
Total 377195.81 760
(S-Significant, NS-Not Significant, Significant at 0.05 level)
 155

It is inferred from the above table 4.24 that the calculated F-values for dimensions

of professional commitment such as commitment to students, commitment to profession,

commitment to achieving excellence are (0.249), (1.936) and (1.810) respectively which is

less than the critical F-distribution value (3.00) at 0.05 level of significance. Hence there

is no significant difference among primary school teachers in professional commitment

and its dimensions with respect to their years of teaching experience.

The above table also reveals that the calculated F-value for overall professional

commitment is (1.297) which is less than the table value (3.00) at 0.05 level of significance.

Hence the formulated null hypothesis H0 6 (i) is accepted and that there is no significant

difference among primary school teachers’ professional commitment and its dimensions

with respect to their years of teaching experience.

 156

H0 6 (ii) There is no significant difference in professional commitment and its dimensions of primary school teachers with respect to

their locality

Table 4.25

Significant difference among primary school teacher’s professional commitment and its dimensions with respect to their years of locality

Sum of Mean F-
Variable Category Groups df Sig Remark
Squares Square Value
Between groups 88.183 2 44.092
Commitment to
Within groups 35489.339 758 0.942 0.390 NS
students 46.820
Total 35577.522 760
Between groups 108.432 2 54.216
Commitment to
Within groups 28382.955 758 1.448 0.236 NS
Profession 37.445
Total 28491.388 760
locality
Between groups 441.964 2 220.982
Commitment to
Achieving Within groups 88312.339 758 1.897 0.151 NS
Excellence 116.507
Total 88754.302 760

Overall Between groups 1657.344 2 828.672

Professional Within groups 376067.49 758 1.670 0.189 NS
Commitment 496.131
Total 377724.84 760
(S-Significant, NS-Not Significant, Significant at 0.05 level)
 157

It is inferred from the above table 4.25 that the calculated F-values for dimensions

of professional commitment such as commitment to students, commitment to profession,

commitment to achieving excellence are (0.942), (1.448) and (1.897) respectively, which

is less than the critical F-distribution value (3.00) at 0.05 level of significance. Hence there

is no significant difference among primary school teachers in professional commitment

and its dimensions with respect to their locality.

The above table also reveals that the calculated F-value for overall professional

commitment is (1.670) which is less than the table value (3.00) at 0.05 level of significance.

Hence the formulated null hypothesis H0 6(ii) is accepted and that there is no significant

difference among primary school teachers’ professional commitment and its dimensions

with respect to their locality.

 158

H0 6(iii) There is no significant difference in professional commitment and its dimensions of primary school teachers with respect to

their type of school

Table 4.26

Significant difference among primary school teachers’ professional commitment and its dimensions with respect to their type of school

Sum of Mean F-
Variables Category groups df Sig Remark
Squares Square value
Between groups 807.900 2 403.950
Commitment to
Within groups 34769.622 758 8.806 0.000 S
students 45.870
Total 35577.522 760
Between groups 587.208 2 293.604
Commitment to
Within groups 27904.180 758 7.976 0.000 S
Profession 36.813
Type of Total 28491.388 760
School Between groups 1687.203 2 843.602
Commitment to
Achieving Within groups 87067.099 758 7.344 0.001 S
Excellence 114.864
Total 88754.302 760

Overall Between groups 8781.315 2 4390.65

Professional Within groups 368943.52 758 9.021 0.000 S
Commitment 486.733
Total 377724.84 760
(S-Significant, NS-Not Significant, Significant at 0.05 level)
 159

It is inferred from the above table 4.26 that the calculated F-values for dimensions

of professional commitment such as commitment to students, commitment to profession,

commitment to achieving excellence are (8.806), (7.976) and (7.344) respectively, which

is greater than the critical F-distribution value (3.00) at 0.05 level of significance. Hence

there is a significant difference among primary school teachers in professional commitment

and its dimensions with respect to their nature of school.

The above table also reveals that the calculated F-value for overall professional

commitment is (9.021) which is greater than the table value (3.00) at 0.05 level of

significance. Hence the formulated null hypothesis H0 6 (iii) is rejected and that there is

a significant difference among primary school teachers’ professional commitment and its

dimensions with respect to their type of school.

 160

Table 4.26 (a)

scheffe’s post hoc test for difference among primary school teachers’ professional

commitment and its dimensions with respect to their type of school

Nature of School Mean

Difference Sig Remark
Dimension Govt Aided Self-finance (I-J)
38.06 39.53 1.471 0.266 NS
Commitment to
39.53 40.37 0.840 0.609 NS
students
38.06 40.37 2.311 0.000 S
34.54 35.77 1.231 0.315 NS
Commitment to
35.77 36.51 0.739 0.620 NS
Profession
35.54 36.51 1.970 0.000 S
61.30 63.63 2.329 0.266 NS
Commitment to
Achieving 63.63 64.64 1.011 0.751 NS
Excellence 61.30 64.64 3.340 0.001 S

Overall 133.90 138.93 5.031 0.232 NS

Professional 138.93 141.52 2.589 0.641 NS
Commitment 133.90 141.52 7.621 0.000 S
(S-Significant, NS-Not Significant, Significant at 0.05 level)

The above scheffe’s post hoc analysis reveals that the mean difference between

government school teachers and self-finance school teachers is (2.311), (1.970), (3.340),

(7.621) for professional commitment and its dimension such as commitment to students,

commitment to profession, commitment to achieving excellence and it is significant at 0.05

level of significance.

While comparing the mean scores of professional commitments and its dimensions,

self-finance school teachers (40.37), (36.51) (64.64), (141.52) and aided school teachers

(39.53), (35.77), (63.63), (138.93) are better than the government school teachers (38.06),
 161

(34.54), (61.30), (133.90). Hence it is concluded that self-finance school teachers and aided

school teachers are significantly differed at professional commitment and its dimensions

with respect to their type of school.

H0 7 There is no significant difference among three types of schools in digital literacy with

respect to (i) gender (ii) educational qualification (iii) subject (iv) online teaching experience

(v) locality (vi) years of experience

Table 4.27

A two-way ANOVA analysis for significant difference among three types of schools in

digital literacy with respect to (i) gender (ii) educational qualification (iii) subject (iv)

online teaching experience (v) locality (vi) years of experience

Type III
Mean
variable category Sum of df F Sig Result
square
squares

type of school * 2
863.214 1124.77 0.506 0.603 NS
gender 760

type of school* 2
243.926 121.963 0.145 0.865 NS
edu.quali 760
2
type of school*subject 1540.009 848.347 0.908 0.404 NS
Digital 760
Literacy type of school*online 2
636.148 845.845 0.376 0.687 NS
te.ex 760
4
type of school*locality 3562.306 848.479 1.050 0.381 NS
760

type of school*Years 4
4881.415 842.851 1.448 0.216 NS
of TE 760
(S-Significant, NS-Not Significant, Significant at 0.05 level)
 162

The above table reveals that the calculated F-value for type of school * gender

(0.506), type of school* educational qualification (0.145), type of school*subject (0.908),

type of school*online teaching experience (0.376), type of school*locality (1.050), type of

school*years of teaching experience (1.448) are less than critical F-distribution value

(3.00) at 0.05 level of significance. Hence the formulated null hypothesis H0 7 is accepted

and there is no significant difference among three types of schools in digital literacy with

respect to (i) gender (ii) educational qualification (iii) subject (iv)online teaching experience

(v) locality (vi) years of experience.

 163

H0 8 There is no significant difference among three types of schools in computational

thinking with respect to (i) gender (ii) educational qualification (iii) subject (iv) online

teaching experience (v) locality (vi) years of experience

Table 4.28

A two-way ANOVA analysis for significant difference among three types of schools in

computational thinking with respect to (i) gender (ii) educational qualification (iii) subject

(iv) online teaching experience (v) locality (vi) years of experience

Type III Mean

variable category Sum of df F Sig Result
squares square

type of school * 2
249.980 267.870 0.464 0.629 NS
gender 760
type of school* 2
44.372 267.329 0.083 0.920 NS
edu.quali 760
type of 2
443.977 268.421 0.827 0.438 NS
school*subject 760
Computational
type of 2
Thinking school*online 230.106 267.752 0.430 0.651 NS
te.ex 760

type of 4
1064.622 268.669 0.991 0.412 NS
school*locality 760
type of 4
school*years of 1449.107 266.518 1.359 0.246 NS
te.ex 760

(S-Significant, NS-Not Significant, Significant at 0.05 level)

The above table reveals that the calculated F-value for type of school * gender

(0.464), type of school* educational qualification (0.083), type of school*subject (0.827),

type of school*online teaching experience (0.430), type of school*locality (0.991), type of

school*years of teaching experience (1.359) are less than critical F-distribution value
 164

(3.00) at 0.05 level of significance. Hence the formulated null hypothesis H0 8 is accepted

and there is no significant difference among three types of schools in computational

thinking with respect to (i) gender (ii) educational qualification (iii) subject (iv)online

teaching experience (v) locality (vi) years of experience.

H0 9 There is no significant difference among three types of schools in professional

commitment with respect to (i) gender (ii) educational qualification (iii) subject (iv) online

teaching experience (v) locality (vi) years of experience

Table 4.29

A two-way ANOVA analysis for significant difference among three types of schools in

professional commitment with respect to (i) gender (ii) educational qualification (iii)

subject (iv) online teaching experience (v) locality (vi) years of experience

Type III Mean

Variable Category Sum of df F Sig Result
squares square

type of school * 2
374.718 487.588 0.384 0.681 NS
gender 760
type of school* 2
645.088 322.544 0.663 0.516 NS
edu.quali 760
type of 2
846.485 489.022 0.865 0.421 NS
school*subject 760
Professional
Commitment type of 2
715.301 488.134 0.733 0.481 NS
school*online te.ex 760
type of 4
2480.748 488.796 1.269 0.281 NS
school*locality 760
type of 4
school*years of 3068.018 486.595 1.576 0.179 NS
te.ex 760

(S-Significant, NS-Not Significant, Significant at 0.05 level)

 165

The above table reveals that the calculated F-value for type of school * gender

(0.384), type of school* educational qualification (0.663), type of school*subject (0.865),

type of school*online teaching experience (0.733), type of school*locality (01.269), type

of school*years of teaching experience (1.576) are less than critical F-distribution value

(3.00) at 0.05 level of significance. Hence the formulated null hypothesis H0 9 is accepted

and there is no significant difference among three types of schools in professional

commitment with respect to (i) gender (ii) educational qualification (iii) subject (iv)online

teaching experience (v) locality (vi) years of experience.

4.4 CORRELATION ANALYSIS

Correlation analysis is used to find out the relationship between two variables.

The correlation co-efficient is valued in the field of education as the measure of relationship

between test scores and other measures of performance. In the present study, the correlation

analysis is used to find out the relationship between digital literacy and professional

commitment, and computational thinking and professional commitment.

H0 10 There is no significant relationship between digital literacy and professional

commitment among primary school teachers

Table 4.30

Significant relationship between digital literacy and professional commitment among

primary school teachers

Variable N Mean r P Result

Digital literacy 761 163.26
0.878** 0.000 S
Professional Commitment 761 138.19
(S-Significant, NS-Not Significant) (** Significant at 0.01 level)
 166

The above table reveals that there is positive significant relationship among primary

school teacher’s digital literacy and professional commitment. The calculated r value for

digital literacy and professional commitment is 0.878 at 0.01 significant level. Hence the

formulated null hypothesis H0 10 is rejected and there is a significant relationship among

primary school teachers’ digital literacy and professional commitment.

H0 11 There is no significant relationship between dimensions of digital literacy and

dimensions of professional commitment of primary school teachers.

Table 4.31

Significant relationship between dimensions of digital literacy and dimensions of

professional commitment of primary school teachers

Professional Commitment

Variables Commitment to
Commitment Commitment
achieving
to student to profession
excellence
Comprehension 0.785** 0.814** 0.835**
Digital
Interdependence 0.819** 0.862** 0.876**
literacy
Curation 0.811** 0.844** 0.861**
(S-Significant, NS-Not Significant), (** Significant at 0.01 level)

The above table reveals that there is positive significant relationship between

dimensions of digital literacy and dimensions of professional commitment. The calculated

r value for comprehension and commitment to student (0.785), comprehension and

commitment to profession (0.814), comprehension and commitment to achieving excellence

(0.835), Interdependence and commitment to student (0.819), Interdependence and

commitment to profession (0.862), Interdependence and commitment to achieving

 167

excellence (0.876), curation and commitment to student (0.811), curation and commitment

to profession (0.844), curation and commitment to achieving excellence (0.861) are

significant at 0.01 level. Hence the formulated null hypothesis H0 11 is rejected and there

is a significant relationship between dimensions of digital literacy and dimensions of

professional commitment of primary school teachers.

H0 12 There is no significant relationship between computational thinking and professional

commitment among primary school teachers

Table 4.32

Significant relationship between computational thinking and professional commitment

among primary school teachers

Variable N Mean r P Result

Computational thinking 761 98.83

0.923** 0.000 S
Professional Commitment 761 138.19

(S-Significant, NS-Not Significant), (** Significant at 0.01 level)

The above table reveals that there is positive significant relationship among primary

school teacher’s computational thinking and professional commitment. The calculated

r value for computational thinking and professional commitment 0.923 significant at 0.01

level. Hence the formulated null hypothesis H0 12 is rejected and there is a significant

relationship between computational thinking and professional commitment of primary

school teachers.
 168

H0 13 There is no significant relationship between dimensions of computational thinking

and dimensions of professional commitment among primary school teachers.

Table 4.33

Significant relationship between dimensions of computational thinking and dimensions of

professional commitment among primary school teachers

Professional Commitment

Variables Commitment to
Commitment Commitment
achieving
to student to profession
excellence

Computational Abstraction 0.843** 0.873** 0.894**

thinking Analysis 0.836** 0.889** 0.901**

(S-Significant, NS-Not Significant), (** Significant at 0.01 level)

The above table reveals that there is positive significant relationship between

dimensions of digital literacy and dimensions of professional commitment. The calculated

r value for abstraction and commitment to student (0.843), abstraction and commitment

to profession (0.873), abstraction and commitment to achieving excellence (0.894),

analysis and commitment to student (0.836), analysis and commitment to profession

(0.889), analysis and commitment to achieving excellence (0.901), are significant at 0.01

level. Hence the formulated null hypothesis H0 13 is rejected and there is a significant

relationship between dimensions of computational thinking and dimensions of professional

commitment of primary school teachers.

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H0 14 There is no significant relationship between digital literacy, computational thinking

in professional commitment among primary school teachers

Table 4.34

Significant relationship between digital literacy, computational thinking in professional

commitment among primary school teachers

Variable Mean r R2 Result

Digital literacy 163.26

Computational thinking 98.83 0.934 0.873 S

Professional Commitment 138.19

From the above table, it is inferred that there is a significant correlation between

digital literacy, computational thinking in professional commitment. The calculated r value

for digital literacy, computational thinking and professional commitment is 0.923

significant at 0.01 level. Hence H0 14 is rejected and there is a significant correlation

between digital literacy, computational thinking in professional commitment of primary

school teachers.

4.5 MULTIPLE LINEAR REGRESSION ANALYSIS

Multiple Linear Regression Analysis is used to find out the influence of digital

literacy and computational thinking in professional commitment of primary school teachers.

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H0 15 There is no significant influence of digital literacy in professional commitment of

primary school teachers.

Table 4.35

Coefficients

Unstandardized Standardized
Coefficients Coefficients
Model t Sig.
B Std. Error Beta

(Constant) 33.387 2.105 15.858 .000

1
DL .642 .013 .878 50.658 .000

a. Dependent Variable: PC

In this table 4.35 the Standardized beta indicates that the relative contribution of

digital literacy in predicting the professional commitment is based on percentage of digital

literacy (87.8). Thus, these variables have influence on professional commitment.

The following regression equation is arrived from the above table

Professional Commitment = 33.387 + 0.642 (digital literacy)

Table 4.35 (a)

Model Summary

Adjusted R
Model R R Square Std. Error of the Estimate
Square

.878a .772 .771 10.765

a. Predictors: (Constant), DL
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From the above table, it is inferred that 77.1% (adjusted R2 =0.771) of variation in

professional commitment of primary school teachers is explained by the above independent

variable’s digital literacy. The remaining 22.9% can be explained by unknown, hidden

variables, or inherent variables. Hence hypothesis 15 is rejected and there is a significant

influence of digital literacy in professional commitment of primary school teachers

H0 16 There is no significant influence of dimensions of digital literacy in professional

commitment of primary school teachers

Table 4.36

Coefficients

Unstandardized Standardized
t Sig.
Model Coefficients Coefficients
B Std. Error Beta
(Constant) 15.051 1.304 11.538 .000
Comprehension .954 .041 .357 23.107 .000
Interdependence .645 .025 .439 26.069 .000
Curation .709 .050 .261 14.075 .000
a. Dependent Variable: PC

In this table 4.36 the Standardized beta indicates that the relative contribution of

dimensions of digital literacy in predicting the professional commitment is based on

percentage of comprehension (35.7%) Interdependence (43.9%), and Curation (26.1%).

Thus, these variables have influence on professional commitment.

The following regression equation is arrived from the above table.

Professional Commitment = 15.101 + 0.954 (comprehension)

+0.645 (interdependence) + 0.709 (Curation)

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Table 4.36 (a)

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

.962a .925 .925 6.175

Predictors: (Constant), Comprehension, Interdependence, Curation

From the above table, it is inferred that 92.5% (adjusted R2 =0.925) of variation in

professional commitment of primary school teachers is explained by the dimensions

of digital literacy. The remaining 7.5% can be explained by unknown, hidden variables, or

inherent variables. Hence hypothesis 16 is rejected and there is a significant influence of

digital literacy and its dimensions in professional commitment of primary school teachers.

H0 17 There is no significant influence of computational thinking in professional

commitment of primary school teachers.

Table 4.37

Coefficients

Unstandardized Standardized
Model Coefficients Coefficients t Sig.
B Std. Error Beta
(Constant) 16.202 1.877 8.630 .000
CT 1.234 .019 .923 65.914 .000

a. Dependent Variable: PC

In this table 4.37 the Standardized beta indicates that the relative contribution

of computational thinking in predicting the professional commitment is based on

 173

percentage of computational thinking (92.3). Thus, these variables have influence on

professional commitment.

The following regression equation is arrived from the above table.

Professional Commitment = 16.202 + 1.234 (computational thinking)

Table 4.37 (a)

Model Summary

Adjusted R Std. Error of the

Model R R Square
Square Estimate

.923a .851 .851 8.689

Predictors: (Constant), CT

From the above table, it is inferred that 85.1% (adjusted R2 =0.851) of variation in

professional commitment of primary school teachers is explained by the above independent

variable computational thinking. The remaining 14.9% can be explained by unknown,

hidden variables, or inherent variables. Hence hypothesis 17 is rejected and there is

a significant influence of computational thinking in professional commitment of primary

school teachers.
 174

H0 18 There is no significant influence of dimensions of computational thinking in

professional commitment of primary school teachers.

Table 4.38

Coefficients

Unstandardized Standardized
t Sig.
Model Coefficients Coefficients
B Std. Error Beta
(Constant) 8.225 1.485 5.537 .000
Abstraction 1.239 .050 .477 24.729 .000
Analysis 1.374 .051 .521 26.989 .000

Dependent Variable: PC

In this table 4.20 the standardized beta indicates that the relative contribution of

dimensions of computational thinking in predicting the professional commitment is based

on percentage of Abstraction (47.7), and Analysis (52.1). Thus, these variables have

influence on professional commitment.

The following regression equation is arrived from the above table.

Professional Commitment = 8.225 + 1.239 (abstraction) +1.374 (analysis)

Table 4.38 (a)

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

.955a .913 .912 6.662

Predictors: (Constant), Abstraction, Analysis

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From the above table, it is inferred that 91.2% (adjusted R2 =0.912) of variation in

professional commitment of primary school teachers is explained by the above independent

variable’s digital literacy. The remaining 8.8% can be explained by unknown, hidden

variables, or inherent variables. Hence hypothesis 18 is rejected and there is a significant

influence of computational thinking and its dimensions in professional commitment of

primary school teachers.

H0 19 There is no significant influence of digital literacy and computational thinking in

professional commitment of primary school teachers

Table 4.39

Coefficients

Unstandardized Standardized
Model Coefficients Coefficients t Sig.
B Std. Error Beta

(Constant) 15.101 1.739 8.683 .000

DL .222 .020 .304 11.364 .000
CT .879 .036 .657 24.569 .000

Dependent Variable: Professional Commitment

In this table 4.11 the Standardized beta indicates that the relative contribution of

digital literacy and computational thinking in predicting the professional commitment is

based on percentage of digital literacy (30.4), computational thinking (65.7). Thus, these

variables have influence on professional commitment.

Professional Commitment = 15.101 + 0.222 (digital literacy) + 0.879 (computational thinking)

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Table 4.39 (a)

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .934a .873 .873 8.037

a. Predictors: (Constant), CT, DL

b. Dependent Variable: PC

From the above table, it is inferred that 87.3% (adjusted R2 =0.873) of variation in

professional commitment of primary school teachers is explained by the above independent

variable’s digital literacy and computational thinking. The remaining 12.7% can be

explained by unknown, hidden variables, or inherent variables. Hence hypothesis 10 is

rejected and there is a significant influence of digital literacy and computational thinking

in professional commitment of primary school teachers.

4.6 CONCLUSION

In this chapter, the investigator clearly described the results obtained from the

quantitative analysis of the data. A summary of the findings of the study, followed with

discussion, recommendation based on the present study, possible areas for the further

research and conclusion are prescribed in the next chapter.