A Critique of the Proposed National Education Policy Reform
Presentation Sample.pptx
1. Supervised by
Dr. Hanan Halabi
The Effectiveness of Teaching
Computational Thinking Skills in
Different Subject Areas at the
Primary Level
School of Education
Lebanese International University (LIU)
Beirut, Lebanon
June 2021
2. Table of Content
1
• Chapter 1: Introduction
• An Operational Definition
• Statement of the Problem
• Purpose of the Study
• Rationale & Significance
• Research Questions & Hypotheses
• Chapter 2: Literature Review
• Theoretical Framework
• CT vs. Bloom’s Taxonomy
• CT vs. Constructivism
• Teachers’ readiness to teach CT
• Chapter 3: Methodology
• Design of the study
• Instruments
• Participants, Sampling and Data
Collection
• Validity and Reliability
• Data analysis
• Chapter 4-5: Results and Discussion
• An overview
• Part 1: The concept of CT
• Part 2: The connection to Problem-
solving
• Part 3: Readiness to teach CT
• Conclusions and Recommendations
• References
3. Abbreviations
2
CT: Computational Thinking
CTPF: Computational Thinking
Pedagogical Framework
ISTE: International Society for
Technology in Education
K-12: Kindergarten to grade 12
NSF: National Science Foundation
STEM: Science, Technology, Engineering,
and Mathematics
STEAM: Science, Technology,
Engineering, the Arts and Mathematics
SPSS: Statistical Package for the Social
Sciences
5. 4
As per Wings,
Computational thinking is a way to solve problems and design systems
and it should be integrated into reading, writing, and arithmetic to
improve the analytical skills of every child (Wing J. M., 2006).
Computational Thinking should be implemented since the early years of
childhood education (Wing J. M., 2008).
“Computational thinking will be a fundamental skill used by everyone in
the world by the middle of the 21st Century” (Wing J. M., 2012).
6. In 2018, ISTE
released the
international
standards for
computational
thinking
Educators continually improve their practice by developing an understanding of computational
thinking and its application as a cross-curricular skill. Educators develop a working knowledge of
core components of computational thinking: such as decomposition; gathering and analyzing
data; abstraction; algorithm design; and how computing impacts people and society.
5
Computational Thinking Competencies | ISTE
7. Statement of the Problem
If teaching computational thinking is a cross-curricular skill,
We need to introduce
CT ideas to K-12
teachers explicitly
specifically in primary
grade levels. (Yadav, A.
et al., 2014)
6
It might constitute a
challenge knowing
how, and when to
teach this set of skills
(Wing J. M., 2008).
Do teachers already teach some of
those skills and are ready to include
them all?
Do they need more support to make
this practice more explicit within their
lessons?
How is the relationship between
problem-solving skills and
computational thinking?
It will help the students
think in new ways
which advances their
problem-solving
abilities and skills.
8. Purpose of the Study
7
To examine the effectiveness of teaching computational
thinking skills in subjects that are not related to computer
science to students at the primary level.
To investigate the teachers’ willingness and readiness to teach
CT skills within their subjects.
To investigate the connection that can be found between
computational thinking and problem-solving skills.
9. Rationale
8
There is an increased interest in computational thinking, a
highly demanded skill to teach.
Significant benefits associated with problem-solving, the
importance of teaching CT skills to 21st century students.
(Mohaghegh, M., et al, 2016).
Few research papers were found in Lebanon discussing the
teachers’ perceptions and experiences teaching CT in primary
classes
The need to investigate how we can integrate CT skills in other
subjects than computer science and the changes that might
occur on the students’ problem-solving skills.
To understand the possibility of bringing computational
thinking skills to the repertoire of our curricula, thus, assess
the needs of educators to prepare them for teaching CT skills.
To deliver information about the teachers’
knowledge of computational thinking and the
possibility of teaching it explicitly in different
disciplines at the primary level.
To benefit the students, the teachers, and
individuals who are involved in studying and
mapping the curricula at the primary level to
understand the needs and methods to embed
computational thinking in different subject areas.
To highlight the readiness and the challenges that
the teachers might face while teaching
computational thinking skills.
Significance
10. Research Questions & Hypotheses
9
Q1. To what extent do teachers teach
computational thinking within their
subjects at the primary level?
H01: Teachers do not teach
computational thinking skills
within their subject areas.
H11: Teachers already teach
computational thinking skills
within their subject areas.
Q2. Is there any significant correlation
between computational thinking and
problem-solving skills?
H02: There is no significant
correlation between
computational thinking and
problem-solving skills.
H12: There is a significant
correlation between
computational thinking and
problem-solving skills.
Q3. To what extent do teachers need
support to teach computational
thinking within the curriculum?
H03: Teachers do need support
to teach computational thinking
within the curriculum.
H13: Teachers do not need
support to teach computational
thinking within the curriculum.
12. Theoretical Framework
Teaching Computational
Thinking at the primary level
enhances the students’
problem solving skills
Cognitive
Domain
Teaching CT skills and
the students’ level of
thinking, analytical
abilities
The correlation
between CT and
Problem-Solving
Are teachers ready to
teach CT in their subject
areas?
Teaching CT skills affects the
students’ academic
performance?
Teaching CT as a
21st century skill
Teaching CT as a
cross-
curricular skill
The connection
to Bloom’s
Taxonomy
The connection
to
Constructivism
Figure 1 Theoretical framework 11
13. CT vs. Bloom’s Taxonomy
Figure 2 A Relationship model
In a study that aimed to explore ways
in which computational thinking and
problem-solving can be taught
through programming,
A model represented with an
illustration was developed at the end
of the study by Cynthia Selby (2014),
to show the level of cognitive skills
in Bloom’s Taxonomy that can
relate to computational thinking.
Computational thinking is a subset of
cognitive skills that overlap with
problem-solving and critical thinking,
and that programming is a more
specialized skill.
12
Selby, C. (2014, April). How can the teaching of
programming be used to enhance computational thinking
skills?.
14. CT vs. Constructivism
Figure 3 CTPF Four pedagogical experiences
13
The present focus on teaching computational thinking is not new. It
was renewed while introducing the program LOGO to engage children in
programming a turtle.
Four pedagogical experiences were proposed in the CTPF framework:
remixing, making, tinkering, and unplugged.
CT can be introduced at any level of this model.
The researchers anticipated that the four suggested experiences in the
framework were fundamentally rooted in the constructionism learning
theory, and that students construct intrinsic representations to make
sense of the environment around them and to develop their
knowledge.
The model can also be applied in other disciplines and not only in
mathematics or programming.
Kotsopoulos, D., Floyd, L., Khan, S. et al. (2017, March 9). A Pedagogical Framework for Computational Thinking.
15. Teachers’ Readiness to teach CT
• In a research to study the teachers’
conceptions of computational thinking
(2019), a survey was run by primary
teachers with different domains of specialty
including Music, Arts, social studies, and
other subject areas.
• The survey covered computational thinking
concepts, the possibility of integrating CT,
resources, and the comfort of the teacher.
• Many teachers defined computational
thinking very briefly, and a large number of
them did not mention the subskills such as
abstraction, decomposition, and algorithm.
Garvin, M., Killen, H., Plane, J., Weinntrop, D. (2019).
Primary School Teachers’ Conceptions of Computational
Thinking.
14
Figure 5 Primary teachers' comfort level responses
17. Purpose of the Study
16
To examine the effectiveness of teaching computational
thinking skills in subjects that are not related to computer
science to students at the primary level.
To investigate the teachers’ willingness and readiness to teach
CT skills within their subjects.
To investigate the connection that can be found between
computational thinking and problem-solving skills.
18. Design of the Study
This study is Descriptive and uses the sequential explanatory design.
>>> The emphasis was on the teachers’ practices using computational
thinking.
A variety of methods and data were collected throughout the research,
>>> The researcher chose a Mixed-method: to integrate between
different types of data.
Qualitative and quantitative data were collected to support the
investigation in this study.
“Soft” data were provided.
Empirical data was also recorded.
Documentation for sayings, viewpoints, and recommendations
17
20. 19
Participants, Sampling and Data Collection
Questionnaire
Interviews Activities
using CT skills
Interviews
94 teachers 14 teachers 10 teachers
25.5%25.5%
17.0%
9.6%
5.3%
3.2% 3.2% 3.2% 2.1% 1.1% 1.1% 1.1% 1.1% 1.1%
Figure 6 Teachers’ Questionnaire: Q2. Specific subject area
Grade Level Frequency Percent
Nursery 0 0.0%
KG1 2 14.3%
KG2 0 0.0%
Grade 1 2 14.3%
Grade 2 3 21.4%
Grade 3 1 7.1%
Grade 4 4 28.6%
Grade 5 2 14.3%
Total 14 100.0%
Subject Area Frequency Percent
Arabic 2 14.3%
Math 2 14.3%
Arts 1 7.1%
PE 2 14.3%
French 1 7.1%
Homeroom 4 28.6%
Music 1 7.1%
Drama 1 7.1%
Total 14 100.0%
Table 21 Teachers' Interview before: Subject Specific Area Table 22 Teachers' Interview before: Grade Level
21. Validity and Reliability
• A Cronbach’s alpha test was conducted to examine the internal
consistency, in other words, the reliability of the questions.
• Using SPSS Statistics version 23.
• The questionnaire was divided into three main parts:
oThe concept of computational thinking 0.75
oThe connection to problem solving skills 0.74
oThe teachers’ readiness to teach computational thinking 0.71
Overall result of Cronbach’s alpha test is 0.7 < 0.75 < 0.9
20
22. Data Analysis
• Questionnaire >> Quantitative Data >> SPSS Statistics
• Interviews >> Qualitative Data >> Excel
• A mix of descriptive and inferential statistics.
• The descriptive statistics to describe the data and better understand the details
of the sample population.
• The inferential statistics to make predictions and inferences about the full
population
• One sample t-test was used to evaluate the hypotheses. To compare the
means, a theoretical value equal to 3.5 was used.
• The p-value was used to determine the statistical significance.
• Pearson correlation test was used to study bivariate correlations.
21
23. Theoretical Value
22
*The null hypothesis will be considered if the mean value was equal to or less than 3.5
*The alternative hypothesis will be considered if the value was more than 3.5.
Figure 14 One sample test - Theoretical Value
25. Purpose of the Study
24
To examine the effectiveness of teaching computational
thinking skills in subjects that are not related to computer
science to students at the primary level.
To investigate the teachers’ willingness and readiness to teach
CT skills within their subjects.
To investigate the connection that can be found between
computational thinking and problem-solving skills.
26. An overview…
• General Results
• One Sample T-test
• Pearson Correlation Test
• Results before teaching CT
• Results after teaching CT
• Related Studies
• Answering the research
question
Part 1:
The Concept
of Computational
Thinking
Part 2:
The Connection to
Problem-Solving
Part 3:
Teachers’ Readiness
to teach
Computational
Thinking
Q1. To what extent do
teachers teach
computational thinking
within their subjects at
the primary level?
Q2. Is there any
significant correlation
between computational
thinking and problem-
solving skills?
Q3. To what extent do
teachers need support to
teach computational
thinking within the
curriculum?
25
28. Research
Hypotheses
27
H01: Teachers do not teach
computational thinking skills
within their subject areas.
H11: Teachers already teach
computational thinking skills
within their subject areas.
H02: There is no significant
correlation between
computational thinking and
problem-solving skills.
H12: There is a significant
correlation between
computational thinking and
problem-solving skills.
H03: Teachers do need support
to teach computational thinking
within the curriculum.
H13: Teachers do not need
support to teach computational
thinking within the curriculum.
29. The Concept of Computational Thinking
Teachers’ Questionnaire - General Results
28
3.2 4.3
17.0
53.2
22.3
0.0
8.5
28.7
41.5
21.3
Strongly Disagree Disagree Neutral Agree Strongly Agree
Previous use of CT skills
Percent before CT Definition Percent after CT Definition
Figure 7 Teachers’ Questionnaire: Q3.1-Q.4. Previous use of CT skills
H11 ✔️
30. The Concept of Computational Thinking
Teachers’ Questionnaire - General Results
29
Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
3.1. I have used computational thinking before in my
previous lessons.
94 1 5 3.87 0.919
3.2. Students are well engaged when they solve
problems in activities that I prepared for them.
94 2 5 4.13 0.691
3.3. Students use high levels in Bloom’s taxonomy: the
cognitive domain, to solve problems within my lesson.
94 2 5 3.79 0.746
4. Now that you have read the definition of
computational thinking, do you think that you have
taught or used this skill directly or indirectly before?
94 2 5 3.76 0.888
Computational thinking skills 94 2.00 5.00 3.8856 0.61529
Table 4 Teachers' Questionnaire: Descriptive Statistics 1
H11 ✔️
*Mean value for CT skills: 3.5 < 3.8856
31. The Concept of Computational Thinking
Teachers’ Questionnaire - One Sample T-test
30
One-Sample Test
Test Value = 3.5
t df
P-value (1-
tailed)
Mean
Difference
95% Confidence Interval of
the Difference
Lower Upper
3.1. I have used computational thinking before in
my previous lessons.
3.93 93 0.00 0.37 0.18 0.56
3.2. Students are well engaged when they solve
problems in activities that I prepared for them.
8.80 93 0.00 0.63 0.49 0.77
3.3. Students use high levels in Bloom’s taxonomy:
the cognitive domain, to solve problems within my
lesson.
3.73 93 0.00 0.29 0.13 0.44
4. Now that you have read the definition of
computational thinking, do you think that you have
taught or used this skill directly or indirectly
before?
2.79 93 0.01 0.26 0.07 0.44
Computational thinking skills 6.08 93 0.00 0.39 0.26 0.51
*Mean difference value > 0
*p-value ≃ 0
Table 14 One-Sample Test: Part 1
H11 ✔️
32. The Concept of Computational Thinking
Teachers interviews before teaching CT – Using the brainstorming tool
31
Figure 15 Example of using the brainstorming tool.
Teachers were able to draw
connections.
They listed activities related to CT
skills.
Gave real examples for each
component.
Identified their points of strengths
and weaknesses.
H11 ✔️
33. The Concept of Computational Thinking
Related Studies
32
(Sentance S., Csizmadia A., 2016)
Computing in the curriculum and
teachers’ challenges and
perspectives
• 69% of the teachers already teach computing at
least 5 hours a week.
• Teachers sometimes have difficulties with an in-
depth understanding of some of the
components or how to deliver related activities
(Garvin, M., et al., 2019)
Primary teachers’ conceptions of
computational thinking
• The necessity to create opportunities for
primary teachers so that they build a
conceptual understanding of computational
thinking
• To identify themselves as CT teachers able to
provide a high quality of lessons in
computational thinking
H11 ✔️
34. Research
Hypotheses
33
H01: Teachers do not teach
computational thinking skills
within their subject areas.
H11: Teachers already teach
computational thinking skills
within their subject areas.
❌ ✔️
The null hypothesis H01 is
rejected.
The alternative hypothesis
H11 is accepted instead.
Answering Research Question 1
To what extent do teachers teach computational thinking within their subjects at the
primary level?
H01: Teachers do not teach
computational thinking skills within their
subject areas.
H11: Teachers already teach
computational thinking skills within their
subject areas.
36. Research
Hypotheses
35
H01: Teachers do not teach
computational thinking skills
within their subject areas.
H11: Teachers already teach
computational thinking skills
within their subject areas.
H02: There is no significant
correlation between
computational thinking and
problem-solving skills.
H12: There is a significant
correlation between
computational thinking and
problem-solving skills.
H03: Teachers do need support
to teach computational thinking
within the curriculum.
H13: Teachers do not need
support to teach computational
thinking within the curriculum.
37. The Connection to Problem-solving
Teachers’ Questionnaire - General Results
36
4.3
16.0
13.8
50.0
16.0
5.3
11.7
13.8
46.8
22.3
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
CT vs Problem-Solving Questions
5.2. CT is embedded in problem-solving (%)
5.1. CT is an extension of problem-solving (%)
Figure 9 Teachers' Questionnaire: Q5.1-Q5.2 CT vs Problem-Solving
5.1 Do you agree that computational
thinking is an extension of problem-
solving skills?
5.2 Do you think that computational
thinking is embedded in problem-
solving?
H12 ✔️
38. The Connection to Problem-solving
Teachers’ Questionnaire - General Results
37
Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
5.1. Do you agree that computational thinking is an extension of problem-
solving skills?
94 1 5 3.57 1.073
5.2. Do you think that computational thinking is embedded in problem-solving? 94 1 5 3.69 1.107
5.3. Have you used decomposition before within your lessons? 94 1 5 4.01 0.849
5.4. Have you used pattern recognition within your lessons before? 94 1 5 3.93 0.845
5.5. Have you used abstraction within your lessons before? 94 1 5 3.64 0.902
5.6. Have you used algorithm design within your lessons before? 94 1 5 3.68 0.986
Problem-solving skills 94 2.00 5.00 3.7535 0.63534
Table 9 Teachers' Questionnaire: Descriptive Statistics 2
H12 ✔️
*Mean value for the connection to PS skills: 3.5 < 3.7535
39. The Connection to Problem-solving
Teachers’ Questionnaire - One Sample T-test
38
One-Sample Test
Test Value = 3.5
t df P-value
(1-tailed)
Mean
Difference
95% Confidence Interval
of the Difference
Lower Upper
5.1. Do you agree that computational thinking is not an extension of
problem-solving skills?
0.67 93 0.25 0.07 -0.15 0.29
5.2. Do you think that computational thinking is not embedded in problem-
solving?
1.68 93 0.049 0.19 -0.04 0.42
5.3. Have you used decomposition before within your lessons? 5.83 93 0.00 0.51 0.34 0.68
5.4. Have you used pattern recognition within your lessons before? 4.88 93 0.00 0.43 0.25 0.60
5.5. Have you used abstraction within your lessons before? 1.49 93 0.07 0.14 -0.05 0.32
5.6. Have you used algorithm design within your lessons before? 1.78 93 0.04 0.18 -0.02 0.38
Problem-solving skills 3.87 93.00 0.00 0.25 0.12 0.38
Table 16 One-Sample Statistics: Part 2
*Mean difference value > 0
*p-value ≃ 0 for the majority
H12 ✔️
40. The Connection to Problem-solving
Teachers’ Questionnaire - One Sample T-test
39
One-Sample Statistics
N Mean Std. Deviation Std. Error Mean
5.1. Do you agree that computational thinking is not an extension of
problem-solving skills?
94 3.57 1.07 0.11
5.2. Do you think that computational thinking is not embedded in problem-
solving?
94 3.69 1.11 0.11
5.3. Have you used decomposition before within your lessons? 94 4.01 0.85 0.09
5.4. Have you used pattern recognition within your lessons before? 94 3.93 0.85 0.09
5.5. Have you used abstraction within your lessons before? 94 3.64 0.90 0.09
5.6. Have you used algorithm design within your lessons before? 94 3.68 0.99 0.10
Problem-solving skills 94 3.75 0.64 0.07
Table 17 One-Sample Statistics: Part 2
*Mean value > 3.5
H12 ✔️
41. The Connection to Problem-solving
Teachers’ Questionnaire – Pearson Correlation Test
40
Measurements
0 <|r|< 0.3 Weak correlation
0.3 <|r|< 0.5 Medium Correlation
0.5<|r|≤ 1 Strong Correlation
P-value > 0.05 Non-significant correlation
P-value < 0.05 Significant correlation
Correlations
Computat
ional
thinking
skills
Problem-solving
skills
Readines
s to teach
CT
Computational
thinking skills
Pearson
Correlation
1 .406** -0.03
P-value 0.00 0.80
Problem-solving
skills
Pearson
Correlation
.406** 1 0.19
P-value 0.00 0.07
Readiness to
teach CT
Pearson
Correlation
-0.03 0.19 1
P-value 0.80 0.07
**. Correlation is significant at the 0.01 level (2-tailed).
CT and Problem-Solving
• P-value = 0.00 < 0.05 (significant)
and the correlation value r = 0.406
(positive).
• Positive, medium, and significant
correlation between both factors.
• H12 is supported.
Table 20 Pearson Correlation test
H12 ✔️
42. The Connection to Problem-solving
Teachers interviews after teaching CT
41
1. What are some changes that you noticed with the students while
solving the problem using computational thinking?
Code Response Frequency Percent
1
Students started making more
connections.
5 50.0%
2
Students became more independent while
solving.
1 10.0%
3 more use of CT skills 1 10.0%
4 The students' participation increased. 2 20.0%
5 The students showed more confidence. 3 30.0%
Table 24 Teachers’ Interview after: Q1 Changes
3. How comfortable did you find the students while interpreting or
decomposing a problem?
Code Response Frequency Percent
Valid
Percent
Cumulative
Percent
1 Very comfortable 3 30.0% 30.0% 30.0%
2 Comfortable 5 50.0% 50.0% 80.0%
3 Uncomfortable 2 20.0% 20.0% 100.0%
4
Very
uncomfortable
0 0.0% 0.0% 100.0%
Table 25 Teachers’ Interview after: Q3 Students’ comfortability
H12 ✔️
43. The Connection to Problem-Solving
Related Studies
42
(Selby, 2014)
How can the teaching of
programming be used to enhance
computational thinking skills?
• CT skills consist of a subset of
cognitive skills overlapping with
problem-solving and critical
thinking.
• There is a connection between
CT aspects and high levels of
thinking skills in Bloom’s
taxonomy
(Román-Gonzàlez, M., et al., 2017)
The cognitive abilities underlying
computational thinking
• CT is fundamentally related to
general mental ability in
connection with inductive
reasoning, verbal and spatial
abilities.
• CT portrays problem-solving
processes requiring complex and
higher order of thinking skills.
H12 ✔️
44. H02: There is no significant
correlation between
computational thinking and
problem-solving skills.
H12: There is a significant
correlation between
computational thinking and
problem-solving skills.
Research
Hypotheses
43
❌ ✔️
The null hypothesis H02 is
rejected.
The alternative hypothesis
H12 is accepted instead.
Answering Research Question 2
Is there any significant correlation between computational thinking and problem-
solving skills?
H02: There is no significant correlation
between computational thinking and
problem-solving skills.
H12: There is a significant correlation
between computational thinking and
problem-solving skills.
45. Part 3:
Teachers’ Readiness
to teach
Computational
Thinking
Q3. To what extent do
teachers need support to
teach computational thinking
within the curriculum?
44
46. Research
Hypotheses
45
H01: Teachers do not teach
computational thinking skills
within their subject areas.
H11: Teachers already teach
computational thinking skills
within their subject areas.
H02: There is no significant
correlation between
computational thinking and
problem-solving skills.
H12: There is a significant
correlation between
computational thinking and
problem-solving skills.
H03: Teachers do need support
to teach computational thinking
within the curriculum.
H13: Teachers do not need
support to teach computational
thinking within the curriculum.
47. Readiness to Teach Computational Thinking
Teachers’ Questionnaire - General Results
46
12.8
30.9
40.4
16.0
4.3
21.3
13.8
40.4
20.2
4.3
24.5
12.8
36.2
22.3
Strongly Disagree Disagree Neutral Agree Strongly Agree
Percent of Teachers' Readiness to teach CT
6.1. I feel comfortable enough to teach computational thinking within my lessons.
6.2. I prefer to receive training before I apply computational thinking within my lessons.
6.3. I need more resources to prepare my lessons and teach computational thinking.
Figure 12 Teachers' Questionnaire: Q6.1-3 Readiness to teach CT skills
H03 ✔️
48. Readiness to Teach Computational Thinking
Teachers’ Questionnaire - One Sample T-test
47
One-Sample Test
Test Value = 3.5
t df P-value (1-
tailed)
Mean Difference 95% Confidence
Interval of the
Difference
Lower Upper
6.1. I feel comfortable enough to teach
computational thinking within my lessons.
1.02 93 0.15 0.10 -0.09 0.28
6.2. I prefer not to receive training before I apply
computational thinking within my lessons.
0.09 93 0.46 0.01 -0.23 0.25
6.3. I do not need more resources to prepare my
lessons and teach computational thinking.
-0.17 93 0.43 -0.02 -0.27 0.23
Readiness to teach CT 0.31 93 0.38 0.03 -0.15 0.21
Table 18 One-Sample Test: Part 3
*Mean difference value > 0 for the majority
*p-value > 0.05 for all.
H03 ✔️
49. Readiness to Teach Computational Thinking
Teachers’ Questionnaire - One Sample T-test
48
One-Sample Statistics
N Mean Std. Deviation Std. Error Mean
6.1. I feel comfortable enough to teach computational thinking within my
lessons.
94 3.60 0.91 0.09
6.2. I prefer not to receive training before I apply computational thinking
within my lessons.
94 3.51 1.16 0.12
6.3. I do not need more resources to prepare my lessons and teach
computational thinking.
94 3.48 1.21 0.12
Readiness to teach CT 94 3.53 0.88 0.09
Table 19 One-Sample Statistics: Part 3
*Mean value ≃ 3.5 for the majority.
H03 ✔️
50. Readiness to Teach Computational Thinking
Teachers’ Questionnaire – Pearson Correlation Test
49
Measurements
0 <|r|< 0.3 Weak correlation
0.3 <|r|< 0.5 Medium Correlation
0.5<|r|≤ 1 Strong Correlation
P-value > 0.05 Non-significant correlation
P-value < 0.05 Significant correlation
Correlations
Computat
ional
thinking
skills
Problem-solving
skills
Readines
s to teach
CT
Computational
thinking skills
Pearson
Correlation
1 .406** -0.03
P-value 0.00 0.80
Problem-solving
skills
Pearson
Correlation
.406** 1 0.19
P-value 0.00 0.07
Readiness to
teach CT
Pearson
Correlation
-0.03 0.19 1
P-value 0.80 0.07
**. Correlation is significant at the 0.01 level (2-tailed).
CT and Teachers’ Readiness
• P-value = 0.80 > 0.05 (non-significant)
and the correlation value r = - 0.03
(negative).
• Negative, weak, and non-significant
correlation between both factors.
• H03 is supported.
Table 20 Pearson Correlation test
H03 ✔️
51. Readiness to Teach Computational Thinking
Teachers interviews before teaching CT
50
Frequency Percent
Are you comfortable using Pattern Recognition in your class lessons? 10 71.4%
Are you comfortable using Algorithm Design in your class lessons? 8 57.1%
Are you comfortable using Decomposition in your class lessons? 6 42.9%
Are you comfortable using Abstraction in your class lessons? 8 57.1%
Table 23 Teachers' Interview before: Readiness to teach CT skills
H03 ✔️
52. Readiness to Teach Computational Thinking
Teachers interviews after teaching CT
51
8. What are recommendations that you can give for future research to enhance the teaching and
learning experience in computational thinking?
Code Response Frequency Percent
1 Include CT skills more in the planning process. 2 20.0%
2 Provide more PD opportunities. 2 20.0%
3 More resources and activities for CT skills. 4 40.0%
4 Making it more visible for the students. 4 40.0%
Table 26 Teachers’ Interview after: Q8 Recommendations
*A teacher mentioned that this set of skills is a powerful tool for both teachers and students.
*Some said it’s a metacognitive tool to think about your thinking.
H03 ✔️
53. The Connection to Problem-Solving
Related Studies
52
(Garvin, M., et al., 2019) Primary
teachers’ conceptions of
computational thinking
• More than 50% felt comfortable
creating integrated CT materials
for their lessons.
• More than 60% showed positive
responses about integrating CT
• 40% expressed that they needed
more training
(Good, J., Sands, P. & Yadav, A., 2018)
Computational Thinking in K-12: In-
service Teacher Perceptions of
Computational Thinking
• Teachers need to be trained
regardless of their academic
discipline.
• There is a need to develop the
teachers’ understanding of CT
• PD needs to entail the teachers’
domain of expertise
H03 ✔️
54. Research
Hypotheses
53
H03: Teachers do need support
to teach computational thinking
within the curriculum.
H13: Teachers do not need
support to teach computational
thinking within the curriculum.
❌
✔️
The null hypothesis
H03 is accepted.
The alternative hypothesis
H13 is rejected.
Answering Research Question 3
To what extent do teachers need support to teach computational thinking within
the curriculum?
H03: Teachers do need support to teach
computational thinking within the curriculum.
H13: Teachers do not need support to teach
computational thinking within the curriculum.
56. Purpose of the Study
55
To examine the effectiveness of teaching computational
thinking skills in subjects that are not related to computer
science to students at the primary level.
To investigate the teachers’ willingness and readiness to teach
CT skills within their subjects.
To investigate the connection that can be found between
computational thinking and problem-solving skills.
57. To sum up…
56
Q1. To what extent do teachers teach
computational thinking within their
subjects at the primary level?
H01: Teachers do not teach
computational thinking skills
within their subject areas.
H11: Teachers already teach
computational thinking skills
within their subject areas.
Q2. Is there any significant correlation
between computational thinking and
problem-solving skills?
H02: There is no significant
correlation between
computational thinking and
problem-solving skills.
H12: There is a significant
correlation between
computational thinking and
problem-solving skills.
Q3. To what extent do teachers need
support to teach computational
thinking within the curriculum?
H03: Teachers do need support
to teach computational thinking
within the curriculum.
H13: Teachers do not need
support to teach computational
thinking within the curriculum.
58. Conclusions
The results of this study revealed that:
Teachers already use problem-solving skills that are related to
computational thinking indirectly within their lessons.
Teachers need more training and support to directly target
computational thinking skills.
Teaching computational thinking skills in classes at the primary
level showed a positive effect on the students' problem-solving
abilities as described by the teachers.
57
59. Conclusions
• In a nutshell, computational thinking includes logical reasoning
and sequencing steps that help the students solve problems
taking into consideration different variables and troubleshooting
all along.
• It was proven to be applicable in many contexts outside
computer science and is the essence of our logical reasoning to
solve problems.
58
60. Recommendations
• Making this practice more explicit within different
disciplines.
• To have a framework or a model that the teachers can
rely on so that they measure their application of
computational thinking. (Example “Use-Modify-Create”
model)
• Align the curricula with International Standards that
promoted teaching computational thinking skills such as
ISTE.
• Teachers are advised to review the ISTE standards and
competencies for computational thinking.
• More PD opportunities, workshop training, and lesson
planning to teach using CT skills.
59
Figure 4 Use-Modify-Create learning progression
Allan, W., et al. (2011, March). Computational
Thinking for Youth in Practice.
61. Recommendations for future studies
>>>No sufficient information to better study the students’ academic performance after practicing and
using those skills.
>>>No sufficient information about how to integrate computational thinking skills within the curriculum.
• Future investigations into the effect of teaching computational thinking skills specifically on
the students’ problem-solving and cognitive abilities.
• More investigations need to focus on the Lebanese curriculum and how to improve its
framework to align with international standards that promote teaching computational
thinking.
• Further investigations to scale the use of CT skills in the Lebanese schools and the teachers’
readiness to teach those skills.
• The difference in practices between private and public schools in Lebanon.
• Including courses that help prepare undergraduates in the Faculties of Education to teach
computational thinking.
60
62. References
• Allan, W., et al. (2011, March). Computational Thinking for Youth in Practice. acm Inroads, 2(1), 32-37.
• Barr, D., et al. (2011, March/April). Computational Thinking: A Digital Age. Learning & Leading with Technology, pp. 20-23.
• Barr, V., Stephens, S. (2011). Bringing Computational Thinking to K-12: What is Involved and What is the Role of the Computer Science Education Community? Acm Inroads, 2(1), 48-54.
• Bers, M. U. (2017). Coding as a Playground Programming and Computational Thinking in the Early Childhood Classroom. London, New York, USA and UK: Routledge.
• Bers, M. U. (2018). Coding, Playgrounds and Literacy in Early Childhood Education: the Development of KIBO Robotics and ScratchJr. 2018 IEEE Global Engineering Education Conference (EDUCON) (pp. 2100-2108). Santa Cruz de Tenerife,
Canary Islands, Spain: IEEE.
• Bers, M.U., Sullivan, A. (2015, March 9). Robotics in the early childhood classroom: learning outcomes from an 8-week robotics curriculum in pre-kindergarten through second grade.
• Garvin, M., et al. (2019). Primary School Teachers’ Conceptions of Computational Thinking. Paper Session: Computational Thinking 2, 19, 899-905.
• Good, J., Sands, P. & Yadav, A. (2018). Chapter 8: Computational Thinking in K-12: In-service Teacher Perceptions of Computational Thinking. Computational Thinking in the STEM Disciplines.(978-3-319-93566-9), 151-164.
• ISTE. (2018). Computational Thinking Competencies. Retrieved from ISTE: https://www.iste.org/standards/computational-thinking
• ISTE, CSTA, NSF. (2011). Computational Thinking Leadership Toolkit. Computer Science Teachers Association (CSTA) and the International Society for Technology in Education (ISTE) supported by the National Science Foundation. ISTE.
• Kotsopoulos, D., et al. (2017, March 9). A Pedagogical Framework for Computational Thinking. Digit Exp Math Educ, 3(DOI 10.1007/s40751-017-0031-2), 154–171.
• Mathewson, T. G. (2016, November 4). Teachers already incorporate computational thinking into lessons. Retrieved from K-12 Drive: https://www.k12dive.com/news/teachers-already-incorporate-computational-thinking-into-
lessons/429780/
• Mohaghegh, M., et al. (2016). Computational Thinking: The Skill Set of the 21st Century. International Journal of Computer Science and Information Technologies, 7(3), 1524-1530.
• Román-Gonzàlez, M., et al. (2017). Which cognitive abilities underlie computational thinking? Criterion validity of the Computational Thinking Test. Computers in Human Behavior, 72(ISSN 0747-5632), 678-691.
• Selby, C. (2014, April). How can the teaching of programming be used to enhance computational thinking skills? University of Southampton, Faculty of Social and Human Sciences.
• Sentance S., Csizmadia A. (2016, April 5). Computing in the curriculum: Challenges and strategies from a teacher’s perspective. 22(Educ Inf Technol (2017) ), 469-495. Retrieved from http://Springerlink.com
• Wing, J. M. (2006, March). Computational Thinking. Communications of the ACM, 49(3), 33-35.
• Wing, J. M. (2008, July 31). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society(366), 3717-3725.
• Wing, J. M. (2011). Computational thinking. Presentation given at bi-annual OurCS conference. Pittsburg, PA: Carnegie Mellon University.
• Wing, J. M. (2012). Computational Thinking. Tianjin, China: Microsoft Asia Faculty Summit - Computer Science Department.
• Yadav, A., et al. (2014, March). Computational Thinking in Elementary and Secondary Teacher Education. ACM Transactions on Computing Education, 14.
• Yadav, A., et al. (2016, May 30). Computational Thinking for All: Pedagogical Approaches to Embedding 21st Century Problem Solving in K-12 Classrooms. TechTrends, 60, pp. 565–568. Retrieved from Springer:
https://doi.org/10.1007/s11528-016-0087-7
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If teaching computational thinking can be used in any discipline, then it will involve, whether directly or indirectly, all the educators that teach other subject areas and not just computer science.
If we add computational thinking to the selection of problem-solving abilities, it might constitute a challenge for educators in knowing how, and when to teach this set of skills (Wing J. M., 2008).
In another study, computational thinking helps the students think in new ways which advance their problem-solving abilities and skills. It is important to introduce CT ideas to K-12 teachers explicitly specifically in primary grade levels. (Yadav, A. et al., 2014)
Many researches have shown that there is an increased interest in computational thinking and that it’s a highly demanded skill to teach.
Significant benefits associated with problem-solving have been proved emphasizing the importance of teaching CT skills to 21st century students. (Mohaghegh, M., et al, 2016).
Few research papers were found in Lebanon discussing the teachers’ perceptions and experiences teaching CT in primary classes, as well as, the effect of teaching CT skills on the students’ problem-solving skills.
There is a significant need to investigate how we can integrate CT skills in other subjects than computer science and the changes that might occur on the students’ problem-solving skills, this is in addition to teaching it to young students.
By tackling this problem, we can better understand the possibility of bringing computational thinking skills to the repertoire of our curricula, thus, assess the needs of educators to prepare them for teaching this new form of thinking skills in classes at the primary level.
Based on Cynthia Selby’s model, abstraction and decomposition were classified at the same level as analysis in Bloom’s Taxonomy, the cognitive domain. Algorithm design was assimilated with synthesis, and evaluation was at the top level of the model. The findings of the study reassured that computational thinking is not necessarily limited to programming language and that the skills that are taught through computational thinking are represented at the higher level of thinking in Bloom’s Taxonomy.
The researchers suggested that the CTPF can be remarkably useful for educators who have limited knowledge and understanding of computational thinking.
The confusion that the teachers encountered while putting a definition resulted in not knowing how to answer questions that were related to the resources to teach it.
Teachers who were specialized in English, mathematics, and sciences were more likely to mention that they already teach computation thinking within their subject areas.
Less than half of the population agreed that they have received sufficient training to teach computational thinking. An evident number of the population chose to stay neutral about the comfort question.
Descriptive and uses the sequential explanatory design:
It identifies the performances, viewpoints of teachers and focused groups, to understand and report their reflections regarding the development of students’ computational thinking and problem-solving skills in different subject areas.
“Soft” data were provided to measure attitudes and the cognitive aspect which lead to evaluating the hypotheses that were written in chapter 1, and arriving at conclusions for future research in the same field.
Empirical Data: There was documentation for the participants’ sayings, viewpoints, and recommendations about the application of computational thinking in the classroom. Observations for specific behaviors and interpretations were conducted.
The sort of data that was collected in this research is soft data. Three different instruments were used to collect data in addition to observations and demonstrated sessions to teach computational thinking skills in specific disciplines.
45 teachers filled out the survey and expressed their interest to try computational thinking skills in the next phases of the study.
A group of 49 educators on social media applications such as Facebook and LinkedIn also completed the questionnaire.
Cronbach’s alpha is the most common test used to measure the internal reliability or the items interrelatedness of Likert scaled questions in a survey (Bonett, D. G., Wright, T. A., 2014).
The recorded value of a Cronbach’s alpha test ranges between 0 and 1 whereby the acceptable values range from 0.70 to 0.95. The closer the value is to 1, the better is the internal consistency of the variables. If the value of alpha was lower than 0.7, this indicates that the correlation between the items is poor and some questions should be rewritten or revised. If the value of alpha was higher than 0.90, there is a possibility of redundancy in the questions of the survey (Tavakol, M., Dennick, R., 2011).
The result indicates an acceptable consistency in measurement which is higher than 0.7 and lower than 0.9. This also indicates that there is a good correlation between the items thus, the questionnaire can be considered as reliable.
Descriptive and inferential statistics.
The descriptive statistics help describe the data set that was collected from the questionnaire to better understand the details of the sample population.
The inferential statistics help the researcher make predictions and inferences about the full population using the data that was collected from the sample population.
Theoretical Value
If the value of the mean in One sample t-test ≤ 3.5, the null hypothesis will be supported whereas, if the value of the mean > 3.5, the alternative hypothesis will be supported. A null hypothesis indicates that no relationship exists between the variables whereas, the alternative hypothesis indicates that there is a positive or negative relationship that exists.
P-Value: By setting the confidence level as 95%, the significance value will be 0.05, hence, the p-value must not exceed 0.05
Pearson Correlation Test: To compare two variables and study the relationship in between them, a descriptive statistic should be used to find the correlation coefficient. The latter is a numerical value which indicates the strength to which two variables are related and whether the detected relationship is positive or negative (Aldrich, O.J., Cunningham, J.B., 2016).
A high percentage of the teachers used computational thinking before. Almost 75% agreed or strongly agreed that they have used CT before during their lessons.
Neutral responses increased by 10% after reading the definition of CT.
Agreed or Strongly Agree responses dropped by 10% after reading the definition of CT.
A minimal percentage of teachers disagreed or strongly disagreed about the use of CT skills before.
The mean value for each question is significantly higher than 3.5.
As shown in the table, the mean difference value in questions 3.1 to 4 is higher than 0 and the p-value for each is equal or close to 0.00, which is lower than 0.05.
Using the brainstorming tool, the researcher helped the teachers identify activities and practices that they already do within their sessions in connection with the set of skills in computational thinking. They were able to draw connections with every component of computational thinking and to give examples for each. The following figure represents an example of a brainstorming tool that was used with a homeroom teacher in KG1.
After discussing the results, the researcher found that the teachers already teach computational thinking directly or indirectly within their practices and can provide examples about how to integrate those skills.
The results for the t-test in this part of the questionnaire indicate that the null hypothesis H0 is rejected and that the alternative hypothesis H1 is accepted instead. This validates that the teachers already teach computational thinking skills within their subject areas as mentioned in the first alternative hypothesis (H11).
Given the results, the teachers have a little confusion between both concepts.
The results are very similar whether that CT skills are embedded in problem-solving or if they are an extension of those skills.
The mean values for all the questions were higher than 3.5 which supports the alternative hypothesis that there is a significant connection between CT and PS.
The standard deviation for questions 5.1 and 5.2 about if CT is embedded or an extension of problem solving is higher than 1 which indicates a high distribution in the responses while answering those questions.
The mean difference value in all the questions is higher than 0 and the P-value is higher than 0.05 except for questions 5.1 and 5.5.
The p-value in question 5.1 is 0.25 > 0.05 and in question 5.5 is 0.07 > 0.05, which leads to accepting the null hypothesis for both questions and rejecting the alternative hypothesis.
As for the rest of the questions in this part, the P-value is less than 0.05 which is an accepted value.
The majority of the results in this table prove that the teachers already teach problem-solving skills within their subject areas as part of computational thinking. Hence, the null hypothesis will be rejected and the alternative hypothesis will be accepted instead.
This indicates that there is a positive connection between problem-solving and computational thinking, in other words, there is a significant correlation between computational thinking and problem-solving skills as mentioned in the second alternative hypothesis (H12).
In terms of the connection between computational thinking and problem-solving skills, P-value = 0.00 < 0.05 (significant) and the correlation value r = 0.406 (positive). The results indicate that there is a positive, medium, and significant correlation between both factors. As such, the second alternative hypothesis will be supported, i.e. there is a significant correlation between computational thinking and problem-solving skills (H12).
The majority of the results in this table prove that the teachers already teach problem-solving skills within their subject areas as part of computational thinking.
This indicates that there is a positive connection between problem-solving and computational thinking, in other words, there is a significant correlation between computational thinking and problem-solving skills as mentioned in the second alternative hypothesis (H12).
A cumulative of almost 56% agreed or strongly agreed of being comfortable using CT within their lessons.
A cumulative of almost 60% agreed or strongly agreed about receiving training before applying CT skills.
A cumulative of almost 58% expressed that they needed more resources to prepare lessons with CT.
30% of the population were neutral about being comfortable enough using CT in their lessons.
The mean difference value for all the questions is higher than 0 except for question 6.3 which is -0.02 < 0.
The p-value for all the questions is higher than 0.05 which indicates that there is a possibility of accepting the null hypothesis.
By looking at the mean values for all the questions, the results are too close to 3.5 which indicates that the null hypothesis is accepted for all the questions in this case and that the alternative hypothesis is rejected.
In terms of the connection between computational thinking skills and the teachers’ readiness to teach those skills, P-value = 0.80 > 0.05 (non-significant) and the correlation value r = - 0.03 (negative). The results indicate that there is a negative, weak, and non-significant correlation between both factors. As such, the third alternative hypothesis is not supported and the null hypothesis is accepted, i.e. teachers do need support to teach computational thinking within the curriculum (H03).
The results in the table showed that the teachers were more comfortable connecting ideas to pattern recognition than to the other components in computational thinking. Decomposition was a bit challenging for many teachers.
The teachers reflected on their experiences and gave recommendations for future investigations in computational thinking skills.
A teacher mentioned that this set of skills is a powerful tool for both teachers and students. She also mentioned that it’s a metacognitive tool to think about your thinking. Amongst the recommendations given by the teachers, many of them mentioned that they needed more resources to learn about computational thinking and how to make it visible to the students. Some teachers mentioned the importance of including computational thinking skills in their planning process and asked for more professional development opportunities in this context.
The responses in this part of the questionnaire proved that the teachers are not ready yet to teach computational thinking skills which validates the null hypothesis that the teachers need support to teach computational thinking within the curriculum (H03).
It is imperative that the teachers understand the concept of computational thinking and that they learn how to apply those skills before that they teach them to the students.
