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Asia-Pacific Forum on Science Learning and Teaching, Volume 21, Issue 1, Article 6 (Dec., 2021) |
Effect of gender
Independent samples t-test was applied to the data obtained to determine whether the scores obtained from the subscales of STEM questionnaire differed according to gender. When the results of the analysis were examined, it was seen that the scores of the male and female participants for each subsection did not differ (p> .05).
Effect of educational background
Independent samples t test was applied to the data obtained to determine whether the scores obtained from the subscales of the STEM questionnaire differed according to the educational background variable. When the results of the analysis were examined, it was seen that only the scores obtained in the ninth subsection of the questionnaire differed according to the level of education [t (78) = -2.61, p <.05]. According to this, the average score of the participants with a bachelor's degree in the ninth part was found to be significantly lower than that of the participants with a graduate level. This means readiness of participants with bachelor’s degree was found to be significantly lower than that of the participants with a graduate level. Information on this result was presented in Table IV.
Table IV. Effect of educational background
Variables
N
Mean
Sd
df
t
p
T-STEM Survey
9th subscale
Undergraduate
45
11.18
4.73
78
-2.61
0.01
Graduate
35
13.89
4.42
Effect of workplace type (school)
Independent samples t-test was applied to the data obtained to determine whether the scores obtained from the subscales of STEM questionnaire differed according to the type of workplace (private school and public school). When the results of the analysis were examined, it was found that the scores obtained from the subscales of the participants did not differ according to their studies in public or private schools (p> .05).
Effect of having previous STEM training
Independent samples t-test was applied to the data obtained to determine whether the scores obtained from the subscales of STEM questionnaire differed according to whether they had received STEM education before. When the results of this test were examined, it was seen that the scores of the participants differed only in the ninth subsection of the STEM questionnaire subscale [t (41.1)=-4.26, p<.05]. Accordingly, the mean scores of the participants who received STEM training from the ninth part of the questionnaire were significantly higher than those of those who did not have STEM education. However, Levene’s test results showed that the variances were not homogeneous (p <0.05). A significant difference between the number of people in the two groups (with and without STEM training) compared should be taken into account when evaluating the results. Information on the results of this analysis was presented in Table V.
Table V. Effect of having previous STEM training
Variables
N
Mean
Sd
df
t
p
T-STEM Survey
9th subscale
Without training
62
11.44
4.76
41.1
-4.26
.001
With training
18
15.56
3.20
Effect of STEM education usage in lessons
Independent samples t-test was applied to the data obtained in order to find out whether the scores obtained from the subscales of STEM questionnaire differed according to whether they used to teach before using STEM or not. When the results of this test were examined, it was seen that the scores of the participants differed only in the ninth subsection of the STEM questionnaire subscales [t (41.1) = -3.02, p < .05]. Accordingly, the mean scores of the participants who had previously experienced STEM education from the ninth subsection of the questionnaire were significantly higher than those not experienced STEM education. Levene’s test results showed that the variances were not homogeneous (p <.05). This should be considered when examining the results. Information on the results of this analysis can be examined in Table VI.
Table VI. Effect of using STEM education in lessons
Variables
N
Mean
Sd
df
t
p
T-STEM Survey
9th subscales
Not using STEM Education
56
11.46
4.94
58.44
-3.02
.01
Using STEM Education
24
14.46
3.64
Inferential Statistics
Pearson Product-Moment Correlation analysis was conducted to investigate the relationships between the sub-dimensions of STEM questionnaire, age, and professional experience variables. Correlation analysis can be examined in Table VII. As it seen in the Table VII, science teaching efficacy and beliefs has positive and meaningful correlation with science teaching outcome expectancy (r=.54, p< .01), mathematics teaching efficacy and beliefs (r=. 23, p< .05), mathematics teaching outcome expectancy (r=.26, p< .05), students’ technology use (r=.38, p< .01), elementary STEM instruction (r=.56, p< .01), 21st century learning attitudes (r=.34, p< .01), teacher leadership attitudes (r=.58, p< .01) and STEM career awareness (r=.35, p< .01). Similarly, science teaching outcome expectancy has positive and meaningful correlation with mathematics teaching outcome expectancy (r=.49, p< .01), students’ technology use (r=.32, p< .01), elementary STEM instruction (r=.47, p< .01), 21st century learning attitudes (r=.41, p< .01) and teacher leadership attitudes (r=.43, p< .01).
On the other hand, mathematics teaching efficacy and beliefs has positive and meaningful correlation with only mathematics teaching outcome expectancy (r=.45, p< .01). According to Table VII, mathematics teaching outcome expectancy has positive and meaningful correlation with students’ technology use (r=.27, p< .05), elementary STEM instruction (r=.30, p< .01), 21st century learning attitudes (r=.29, p< .01) and teacher leadership attitudes (r=.37, p< .01).
Table VII. Correlation coefficients between variables
Variables
1
2
3
4
5
6
7
8
9
10
11
1 Part 1
-
2 Part 2
.54**
-
3 Part 3
.23*
.12
-
4 Part 4
.26*
.49**
.45**
-
5 Part 5
.38**
.32**
.16
.27*
-
6 Part 6
.56**
.47**
.17
.30**
.55**
-
7 Part 7
.34**
.41**
.05
.29**
.25*
.40**
-
8 Part 8
.58**
.43**
.18
.37**
.26*
.53**
.56**
-
9 Part 9
.35**
.17
.03
.16
.23*
.40**
.24*
.29**
-
10 Age
.14
.11
-.17
.11
-.04
-.02
.16
-.01
.02
-
11 Exp.
.16
.07
-.16
.12
.01
.07
.16
.02
-.01
.84**
-
*p<0.05, **p<0.01 Students’ technology use has positive and meaningful correlation with elementary STEM instruction (r=.55, p< .01), 21st century learning attitudes (r=.25, p< .05), teacher leadership attitudes (r=.26, p< .05) and STEM career awareness (r=.23, p< .05). Elementary STEM instruction has positive and meaningful correlation with 21st century learning attitudes (r=.40, p< .01), teacher leadership attitudes (r=.53, p< .01) and STEM career awareness (r=.40, p< .01). 21st century learning attitudes has positive and meaningful correlation with teacher leadership attitudes (r=.56, p< .01) and STEM career awareness (r=.24, p< .05). Teacher leadership attitudes has positive and meaningful correlation with STEM career awareness (r=.29, p< .01).
As it was stated earlier, science teaching outcome expectancy (Part 2), mathematics teaching outcome expectancy (Part 4) and teacher leadership attitudes (Part 8) were specified as “attitude” related parts of the T-STEM questionnaire; science teaching efficacy and beliefs (Part 1), mathematics teaching efficacy and beliefs (Part 3) and STEM career awareness (Part 9) were specified as “readiness” related parts of the T-STEM questionnaire; student technology use (Part 5), elementary STEM instruction (Part 6) and 21st century learning attitudes were specified as “attitudes and readiness effect on implementation” related parts of the T-STEM questionnaire. As it was stated above in, attitudes related parts of T-STEM questionnaire and readiness related parts of T-STEM questionnaire have positive and meaningful correlation with implementation of STEM education related parts of T-STEM questionnaire. Attitudes of the science teachers has positive and meaningful correlation with readiness of the science teachers (r=.58, p< .01). Implementation of STEM education has positive and meaningful correlation with attitude of the science teachers (r=.64, p< .01) and readiness of the science teachers (r=.93, p<.01). These correlations can be interpreted as if a science teacher has a positive attitude and readiness, this teacher may be use STEM education in his/her lessons. When Table IX was examined, it was found that the attitudes and readiness of the participants about STEM significantly predicted the implementation of STEM. Attitude and readiness variables together account for 87% of the total variance (R2 = .87, F (2.78) = 255.84, p <0.01). Relationships among attitude and readiness of the science teachers and implementation of STEM can be examined in Table VIII and Table IX.
Table VIII. Relationships among attitude, readiness and implementation of STEM
Variables
1
2
3
1 Attitude
-
2 Readiness
.58**
-
3 Implementation
.64**
.93**
-
*p<.05, **p<.01 Table IX. Variables that predict STEM implementation
Variables
R
R2
Beta
F
t
Attitude
.15
3.12**
Readiness
.93
.87
.84
255.84
17.12**
*p<.05, **p<.01 The reason why the variance is so high may be that attitude and readiness are sub-sections of the T-STEM survey. However, the researcher analyzed the collected data looking for significant statements that explains science teachers’ attitudes, views and readiness for STEM education. For the purpose of the study, it is expected to approach collected data inductively.
In order to understand views of the science teachers on STEM education and STEM integrated science curriculum, interviews done. The responses of the participants translated to English while quoting. The combination and analysis of responses created deeper understanding of mindset of science teachers. Responses relating views of the teachers on STEM education and STEM integrated science curriculum has been gathered through interview questions and combined. These combined responses coded. Then codes come together around two main themes which are definition of STEM and features of STEM learning resources. Frequency of the codes can be examined in Table X. The findings indicated responses of participants revolve around two main themes which are definition of STEM and features of STEM. Regarding definition of STEM education, 70% of the science teachers defined STEM education as an integrated disciplines. An example of participants’ responses is follows:
“STEM education is mixture of science, technology, engineering and mathematics.”
Table X. Interview themes and frequency of codes
Themes
Codes
# of participants mentioned
%
Definition of STEM
Integrated disciplines
7
70
Teaching approach
9
90
Acronym
3
30
Teamwork
5
50
Design process
9
90
Engineering
3
30
Features of STEM
Usable & Helpful
6
60
Implementable in our country
6
60
Science-based
8
80
Project-based
9
90
Not Implementable in our country
4
40
Learnable
7
70
Un-learnable
3
30
Total
10
100
90% of participants defined STEM as teaching approach to use in science lessons. The reason for this response might be the fact that participant group of the study consist of science teachers. Also, this definition might be a sign of science teachers’ perception regarding STEM because they accept it as a compulsory that comes from new arrangements of the science curriculum. An example of participants’ responses:
“STEM is the new teaching approach that we should use in our science classes because of the fact that it is integrated in the new science curriculum.”
30% of the participants defined STEM as an acronym that stands for Science, Technology, Engineering and Mathematics. This might be seen as superficial, the reason for these responses might be the fact that when some of participants graduated, STEM education is not included to the teacher training curriculums. Also, since they had many years of work experience, it might cause vocational blindness as a barrier to adapt new trends. An example of participants’ responses:
“STEM is just an acronym that stands for science, technology, engineering and mathematics.”
90% of participants defined STEM as design process and 30% of participants defined STEM as engineering. Because of the fact that STEM is accepted as a project-based approach, majority of participants defined STEM as a design process. On the other hand, although engineering is one of the components of STEM, responses of participants may be intended to state STEM as a baseline for engineering profession, which might cause specialization of STEM education for engineering profession.
“STEM is a mixture of all sciences that are required for mainly become an engineer. You need to know science and mathematics and you need to use technology in order to be an engineer.”
There is not a wrong or right definition of STEM. Although there are widely accepted definitions like Capraro, & Slough (2013) and Capraro et al. (2013), stated; as an interdisciplinary educational approach, STEM (Science, Technology, Engineering and Mathematics) encircles K-12 curriculum. STEM education should not be bordered with definition but expressed according to perspectives. That is why, it is expected to receive different definitions as responses.
Regarding features of STEM education, majority of participants described STEM as project-based education (90% of participants) and science-based education (80% of participants) defined STEM education as an integrated discipline. Two examples of participants’ responses are follows:
“When I assigned the project to the students, I try to follow STEM education approach as much as I can. I first want my students to find real world problem and I want them to make brainstorming about the possible solutions. Then, I want them to visualize their solutions.”
“I try to explain scientific background of all topics. Because, I think, STEM approach is all about science. Technology, engineering and mathematics all of them are branches of science. That is why, I think, STEM means science.”
Although 70% of the participants think that STEM is learnable and they attend courses and workshops to learn STEM, 30% of the participants think it is not learnable. Even, the veteran science teachers who are participants of the present study, although they spend many years in the profession, they still attend courses and workshops to learn STEM. Reason of this resistance to learn STEM might arise from their opinion, which is that STEM is not implementable in Turkey because of inefficient physical conditions, insufficient teacher education and high student population.
“I attended a workshop to learn STEM. It did not provide comprehensive knowledge but I learned its mindset. However, when I try to implement it in my classes, it was so hard to manage process. The time was not enough, number of students was high and we did not enough material to use. So, I think, unfortunately, STEM is helpful approach however it is not implementable in our country.”
When taking into consideration all the aforementioned analysis, it was emerged in the interviews that majority of the participants think STEM is useful and helpful approach to teach science, technology, engineering and mathematics to students. However, although they think it can be learnable, they think it is hard to implement in our country because of the circumstances.
