*Article* **Searching for Pedagogical Answers to Support STEM Learning: Gender Perspective**

**Linda Daniela 1,\*, Silvija Kristapsone 2, Gunta Kra ' ge 3, Ludmila Belogrudova 4, Aleksandrs Vorobjovs <sup>1</sup> and Ilona Krone <sup>1</sup>**

	- <sup>3</sup> Regional Centre, University of Latvia, LV-1083 Riga, Latvia

**Abstract:** This article analyzes the results of a study on the situation concerning the educational achievements of girls and boys in the STEM field in Latvia. The study was conducted at the compulsory education level to understand the conditions in the learning environment that can predictably affect the academic achievements of girls. For the purposes of the study, a survey questionnaire was developed, which was filled out by 1847 students from the 7th–12th grades. The obtained results show which factors affect learning achievement in STEM subjects for students with low and high learning achievements in groups divided into boys and girls and which pedagogical activities can predictably improve STEM learning achievements. The research data confirm that there are differences in predicted learning achievements in groups of boys and girls with high learning achievements, depending on the pedagogical strategy used. Additionally, the results of the study confirm that the most significant factor for a predictable increase in learning achievements in the STEM field is students liking mathematics and, in the case of high learning achievements, them liking chemistry. An indicator that has a significant negative impact on academic achievement in STEM subjects is a dislike of physics, which appeared in the group of girls with low academic achievements. Liking other STEM subjects to improve student achievement did not appear statistically significant in any of the analyzed groups. This study is essential to supplement the knowledge base on gender differences in learning achievements in STEM subjects and is also important for the educational space of Latvia because the learning achievements of girls in the STEM field are lower in this country than it is for boys. The obtained results show which methods of pedagogical work have a higher impact on increasing the predicted learning achievements and also show potential future research directions.

**Keywords:** gender differences; STEM; learning outcomes; compulsory education; pedagogical methods

#### **1. Introduction**

It is known that there are relatively few women in the field of STEM (Science, Technology, Engineering, and Mathematics) [1–3] and that girls generally have lower academic achievement in STEM subjects [4,5]. There are many studies in which researchers have tried to find answers that explain the fact that girls both have lower academic achievement in the the STEM field and are still not choosing to study it in depth. Some studies have found evidence that stereotypes play a big role [6]. As a result, girls develop a negative attitude toward mathematics and other STEM subjects and this attitude, in turn, affects girls' academic achievements and whether they will choose to learn in-depth knowledge related to this field. This attitude is formed under the influence of teachers and parents [7] because they are three times more likely to associate STEM sciences with boys than with girls and these beliefs are internalized by the children they are with on a daily basis [8]. The majority of girls have to face gender discrimination in one of its forms as early as

**Citation:** Daniela, L.; Kristapsone, S.; Kra ' ge, G.; Belogrudova, L.; Vorobjovs, A.; Krone, I. Searching for Pedagogical Answers to Support STEM Learning: Gender Perspective. *Sustainability* **2022**, *14*, 14598. https:// doi.org/10.3390/su142114598

Academic Editors: Noora J. Al-Thani and Zubair Ahmad

Received: 2 September 2022 Accepted: 2 November 2022 Published: 7 November 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

their teenage years and, while this is most often from their peers, it is often also from adults with whom the children are together with on a daily basis—teachers, school staff, and even parents [7,9]. This experience can be of different levels of intensity, including when experiencing a stereotypical attitude, when faced with seemingly innocent comments about their appearance, and they can be offensive gestures or involve ignoring the female's expressed opinion.

#### **2. Literature Review**

Gender stereotypes have been found to affect girls who are considered gifted in STEM fields [10] because stereotypes in society can influence their choices and girls who do well in STEM have been found to choose a career in the humanities, life sciences, or social sciences instead of computer science, engineering, mathematics, or physics [6,11]. This is linked to regular expressions of microaggressions, which contribute to the fact that not everyone has equal access to STEM knowledge and contributes to the perception among girls that the STEM field is not for them [12,13]. The term "microaggression" was coined by Pierce (1970) [14] and, since then, there have been countless studies showing that it has a significant impact on underrepresented groups, which in the context of this study are girls in the STEM field. Both microassaults and microinsults accumulate and, over time, females begin to feel less capable in engineering because they feel marginalized, ignored, or even prevented from working on more serious projects in the STEM field [15]. The impact of microaggression on girls' choices is surprisingly high and it has been shown that girls exposed to microaggressions (compared to a control group) indicated a significantly lower desire to choose a career related to STEM [16,17].

Kessels (2005) [18] and Hannover and Kessels (2004) [19] concluded in their research that girls who like and are good at physics are considered more masculine and that the girls themselves perceived themselves as unpopular among boys. The boys' answers to the questions asked by the researchers also showed that they do not like girls who are interested in physics. Another study concluded that girls lose interest in STEM at the age of 10–15 [20]. The results of these studies may be one of the explanations why the break in girls' interest in the STEM field occurs precisely in adolescence, that is, when children begin to feel a romantic attraction toward each other, but there is no exact answer to the question of how to maintain this interest using pedagogical methods in the daily learning process.

Girls who believe that STEM is a subject for girls are better able to resist the discrimination and stereotyping they experience and this does not affect their motivation to study [21]. It is important to understand how to promote the development of motivation because it has been found that those students who have already developed a negative attitude toward STEM and who are indifferent to their learning achievements are no longer affected by various stereotypes [22]; these findings point to the importance of being purposeful in order to maintain this motivation toward STEM.

There are researchers who believe that apart from the influence of parents and educators, the surrounding environment is also important. For example, females who are in minority conditions showed a deficit in accuracy vis à vis females who were placed in same-sex conditions [23]. Additionally, setting the environment so that there are many references to the role of women, which can be expressed in the choice of learning materials or the colors of learning spaces and materials, can affect the learning achievements of girls [7,24]. One study experimentally demonstrated both that the pre-test information girls receive can lower their performance on that particular test and even that telling girls before the test that girls tend to perform lower academically on these types of tests can result in lower scores, but if such information is not provided before the test, then the results do not differ between boys and girls. This experiment also proved that negative self-esteem has an impact on academic achievement [22]. Similar conclusions were reached in another study, which found that boys and girls show equivalent learning results when their gender identity is not activated [25]. This evidence clearly shows that stereotypes have a huge impact on academic achievement and that girls' academic achievement can be identical

and surely also superior to that of boys, so there is no biological basis for differences in academic achievement.

Sometimes there are ideas that it is necessary to organize the learning process differently for girls and that perhaps STEM lessons should be organized in gender-separated groups. However, such a division is opposed by the results of studies that confirm that working together in mixed-gender groups contributes to a more inclusive environment [26] and can reduce gender stereotypes [27], which is contrary to research that shows girls can achieve higher results if they can work in a single-gender group [23]. The only way to resolve this contradiction is to reduce stereotypes and microaggressions against girls in the STEM field because gendered groups contribute to the formation of stereotypes.

It is essential for educators to be aware of the possible influence of stereotypes on academic achievements in the group of girls, as well as of various manifestations of microaggressions, which gradually push girls out of the STEM field. It is essential to apply pedagogical work strategies that have a positive outcome to the learning achievements of all students because the teacher's pedagogical activity can be both a support point and can create barriers for girls' achievements in the STEM field [28–30].

The objective of the present research is to analyze the data obtained and understand which pedagogical methods predictably have the greatest potential impact to increase academic achievement in the STEM field. Initially, the article analyzes the relevant literature on girls in STEM, then it describes the methodology used, the research design, and the findings, then discusses what was learned. This paper adds to the literature in several ways. First of all, these data were obtained in one specific country where, until now, no studies have been conducted on the factors that can increase educational achievements in the STEM field and it is important to understand whether the results of the study resonate with those conducted in other countries. Second, it compares several groups of students: students with high and low academic achievements and high- and low-achieving boys and girls. These data will enable the modeling of pedagogical processes to remove barriers to girls entering the STEM field. The results of this study will expand the understanding of attitudes toward STEM, academic achievement, and the meaning of pedagogical work from a gender perspective.

#### **3. Disclosure of Context**

This article is based on data obtained from European Social Fund Project No. 8.3.1.1/16/I/002, "support for the implementation of national and international events for the development of educational talents", but it should be pointed out that it only analyzes the data that provide answers as to which pedagogical methods have the greatest potential impact on improving academic achievement among groups of high- and low-achieving girls and boys in the STEM field. The rest of the data will be analyzed in future articles.

#### **4. Methodology**

For the purposes of this study, a survey questionnaire was developed, which included questions about student demographics, their learning achievements in STEM subjects in the previous semester, what subjects the students like, which activities in the classroom they consider to have an impact on their learning achievements, and which pedagogical work methods they recommend teachers use to improve learning achievements in STEM subjects. The questionnaire was developed based on results of the literature analyses on aspects that influence learning achievements in STEM. In the first step, the questionnaire was piloted with a small group of students (N 114). After the piloting phase, the results were discussed in the project group to agree on necessary corrections of the questionnaire. After the piloting phase, the necessary changes were performed and the questionnaire was distributed to the students.

The purpose of this project was to analyze girls' learning achievements in physics and mathematics, but the boundaries of the research were expanded in the survey, for example, by providing the survey questionnaire to both boys and girls, including questions about a

wide range of subjects, and sending it to all schools in Latvia. The data analysis takes into account not only the answers of girls with high academic achievements but also the answers of all girls and boys who participated in the survey. Regression calculations include not only mathematics and physics but also chemistry, computer science, programming, and biology. The data were obtained from 1847 students. Participation in the survey was voluntary and students could stop filling out the questionnaire at any time. Students from the 7th–12th grades were invited to participate in the study; there are two reasons for this. Firstly, schools start teaching such subjects as physics and biology from the 7th grade, prior to which they are included in the one subject of science. Secondly, 7th-grade students have reached the age of 13, which, according to the legislation in force in Latvia, is the age when a student can make an independent decision to answer the questions asked in a questionnaire if it does not require them to disclose sensitive information about the state of their health, family situation, etc. The data were analyzed to identify factors that have a statistically significant impact on academic achievement in STEM subjects by performing calculations both for the entire group of respondents and after dividing students, firstly, into groups with low and high academic achievements and, secondly, into groups of boys and girls irrespective of their academic achievements.

All ethical norms have been observed in the study and the anonymity of the respondents is guaranteed. The obtained data have been analyzed only in an aggregated form. The survey was administered through Google Sheets and the results were then transferred to SPSS for data calculation. The data calculation descriptive statistics and inferential statistics—regression analysis methods—were used to find out the extent to which the factors identified have an impact on learning achievement, which is the basis for further learning achievements.

#### **5. Results**

To be sure about the reliability of data, the Cronbach alpha was calculated for items used in the analyses and the results show that there is good internal consistency. It was 0.620 for the learning subjects that the students had to evaluate and 0.832 for the items that indicate different pedagogical strategies.

The questionnaire was filled out by 1847 students, of whom 1190 were girls and 657 were boys. Students could also choose the answer that they did not want to indicate their gender, but none of those who answered this question marked this option. The distribution of the number of respondents by their class group is even and there were 322 (17.4%) students from 7th grade, 426 (23.1%) from the 8th grade, 293 (15.9%) the 9th grade, 313 (16.9%) the 10th grade, 266 (14.4%) the 11th grade, and 227 (12.3%) from the 12th grade.

Students were asked to evaluate their attitudes toward different subjects on a 5-point Likert scale, where 1—I do not like it at all; 2—I do not like it much; 3—I am indifferent; 4—I like it; and 5—I like it a lot. The results of this evaluation show what subjects they prefer (see Figure 1) and these confirm that there are gender differences: girls mostly do not like physics and ICT and prefer arts, while boys mostly like sports and ICT.

The students were asked to evaluate the classroom environment by assessing what was offered to them that affected their desire to learn (see Table 1). The students had to rate their assessment using a 5-point Likert scale, where 1—it does not influence me at all; 2—it rarely influences me; 3—it sometimes influences me; 4—it influences me quite often; and 5—it influences me very often. It can be seen that girls in general evaluated all the proposed statements to be slightly more influential. Boys rated the opportunity to work in groups with their friends and the willingness of their classmates to learn higher than girls, but these differences are not statistically significant. The collected data show that the highest-rated factor was "the teacher helps me if I do not understand something" (M for girls = 4.61; M for boys = 4.32), which is not surprising since the teacher's role is to help students. The factor that has the least influence on the desire to learn is "if my classmates also want to learn a lot" (M for girls = 3.19; M for boys = 3.24).




In the subsequent stage, students with high learning achievements in STEM subjects (math, physics, chemistry, and ICT), as well as biology, were separated, and these students were then subdivided into two groups: those whose grades in these subjects are 7.5 (out of 10) and below (students with low academic achievements) and those whose grades are 7.51 and above (students with high academic achievements).

The regression analysis includes questions about what subjects the students like, offering a choice of 10 subjects, and which of the 14 offered activities that take place in the classroom (see Table 1), in their opinion, affect students' learning achievements in STEM subjects.

Initially, the results of the group of students whose learning achievements in STEM subjects are lower than 7.5 were analyzed. The obtained results show that there are four statistically significant models that have an impact on the predicted learning achievements of students in STEM subjects. The ANOVA calculations show that all four models are statistically significant, but Model 4 has the greatest impact on increasing student learning achievements. The overall regression was statistically significant and results for the fourth model are as follows—R2 = 0.71, F = 4.132, *p* ≤ 0.043.

As can be seen from regression analyses (see Table 2), the beta coefficient shows that the higher predictable outcome in STEM subjects is liking mathematics and the indicator "the teacher helps me if I do not understand something" in all models. In the fourth model, which has the most influential liking of mathematics, can increase learning achievements by 1.4, help of teachers by 2.12, and the possibility to succeed by 0.87. It can be concluded that students expect teachers to be kind, but it does not predict an increase in learning success. Students expect that teachers help them if they do not understand something, which is a strong predictor of learning success. This confirms that focused teacher support is needed more than just a kind personality, which does not support the learning of students whose learning outcomes are not high. In all of these models, liking mathematics emerges as a factor that predictably improves learning outcomes, implying that there is a strong emphasis on students being good at mathematics to predictably improve learning motivations across the STEM field.


**Table 2.** Coefficients\_Low achievers\_subjects\_all students.

Next, regression calculations were performed for this group of students with low academic achievement in STEM subjects about the methods they would recommend teachers use and their predicted effects on academic achievement in these subjects. Students had to evaluate nine methods (group work, experiments, reading, etc.) on a scale from "never" to "all lessons" and the obtained results show that a statistically significant model for a predictable increase in learning achievement is "if teachers use individual tasks" and "if teachers ask us to analyze different examples". The overall regression was statistically significant and results for the second model are as follows—R2 = 0.33, F = 7.22, *p* = < 0.007. The R-square calculations show that the combination of such methods would predictably increase learning achievements in STEM subjects by 3.3%.

The beta coefficient shows that using "individual tasks", the assessment will increase by 1.25 points, and when using "analysis of examples", the assessment will increase by 1.26 points for the group of students with low learning achievements in STEM subjects (see Table 3).


**Table 3.** Coefficients\_Low achievemers\_suggestions\_all students.

Dependent Variable: Avg\_5.

The next group analyzed was the students who have high learning achievements (over 7.5) in STEM subjects. In this group, two patterns emerge that predictably increase academic achievement, which are liking mathematics and chemistry. The ANOVA calculations show that both models are statistically significant and the R-square results show that for highachieving students in the defined subjects liking mathematics and chemistry predictably increases their academic achievement in all STEM subjects by 7.6%, which confirms that the most important thing for high-achieving students to increase their learning achievements is liking the subject. The overall regression was statistically significant and results for the second model are as follows—R2 = 0.79, F = 16.212, *p* = < 0.000. No other indicators are significant in this group of students. Beta coefficients show that liking mathematics in groups of high achievers predictably increases grades in STEM subjects by 1.4 points, while liking chemistry predictably increases grades by 0.94 points (see Table 4). For this group, the activities taking place in the classroom are less important and the main driving force is their liking the subject, which means that the greatest emphasis in terms of pedagogical support should be placed on the stage when students develop an attitude toward one or another subject.


**Table 4.** Coefficients\_High achievers\_subjects\_all students.

Dependent Variable: Avg\_5.

After performing regression calculations for the group of high-achieving students on the instructional methods they would recommend teachers use, the one that had a statistically significant effect on increasing academic achievement is reading; its predicted impact is 2.3%. The overall regression was statistically significant and the results for this model are as follows—R2 = 0.23, F = 13.586, *p* ≤ 0.000. No other indicators are important for high achieving students.

The beta coefficients show that reading can predictably influence learning achievements in STEM for 1.15 points (see Table 5). This in turn demonstrates the importance of having appropriate learning materials for students to read to deepen their understanding of the subject.

**Table 5.** Coefficients\_ High achievers\_suggestions\_all students.


Dependent Variable: Avg\_5.

Next, the data of students were separated by gender and the group of girls with low academic achievement in STEM subjects was analyzed first. Here, again, the ANOVA calculations were performed to ascertain whether the data were statistically significant. Ten questions were included in the regression calculations, in which the students had to evaluate to what extent they like specific subjects and to what extent the activities taking place in the classroom influence the students. They expressed their assessments on a 5-point scale, ranging from "it does not influence me at all" to "it influences me a lot". The obtained results show that four models are statistically significant. The overall regression was statistically significant and the results for the fourth model are as follows—R2 = 1.25, F = 7.516, *p* ≤ 0.006. However, interesting results emerge here, as in the strongest model, liking physics predictably affects academic achievement by 12.5%, but the beta coefficient shows that this liking for physics has a negative effect on predicted academic achievement in STEM subjects of 1.31 points for low achieving girls (see Table 6). This means that lowachieving girls dislike physics to a great extent, even to the point of affecting their academic achievement in all STEM subjects. According to the beta coefficients, a predictable increase in the results is for the indicators "the teacher helps me if I do not understand something" by 3.76 and liking mathematics by 1.48; these results are quite similar as for the general group of low achieving students where the help of teachers and liking of mathematics has a strong impact on predictable learning achievements. Similar to the case of the general group of students with low achievements, the negative effect is "the teacher is kind", where the predicted decrease in learning achievements in STEM subjects is 1.63 points, confirming that students understand that a teacher who helps them learn can increase their learning achievements, but they still believe that it is important for teachers to be kind.

**Table 6.** Coefficients\_ Low achievers\_subjects\_girls.


Dependent Variable: Avg\_5.

Next, the analysis turned to the questions where students had to recommend the frequency of the instructional methods used by the teachers on a 5-point Likert scale from "never" to "all lessons" and the obtained data show that in the group of girls with low learning achievements in STEM subjects, the model is statistically significant where "analysis of examples" and "individual tasks" are recommended. The overall regression was statistically significant and the results for the second model are as follows—R2 = 0.43, F = 5.287, *p* ≤ 0.022. The predicted increase in academic achievements is 4.3% and the beta coefficients show that a predictable increase in STEM learning achievements for low achieving girls is by 1.53 points if the pedagogical strategy "analysis of examples" is used and by 1.34 points if "individual tasks" are provided (see Table 7).

A regression analysis of the group of boys with low learning achievements in STEM subjects, where the analysis includes the subjects that students like and classroom activities that students evaluate as important, was performed next, and the obtained results show that the statistically significant predicted learning achievements in STEM subjects are influenced by a liking for mathematics and the option "I can succeed at what I do". The overall regression was statistically significant and results for the second model are as follows—R2 = 0.64, F = 5.374, *p* ≤ 0.021. Liking mathematics predictably increases learning achievement in STEM subjects by 4.5%, while "I can succeed at what I do" predictably increases it by 6.4%. The beta coefficients show that liking mathematics can predictably increase the results by 1.88 points and "I can succeed at what I do" by 1.44 points for low achieving boys (see Table 8) and these results are in line with a general group of low achieving students where the liking of mathematics and feeling that one can succeed were indicators with positive predictable influences on improved learning achievements.


**Table 7.** Coefficients\_ Low achievers\_suggestions\_girls.

Dependent Variable: Avg\_5.

**Table 8.** Coefficients. Low achievers\_subjects\_boys.


Individual tasks 0.134 0.130 0.022

Dependent Variable: Avg\_5.

When performing regression calculations for boys with low learning achievements in STEM subjects on the methods they would recommend teachers use, "individual tasks" appears as statistically significant. The overall regression was statistically significant and the results for this model are as follows—R2 = 0.14, F = 4.278, *p* ≤ 0.040. The data of adjusted R2 shows that the predicted impact of this indicator is of 1.4%. The beta coefficient shows that the predicted impact of this indicator is 1.38 points for improved learning outcomes (see Table 9). These results are quite similar for the general group of low achieving students and also for the group of low achieving girls and it means that low achieving students can succeed in STEM learning if they are provided with individual tasks.

**Table 9.** Coefficients\_ Low achievers\_suggestions\_boys.


Dependent Variable: Avg\_5.

Subsequently, data calculations were performed on girls with high educational achievements in STEM subjects. The regression analysis for this group includes subjects that students like and what happens in the classroom that students consider important. The obtained results show that five models to increase the predicted academic achievement in STEM subjects are statistically significant. The strongest predictive value is for the model is where high-achieving girls can work with classmates of their own gender. The overall regression was statistically significant and results for the fifth model are as follows—R2 = 1.46, F = 4.373, *p* ≤ 0.037. The adjusted R2 results shows that predicted academic achievement in STEM subjects can improve by 13.4% for the fifth model. The beta coefficient shows that a predictable increase in STEM learning achievements in the fifth model is in the amount of 1.29 if these girls like math, by 1,08 if they like chemistry, by 1.01 if they can succeed in what they do and by 0.47 if they can work in their own gender group. The predictable decrease

in learning achievements by 0.8 is if these girls work in different groups (see Table 10). In these results we can see the same pattern of results as it was in all groups that liking mathematics is a strong predictor of increased learning achievements; this has the same pattern as in the general group of students with high learning achievements that the liking of chemistry has a predictable influence, but indicators about working in particular groups are present only for girls with high achievements in STEM subjects.


**Table 10.** Coefficients\_ High achievers\_subjects\_girls.

Dependent Variable: Avg\_5.

When performing regression calculations on girls with high academic achievements in STEM subjects on the methods they would recommend teachers use, "individual tasks" appears as statistically significant, with a projected 1.9% increase in academic achievement in STEM subjects. The overall regression was statistically significant and results for this model are as follows—R2 = 0.21, F = 8.255, *p* ≤ 0.004. The beta coefficients also show that, for high achieving girls, the use of "individual tasks" predicts an increase in STEM learning achievements by 1.07 points (see Table 11). Such results are similar as for other groups where it was confirmed that individual tasks have a high predictable impact.


**Table 11.** Coefficients\_ High achievers\_suggestions\_girls.

Dependent Variable: Avg\_5.

A regression analysis was also performed for the group of boys with high academic achievements in STEM subjects, which included the subjects that the students like and what happens in the classroom that the students consider important. The obtained results show that only one model (liking chemistry) is statistically significant for increasing the predicted academic achievement in STEM subjects. The overall regression was statistically significant and the results for this model are as follows—R2 = 0.19, F = 4.409, *p* ≤ 0.037. The predicted increase in improved STEM learning achievements is 1.9%. The beta coefficient shows that the predicted increase in learning achievements for high achieving boys if they like chemistry is 0.99 points (see Table 12).

**Table 12.** Coefficients\_ High achievers\_subjects\_boys.


Dependent Variable: Avg\_5.

When performing regression calculations on high-achieving boys in STEM subjects on the methods they would recommend teachers use, "reading" emerges as statistically significant, with a projected increase in academic achievement in STEM subjects of 3.3%. The overall regression was statistically significant and the results for this model are as follows—R2 = 0.33, F = 6.710, *p* ≤ 0.010. The beta coefficient confirms that the predicted improvement in STEM learning achievements for high achieving boys is 1.49 points if students are reading (see Table 13).


**Table 13.** Coefficients\_ High achievers\_suggestions\_boys.

#### **6. Discussion**

In almost all the analyzed groups, it can be seen that a liking for mathematics plays a statistically significant role in predicting an increase in academic achievement in STEM subjects. The only exception is in the group of boys with high academic achievements, where a liking for chemistry is the most influential model. This confirms that mathematics is a basic subject, the liking of which teachers should encourage because it has a positive effect on high learning achievements in other STEM subjects as well, which echoes the conclusions of other researchers [31].

For the group of all students with low learning achievements and the group of girls with low learning achievements in the STEM subjects, it is essential that "the teacher helps me if I do not understand something". For low-achieving boys, this is not a significant factor. In the group of all students with low learning achievements and in the group of girls with low learning achievements, "the teacher is kind" is statistically significant, albeit with a negative impact on learning achievement, which means that it is important for students to explain what they do not understand and learn in the process. They consider the teacher's kindness of little value if the teacher's desire to help in the learning process is not present. This feature is not significant for boys in any of the groups or for students with high academic achievements in STEM subjects. "I can succeed at what I do" is statistically significant for predicted improvements in academic achievement in STEM subjects in the group of all students with low academic achievements and boys with low academic achievements, as well as girls with high academic achievements.

Liking chemistry is a significant predictor of high academic achievement in all analyzed groups with high academic achievement in STEM subjects. The factors "I can work in different groups" and "I can work in a group with classmates of my own gender" are statistically significant models in the group of girls with high academic achievements, but working in groups without a gender division has a negative effect. This is interesting because when asking girls about what methods they would recommend teachers use, only one appears with a statistically significant pattern (using individual tasks), and this may indicate that girls want to work individually and take individual responsibility for their learning achievements. This may be because they have already experienced the effects of microaggressions on their academic achievements.

The importance of individual tasks to high learning achievements must be emphasized and this should definitely be taken into account when planning and organizing the pedagogical process in the classroom, where great emphasis is placed on group work. These results indicate that students still want to be individually responsible for the work to be completed and to understand the task individually.

A liking for physics is statistically significant in a model that predicts academic achievement in STEM subjects, but this factor has a negative effect on low-achieving girls. This result is also very important when searching for the causes of such dislike. It is important to remember that in Latvia, physics is only taught as a separate subject from the 7th grade, which is also the lower age boundary for students involved in answering the survey. Therefore, we can conclude that those girls who have developed a dislike for physics most likely developed it before the 7th grade (13 years old). This indicates that it is important to think about how to teach physics to students at a younger age, promote students' understanding of it, as well as create an environment where girls feel safe and do not feel the effects of stereotypes and microaggressions. It should also be noted that a liking for physics does not have a significant effect on any of the other analyzed groups. Additionally, a liking for ICT or biology was not found to be statistically significant in any of the analyzed groups.

Of the students' recommendations to teachers about the methods that they use, "individual tasks" appeared as statistically significant in all the analyzed groups with low learning achievements and in the group of girls with high learning achievements, but it was not significant in the group of boys with high learning achievements. The "analysis of examples" tool is an essential recommendation for teachers, as it has a statistically significant role in predicting improvements in learning achievements in STEM subjects in the general group with low learning achievements and also in the group of girls with low learning achievements. On the other hand, there appears a statistically significant recommendation to use "reading" tasks more in the group of all students and in the group of boys with high learning achievements in STEM subjects. This means that boys with high academic achievements are aware of the need to read and delve into information in order to acquire new knowledge. This proves that it is essential to provide learning materials that students can read and discover new information about a subject for themselves. The results also confirm that a significant emphasis must be placed on mathematics in order to increase both students' interest in STEM subjects and learning achievements in this field, which will predictably increase the number of people who will choose to link their careers to STEM; research shows that increased requirements to learning achievements also have a positive effect on girls' involvement in the STEM field [32].

The results of the survey show that students wish to understand STEM subjects and they would like teachers to help resonate with the motivational development idea of the important role of self-efficacy [33,34] because it confirms that it is important for students with low learning achievements to feel they are succeeding, but this is not so important when students are already aware of the impact of their learning activities on learning achievements. The fact that this factor is significant for the group of high-achieving girls echoes the findings expressed by other researchers [35,36] regarding girls feeling more insecure about their academic achievements, so it is important for them to feel they are succeeding. The reason for this is probably related to the fact that they do not want to be exposed to various stereotypical comments or expressions of microaggressions from boys, which echoes the findings of other researchers that girls who perform well in the STEM field also feel the influence of stereotypes [10] and are able to perform better when working in a group of their own gender [23]. However, one should not assume the hasty conclusion here that these results indicate that it is necessary to divide students into gender-appropriate groups because such a division contributes to the formation of various stereotypes. It would be better to create environments where girls can act as equals in the learning process and not feel the influence of stereotypes or microaggressions.

The results of the survey confirmed that students do not wish that requirements to learning outcomes should be lowered. They are eager to learn more if there is pedagogical support, however, increased requirements for students' learning achievements must also be in synergy with the support provided by teachers if something is not understood, with the reduction in stereotypes and expressions of microaggression, and with the opportunity to perform tasks individually.

In the survey, the students could answer which profession they associate their future with and the data show that not only do a very small number of girls associate their future careers with the STEM field, but boys only rarely associate their career choice with STEM as well. This is an extremely worrying fact and it is necessary to start thinking immediately about how to change this ratio. It is undeniable that such changes cannot happen in a short period of time, but it is also unequivocally clear that steps must be taken in the Latvian education system in order to change the situation. These should be rapidly growing actions to ensure a wide range of educational activities that contribute to the growth of knowledge in the STEM field.

#### **7. Conclusions**

Mathematics is a basic subject, the liking of which strongly predicts high learning achievements in STEM for all students; teachers should encourage and aim to ensure this. For high achieving boys, liking chemistry has a significant positive predictor. For low achieving girls, a liking for physics statistically and significantly predicts lower academic achievement in STEM subjects and it means that particular pedagogical strategies (individual pedagogical support, individual work, and feelings of success) should be used to improve girls' attitude to physics. The liking of other learning subjects included in the model namely, Biology and ICT—do not have a predictable impact on learning achievements.

A strong predictable influence on STEM learning achievements has teachers' support when students do not understand something in low achieving students' groups as well as "individual work", which means that low achieving students cannot be let aside and they need individual pedagogical support. Such strategies as different kinds of group work do not support higher learning achievements.

Teachers' kindness plays an important role in learning but has a negative predictable impact on learning achievements. This result breaks a stereotype that if teachers are kind to students they will be eager to learn.

Students need to experience success and it was proved by the results that show that a high predictable impact on low achieving students has the indicator "I can succeed in what I do".

In a group of low achieving students, the indicator "analysis of examples" has a significant predictable impact on higher learning achievements.

Only in a group of high achieving girls does the indicator "to work in the same gender group" show a positive predictable influence on higher STEM learning achievements.

#### **8. Future Research Directions**

In the next steps of the research, the researchers will analyze the impact of stereotypes on students' learning achievements as well as the impact of teachers' actions on the formation of stereotypes.

**Author Contributions:** Data curation, S.K.; Formal analysis, L.D.; Funding acquisition, G.K.; Investigation, L.B., A.V. and I.K.; Methodology, L.D.; Visualization, S.K.; Writing—original draft, L.D. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research "Support for the implementation of national and international events for the development of educational talents", was funded by European Social Fund grant number 8.3.1.1/16/I/002.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** All anonymized data are available here: https://drive.google.com/ drive/folders/1nu-rQBe-WyWvyHLHo\_BRwabS8AqcZRKG.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**Abdellatif Sellami 1,\*, Mohammad Ammar <sup>2</sup> and Zubair Ahmad 2,\***


**Abstract:** Understanding teachers' attitudes and perceptions of STEM teaching is a key pathway to enhance effective STEM teaching. Inarguably, teachers are the cornerstone of educational quality and play a central role in students' academic performance. Specifically, the pedagogical strategies teachers employ and their effective use in the classroom are strong determinants of students' enrollment or retention in STEM fields of study and eventual careers. This study sought to explore the experiences of high school STEM teachers in Qatar, focusing on the pedagogical approaches they utilize and the challenges they encounter, with the aim of delving into how these approaches and barriers affect the teaching of STEM in the country's high schools. The study's design is observational, with data collected using a survey of 299 secondary high school STEM teachers (11th and 12th grades). To attain the goal of this study, we examined the barriers perceived to impede engagement in effective STEM teaching from high school teachers' perspective. The study's findings pointed to the influence of student- and school-related factors in shaping STEM teaching. Significant differences were detected based on teachers' gender, grade level of teaching, age group, and university education. Logistic regressions revealed that teachers' demographic attributes, including age group and university education, affect their likelihood to use STEM pedagogies in class. This likelihood was significantly affected by student-related barriers and the learning resources/materials employed in classrooms. These findings postulate critical evidence in directing the development of successful STEM learning practices within Qatar's high schools.

**Keywords:** STEM education; high school; teacher education; STEM pedagogies; barriers

#### **1. Introduction**

In the face of the many global challenges the world is facing and the risks they pose to the future well-being of humanity, science, engineering, and technology are key to understanding and solving the pressing problems. Through advances in science, engineering, and technology, human beings can now find solutions to many of the urgent ills facing humanity, including climate change, health-related problems such as COVID-19, food shortages, overpopulation, resource management, and various other ailments. To deal with the complexities of modern society, which are mainly due to human activity, a new set of core skills and knowledge is needed. Herein lies the importance of science and technology as catalysts of prosperity and sustainable development for the present and future generations.

In the context of Qatar, in recent years, leadership has placed the importance of transforming the country from a hydrocarbon-based economy to a knowledge-based society high on its national agenda. At the heart of this plan is the demand for federal capacity building. Against this background, the need for professionals in science, technology, engineering, and mathematics (STEM) fields in Qatar is considered to be in crisis by various education, government, and industry circles [1,2]. While the demand for STEM professionals in Qatar is very high, the number of citizens with the education and training

**Citation:** Sellami, A.; Ammar, M.; Ahmad, Z. Exploring Teachers' Perceptions of the Barriers to Teaching STEM in High Schools in Qatar. *Sustainability* **2022**, *14*, 15192. https://doi.org/10.3390/ su142215192

Academic Editor: Pedro Guilherme Rocha dos Reis

Received: 27 October 2022 Accepted: 11 November 2022 Published: 16 November 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

required for sustaining the industries vital to its economy remains alarmingly low. The mismatch between education and the job market needs in Qatar has resulted in a very high proportion of unskilled and semi-skilled citizens presently employed in the public sector [3]. Consequently, the private sector has had to rely on foreigners to fill the gap in STEM professions. With a significant deficit in the number of young people studying and contemplating a career in STEM, Qatar will continue to rely heavily on expatriate labor.

Compounding the problems associated with high levels of foreign labor in Qatar is that most young and highly educated Qatari citizens hold credentials in non-STEM fields. The private sector, dominated by industries, offers only a few positions suitable for young Qataris who attain university education in a non-STEM area [4]. Moreover, there is strong evidence that many Qataris, especially males, do not intend or desire to pursue tertiary education [5], a trend with severe ramifications for attempts to create a sustainable local STEM human capital in the country [6]. Indeed, there is a lack of documented research investigating how these problems linked with the shortage of skilled professionals in Qatar and the broader Gulf Cooperation Council (GCC) region can be addressed effectively.

While substantial gains have been made in terms of equitable access to formal education and enrolment and literacy rates in Qatar [6,7], many are critical of the inability of Qatar's education system to produce highly skilled graduates that can contribute to the nation's development, prosperity, and well-being [8,9]. Despite decades of steady gains, Qatari women's participation in the labor force is still meager. Declining female participation continues to affect growth and development in Qatar. Exacerbating the job market demographic imbalance is the significant dependency on highly skilled professionals from foreign countries, as was stated previously. To improve the capacity of its skilled workforce in the labor market, concerted efforts are required to increase the number of men and women enrolled in disciplines associated with the knowledge economy on a par with developing nations.

STEM education is essential to the economic development of Qatar. While the country's national development strategy highlights the importance of STEM education for progress and development, the practical application of STEM education continues to face many challenges, especially in developing countries, such as the GCC states. Therefore, this study aims to investigate teachers' perceptions regarding salient barriers to STEM education in high schools in Qatar. The originality of our research lies in offering insights into such barriers from an Arab Middle Eastern perspective.

This paper is structured as follows. Section 2 reviews the relevant literature on STEM, synthesizing and critically evaluating research dealing with critical challenges to STEM education. Section 4 describes in detail the research design and the methods employed in this current study, including the data collection and the type of analysis used. Section 5 provides a detailed description of the study's results, focusing on the different factors that shape teachers' perceptions of STEM education. Finally, Section 6 provides a discussion of these results.

#### **2. Review of Literature**

With the increasing demand for professionals who possess the skills and knowledge that are key to economic growth and development, the onus rests with educational institutions to prepare students equipped with critical STEM skill sets. To enhance students' STEM-related capabilities, schools in particular need to improve their STEM education offerings and redesign their instructional pedagogies [10]. Not surprisingly, the urgency of STEM for national progress, security, and well-being triggered the launch of a plethora of educational reforms that many countries worldwide embraced to revamp STEM education for the economy.

Hsu and Fang [11] identified two distinct approaches adopted in STEM education. One is both interdisciplinary and transdisciplinary and treats the contents of the different STEM disciplines as integrated and interrelated components. The other employs a multidisciplinary instructional approach that views STEM discipline contents as a cluster

or constellation of individual STEM fields of study. In previous research, Gomez and Albrecht [12] suggested using an interdisciplinary approach that anchors STEM instruction and education in pedagogy to prepare students for STEM-related career pathways.

As key catalysts in the education process, teachers can have a critical role in teaching STEM, affect students' educational achievement in STEM subjects and ultimately influence their interest in STEM fields of study and careers [13]. Students learning and practical experiences are determinant factors that enhance their STEM skills and knowledge. Indeed, alongside these experiences, teachers and quality STEM programs create ideal opportunities for developing students' talents and abilities in STEM domains [14].

The extant literature refers to the interplay between a host of individual (personal), environmental (contextual), and behavioral factors that act as either enablers or barriers to STEM teaching. For example, Nugent and colleagues [15] suggested various social (contextual), motivational (interest and self-efficacy), and instructional (teachers and teaching) factors that create adequate conditions for effective STEM teaching. Other research conducted by Margot and Kettler's [10] systematic review of research exploring the teachers' perception regarding STEM education noted six key barriers that thwart STEM teaching. These challenges are associated with the curriculum, pedagogy, assessment, teacher support, students, and structural systems.

Current debates on STEM education point to hindrances that impede the implementation of effective interdisciplinary modes of teaching STEM. Examples reported in the literature include teachers' beliefs, knowledge, and understanding of STEM [16,17]. Other examples include poor teacher preparation, lack of professional development for teachers, shortage of teachers, poor cross-disciplinary content integration, low student motivation, inadequate facilities, and inappropriate assessments [11,18]. Work by Wahono and Chang [19] indicated three main barriers facing STEM teachers: insufficient knowledge, difficulty applying STEM to some topic areas, and difficulty linking the different STEM topics.

For the purpose of this study, two main theoretical models provided a framework for our research: Bandura's social cognitive theory (1986) and Attribution Theory [20,21]. First, the social cognitive theory (SCT) is used as a theoretical lens that lends a rationale for considering individual and environmental (contextual or school-related) factors. This theoretical lens proved helpful in examining individual characteristics, including selfefficacy, a concept central to SCT [22]. Past research revealed the importance of selfconfidence in classroom instruction and the teaching of science subjects [23,24]. Second, the attribution theory (AT), a well-known research paradigm in social psychology, helps to understand why a particular behavior or event occurs and attributes the specific causes to the occurrence. In other words, the AT serves to make sense of the social world and explain how individuals perceive the causes of daily life experiences. Therefore, based on the literature, this study hypothesized that high school STEM teachers face challenges in Qatar that affect their teaching process.

#### **3. Research Questions**

This study aims to address the following research questions:


#### **4. Methods**

Our study's design is observational, with data obtained using survey questionnaires to explore the experiences of high school STEM teachers in Qatar regarding the pedagogical approaches they use and the challenges they encounter. In so doing, the aim was to dig into the way these approaches and challenges affect the teaching of STEM subjects in Qatar's high schools. A cross-sectional survey was created based on two components: STEM teaching approaches and barriers to effective STEM teaching. To collect the data required for this research, a survey was administered physically and virtually over two months during the 2021 Spring Semester (March–April 2021). The survey was first administered using paper questionnaires (paper-and-pencil interviewing–PAPI). However, the response rate was low, and the researchers decided to also gather data using computer-assisted personal interviewing (CAPI).

#### *4.1. Participants*

The study was carried out in thirty-nine high schools across Qatar. These schools were randomly selected from local government schools (56.4%) and private schools (43.6%) in Qatar. Upon receiving approval from Qatar University's research ethics board (IRB), school board superintendents and teachers were contacted to allow the researchers to collect teacher data in their schools. With the exclusion of teachers who did not complete the entire survey, a total of 299 teachers participated in the study.

Table 1 illustrates teachers' demographic distribution, demonstrating their distribution by gender (54.5% males and 45.5% females) and age group, ranging from 31 to 40 (40.1%). More than half of the participants held a bachelor's degree (59.5%) and many more reported graduating from an Arab university outside Qatar (64.9%). Almost all were expatriates (96%). Although the bulk of teachers taught both grades 11 and 12 (45.8%), 25.8% taught grade 11, and 24.7% taught grade 12 exclusively. Science teachers made up the majority of respondents (45.8%), followed by mathematics teachers (30.1%), followed by engineering and technology teachers (8.7%). The remaining 15.38% taught multiple subjects (at least one STEM subject). Most teachers reported teaching between 11 and 20 h per week (65.6%).


**Table 1.** Teacher demographics (N = 299).


#### **Table 1.** *Cont.*

#### *4.2. Survey Instrument*

The execution process consisted of three phases: (1) survey formulation, (2) survey piloting, and (3) survey execution.

Step 1: To develop the survey, we examined existing research on (a) STEM teaching [25–31]); (b) the role of teachers in STEM (e.g., [10,32–36]); (c) successful pedagogical approaches in STEM education (e.g., [37–40]; and (d) barriers to STEM teaching (e.g., [18,41–43]. Reviewing this literature allowed us to grasp the survey's target areas better and helped us understand general perceptions of STEM teaching as perceived by teachers and students, thus enabling us to develop items addressing the barriers and challenges teachers face when teaching STEM. A five-point Likert scale was used to grade 110 closed objects within five constructs as follows: Student-related barriers faced in teaching STEM, School-related barriers faced in teaching STEM, STEM-related pedagogical approaches, STEM-related teaching activities, and Factors affecting the decline of student interest in STEM. For each survey construct, teachers were given different response options depending on the type of question. These types included disagree-agree questions (strongly disagree = 1; disagree = 2; slightly disagree = 3; slightly agree = 4; agree = 5; and strongly agree = 6), frequency questions (never = 1; rarely = 2; sometimes = 3; often = 4; always = 5), percentage questions, rating questions (very poor = 1; poor = 2; fair = 3; good = 4; very good = 5), emphasis questions (none= 1; minimal = 2; moderate = 3; considerable = 4; heavy = 5), and importance questions (not important at all = 1; not important = 2; undecided = 3; important = 4; very important = 5).

Step 2: This step included testing the developed survey with two focus groups, one in Arabic and the other in English, to fine-tune the instrument. The focus group discussions aided us in addressing concerns we had regarding the wording of questions. This helped in rewriting and clarifying inadequately worded questions. The survey's primary goals were to collect (a) basic background knowledge, (b) systematic evidence of teaching approaches, and (c) structured evidence of the main challenges to effective STEM teaching.

Step 3: The questionnaires were distributed after receiving all signed consent papers from teachers and school authorities. Teachers were instructed to respond to the survey in English or Arabic. The average time it took for the participants to complete the study was between 13 and 17 min. Factor analysis was used to form constructs which measured important factors that would help answer the RQs of this study. This was performed using a principal component analysis and varimax rotation with a minimum factor loading criteria of 0.50. To guarantee adequate levels of explanation, the communality of the scale, which depicts the degree of variation in each component was evaluated. The findings indicate that

all communalities were more than 0.50. The significance of the data, (χ<sup>2</sup> (820) = 6096.87, *p* < 0.010), indicated that factor analysis was appropriate. The Kaiser–Mayer–Olkin (KMO) and Bartlett's test of sphericity were used to confirm the sampling adequacy. The data was found to be suitable for factor analysis according to the KMO value which was 0.885. Results of the factor analysis are shown in Table 2.

**Table 2.** Factor loadings for the items in each construct.


Further, Cronbach alpha (α) was used to assess the internal consistency of the reliability. The computed values of α for each survey construct are given in Table 3. According to researchers [44], alpha levels above 0.70 are regarded as reliable, whereas values greater

than 0.90 are considered extremely reliable. The estimated alphas in this study revealed a reliable and very highly reliable scale.

**Table 3.** Cronbach's Alpha values for constructs in the teacher questionnaire (with examples of survey items).


#### *4.3. Data Analysis*

#### 4.3.1. Measures

The survey constructs were formulated as quantitative measures to represent important factors that would help answer the RQs of this study. These measures included studentrelated teaching barriers, school-related teaching barriers, teacher pedagogy, teacher activity, and a decline in student interests. The reason for the selection of these measures was because previous analyses revealed that though instructors favor STEM teaching, many instructional impediments hinder effective STEM teaching, including the curriculum, structural problems, concerns with students and evaluations, and a lack of teacher support [10,45]. Moreover, there is evidence that suggests that high-quality teachers significantly impact students' perceptions of STEM and, in many circumstances, student achievement [46]. Therefore, we consolidated our survey items into five measures to singularly represent the items they contain. For this, Likert-scaled survey items under each construct were coded into numbers, and then summed to obtain an overall score for the respective construct. Since Likert-type data are ordinal and only account for one score being higher than the other, and not the distance between the points, some of the measures required to be coded into dichotomous variables to represent the data as nominal categories (as explained below for each measure). These measures have already been validated in Section 4.2. Below are the details of the formulation of these measures.

#### Student-Related Teaching Barrier Score

The first measure used in this analysis is a student-related teaching barrier (STB) score. Teachers were asked to define the extent to which their teaching was affected due to various student-related issues. These issues comprised of the following: lack of required skills, lack of necessary knowledge, not having enough sleep, disruption in the classroom, and lack of interest. Teachers' answers on their perceptions of students were encrypted to dichotomous variables by allocating a score of "1" to responses that corresponded to "often" and "always" and a value of "0" to those with "never", "rarely" or "undecided". These five statements were then tallied to get a single STB score ranging from 0 to 5. This score indicated the collective magnitude of challenges teachers faced in their STEM teaching due to student-related issues.

School-Related Teaching Barrier Score

The second measure used in this analysis is a school-related teaching barrier (SCTB) score. Teachers were asked to define the extent to which their teaching was affected due to various school-related issues. These issues comprised of the following: technical support, STEM training and pedagogical support, curriculum and teaching hours, instructional materials and supplies, classroom adequacy, outdated school computers, school space organization, administrative and budget constraints, school environment, and support and interest from fellow teachers. Teachers' responses to these questions were encoded into dichotomous variables by giving "1" to responses that matched with "often" and "always" and a "0" to those which matched with "never," "rarely," or "undecided". These five statements were then added to get a single SCTB score ranging from 0 to 18. This score reflected the cumulative extent to which teachers faced challenges in their STEM teaching due to school-related issues.

#### Teacher Pedagogical Score

The third measure used in this analysis is a teacher pedagogical (TP) score. Teachers were asked to define the extent to which they used various pedagogical approaches on a scale ranging from (1) 0–20% to (5) 81–100%. These approaches comprised of the following: project/problem-based approaches, collaborative learning, peer teaching, flipped classroom, personalized teaching, integrated learning, and differentiated instruction. Teachers' responses were then encoded into numerical variables by allocating values from 1 to 5 to the range of percentages. Therefore, 0–20% was coded as 1, 21–40% as 2, 41–60% as 3, 61–80% as 4, and 81–100% as 5. Additionally, the scores of all the different pedagogies were summed to obtain a single TP score ranging from 1 to 35. This score reflected the extent to which teachers applied pedagogical approaches in STEM. The TP score was then used to formulate the likelihood of teachers using pedagogical approaches in STEM teaching. This was done by translating the TP score to dichotomies for use in the logistic regression model on the basis that the extent of using pedagogical approaches by teachers in classrooms should at least correspond to 50%. Therefore, an average score of 2.5 for the seven items pertaining to TP score could be considered as teachers having a high likelihood to use pedagogical approaches in STEM teaching. Hence, teachers with high TP scores greater than 18 out of 35 were coded as "1" and teachers with low TP scores below 23 were recorded as "0".

#### Teacher Activity Score

The fourth measure used in this analysis is a teacher activity (TA) score. Teachers were asked to define the extent to which they implemented activities beneficial for STEM teaching. This included their use of different type of materials (audio, visual, written), engaging students in group discussions, making students see connections between different disciplines, helping them consider alternative explanations, and encouraging students to provide explanations. Teachers' responses to these activities were then coded into numerical equivalents by giving a score of "1" to responses that matched with "often" and "always" and "0" to those which matched with "never," "rarely," or "undecided". Furthermore, the scores were summed to obtain a single TA score ranging from 1 to 6. This score showed the overall degree to which teachers used activities beneficial for STEM learning.

#### Decline in Student Interest Score

The fifth measure used in this analysis is a decline in student interest (DSI) score. Teachers were asked to define the extent various student-related factors contributed to the decline of students' STEM interests in the class. These factors comprised of the following: lack of confidence, negative perceptions of STEM-related careers, lack of parental and family involvement, facing difficulty in homework, and lack of use (or misuse of) technology. Teachers' responses to these various activities were then coded into numerical equivalents by allocating a score of "1" to responses that corresponded to "often" and "always" and

a value of "0" to those with "never", "rarely" or "undecided". Further, the scores of all the different activities were summed to obtain a single DSI score ranging from 1 to 5. This score reflected how various student-related factors contributed to declining students' STEM interests.

#### 4.3.2. Statistical Analysis

SPSS (Version 29) was used to analyze all the data. Descriptive statistics were used to show the distribution of the demographics of the teachers. Graphical scales were developed to represent the distribution of the measures formulated in Section 4.3.1. These measures were analyzed using means and percentages to answer the RQ1. Further, bivariate logistic regression models were built to analyze the RQ2 which included interval and ratio-scaled variables. Using this, the relative effect of various factors on the likelihood of teachers employing STEM pedagogies was investigated. These factors included teachers' schoolrelated barriers, teaching activities, age group, university education, and teachers' use of resources and materials. Furthermore, teachers' use of various resources and materials on their likelihood to employ pedagogical practices in STEM teaching was also regressed. Lastly, various non-parametric tests were chosen to answer the RQ3 depending on the statistical measurement and distributions [47,48]. Non-parametric analyses were used to compare differences between teacher's demographical groups. In the case of two groups (gender, grade level of teaching) the Mann–Whitney U test was used to evaluate significant differences. The Kruskal–Wallis H test was used to explore significant differences between three or more groups (age group, geographic location of graduation university of teachers). If the Kruskal–Wallis test yielded statistically significant findings, Dunn's test was used to compare each independent group pairwise and check whether groups are statistically significant at some threshold. In addition, since the given value of significance may be appropriate for individual comparisons and not for the set of all comparisons, the Bonferroni correction was employed when performing the Kruskal–Wallis test.

#### **5. Results**

#### *5.1. Teachers' Perceptions of STEM Teaching*

The perceptions of teachers were evaluated using specified measures, as stated in Section 4.3.1. The results of these measures are discussed below.

#### 5.1.1. Student-Related Teaching Barrier Score

Teachers were asked to rate the extent to which they faced student-related barriers. This was done by asking teachers if their teaching was affected by various student-related issues. These responses were summarized to achieve an overall STB score. The STB score ranged from a scale of 0 to 5, with 5 denoting a high degree of teaching barrier due to student-related factors. Figure 1 shows the distribution of the STB score.

**Figure 1.** Student-related Teaching Barrier (STB) Scale.

The mean STB score was 1.93 (SD 1.72), indicating that there was no serious concern in the combined student-related barriers faced by teachers on an overall level. However, for further investigation into each student-related barrier individually, the teachers' responses were coded into two groups, having an extreme or high effect or low or no effect. The results are portrayed in Table 4. It was observed that almost half of the teachers were highly affected by students lacking the required skills (48.95%) and students not having enough sleep (48.09%). Moreover, 46% of the teachers reported that students lacked the necessary knowledge, which affected their teaching. Students' lack of interest and disruption in the classroom were reported to have an extreme or high effect on instruction by a lesser proportion of teachers, 36.11% and 22%, respectively. Therefore, these results indicate that, though teachers generally do not face student-related barriers on a macro scale, students' lack of skills, knowledge, and sleep significantly impacts STEM teaching in Qatar.

**Student-Related Barrier To What Extent Is Your Teaching Affected by the Following? (N = 299) Extreme or High Effect (%) Low or No Effect (%)** Students lacking the required skills 49.00 51.00 Students did not have enough sleep 48.10 51.90 Students lacking the required knowledge 46.00 54.00 Students' lack of interest 36.10 63.90 Students' disruption in the classroom 22.00 78.00

**Table 4.** Student-related barriers which affect STEM teaching in Qatar.

#### 5.1.2. School-Related Teaching Barrier Score

Teachers were asked to rate how much they struggled with school-related issues. This was accomplished by asking teachers if school-related specific problems had an impact on their teaching. To get an overall SCTB score, the replies were added together. The SCTB score varied from 0 to 18, with a score of 18 indicating a significant teaching barrier due to student-related variables. Figure 2 shows the distribution of the SCTB score.

The mean SCTB score was 3.09 (SD 4.41), indicating that overall, student-related barriers faced by teachers were low. However, for further research into each student-related barrier separately, teachers' responses were divided into two groups with an extreme or high effect or low or no effect, as shown in Table 5.


**Table 5.** School-related barriers which affect STEM teaching in Qatar.

#### 5.1.3. Teacher Pedagogical Score

Teachers were asked to rate the extent to which they employed different pedagogical practices in their STEM teaching. Teacher's responses were combined to produce an overall TP score. The TP score ranged from 0 to 35, with the latter denoting a high use of STEM pedagogical approaches. Figure 3 shows the distribution of the TP score.

**Figure 3.** Teacher Pedagogical Score (TP) Scale.

The mean TP score was 21.45 (SD 6.88), indicating that overall, teachers employed pedagogical approaches to a reasonable extent. However, for the examination of each pedagogical approach individually, teachers' responses were divided into two groups having a high extent or low extent, as shown in Table 6.


**Table 6.** Pedagogical Approaches used in STEM teaching in Qatar.

#### 5.1.4. Teacher Activity Score

Teachers were asked to rate how often they used various teaching activities in their STEM instruction. Teacher's responses to these items were added together to generate a total TA score. The TA score varied from 0 to 6, with a score of 6 indicating extensive usage of activities in the classroom. Figure 4 shows the distribution of the TA score.

The mean TA score was 4.53 (SD 1.68), implying that on the overall level teacher's use of activities in their teaching was considerably high. For further research into each teaching activity, teachers' responses were divided into two groups having a high or low extent, as shown in Table 7.

**Table 7.** Teaching Activities used in STEM teaching in Qatar.


5.1.5. Decline in Student Interest Score

Teachers were asked to rate how various student-related factors contributed to the decline of students' STEM interests in class. The responses were totaled to arrive at a total

DSI score. The DSI score ranged from 0 to 5, with a 5 signifying a high decline in student interests. Figure 5 shows the distribution of the DSI score.

The mean DSI score was determined to be 1.19 (SD 1.46), implying that the overall decline in students' interests due to student-related factors was not very concerning. However, for further investigation into each factor, teachers' responses were coded into two groups: either extreme or high effect or low or no effect. The results are portrayed in Table 8. It was observed that more than one-third of the teachers described a lack of parental and family involvement as a major factor in students' decline in STEM interests.

**Table 8.** Teachers' perception of the extent to which student-related barriers cause the decline in students' interest.


#### *5.2. Factors Likely to Influence Teachers' Use of STEM Pedagogical Approaches*

Regression analyses were performed to assess the parameters that affected teachers' perception of STEM teaching. Several bivariate regression models were built to predict teachers' likelihood to employ pedagogical approaches in their STEM teaching in connection with other measures and demographic factors.

5.2.1. Teacher's Student-Related Barriers, Teaching Activity, Age Group, and University of Graduation on Their Likelihood to Use STEM Pedagogical Approaches

A bivariate logistic regression model was built to ascertain whether factors are associated with the likelihood of teachers employing STEM pedagogical practices in their classrooms. These factors include STB score, TA score, nationality (Qatari or non-Qatari), age group, and graduated university. For this, the dependent variable was chosen to be the TP score, which was coded into dichotomies (TP score greater than 23 as "1" and TP score less than 23 as "0") to fit the regression model. The proposed regression model pointed to the chances of employing more pedagogical practices in STEM teaching (ODDS) = ƒ(STB

score, TA score, nationality, age group, and university level). Upon examination of comparing a complete regression model to an intercept-only model, the analysis was statistically significant (χ<sup>2</sup> (9) = 33.434, *p* < 0.001). The regression explained 15.5% of the variation of teachers who were likely to employ STEM pedagogies and correctly predicted 74.8% of all the cases. The regression also uncovered that teachers with high STB scores were marginally less likely to use STEM pedagogies than teachers with low STB scores (probability = 0.54).

Moreover, teachers with high TA scores were 1.3 times more likely to use pedagogical approaches in STEM teaching than those with low TA scores (probability = 0.58). Additionally, the age group of teachers was a statistically significant predictor of the likelihood of using pedagogical approaches in STEM teaching. Teachers younger than 50 were 2.182 times (on average) more likely to use pedagogical practices in their STEM teaching compared to those above 50 years of age (probability 0.68). Another interesting finding that the regression revealed was that the region of the university that teachers graduated from significantly affected their likelihood of employing pedagogical approaches in STEM teaching. Teachers from an American or European university were 6.07 times more likely to use pedagogical approaches in STEM teaching compared to teachers from Asian or African universities (probability = 0.86). Corroborating this, teachers from Arab universities were 1.896 times (on average) more likely to use pedagogical approaches in STEM teaching compared to teachers from Asian/African universities (probability = 0.65). Lastly, nationality was not statistically significant predictor of the likelihood of using pedagogical approaches in STEM teaching.

In summary, the results of this logistic regression showed that student-related teaching barriers faced by STEM teachers in Qatari schools significantly decrease their likelihood of using STEM pedagogies. Meanwhile, using STEM teaching activities in classrooms is a substantial factor in increasing the possibility of employing STEM teaching approaches. Further, their age group and university education were the main predictors of teachers' likelihood to use STEM pedagogies. While teachers under 50 were more likely to use STEM pedagogies, teachers from American or European universities were highly likely (probability almost 1) to use STEM pedagogical approaches. It should be noted that while the age group was found to be a significant predictor, teaching experience was not. Further, while university location significantly affected the likelihood of using STEM instructional approaches, nationality (Qatari/Non-Qatari) did not. The results are summarized in Table 9.


**Table 9.** Bivariate logistic regression of the relationship between STB score, TA score, nationality, age group, and university level on teacher's likelihood to employ pedagogical approaches in STEM teaching.

5.2.2. Teacher's Use of Resources and Materials on Their Likelihood to Employ Pedagogical Practices in STEM Teaching

A second bivariate logistic regression model was built to determine whether or not teachers' use of teaching resources correlated with their likelihood of employing STEM pedagogical practices in classrooms. Eleven teaching resources were explored in the regression. The hypothesized regression model was the likelihood of using more pedagogical practices in STEM teaching (ODDS) = ƒ(paper-based materials, audio or video materials, presentations, robots, calculators, graphing calculators, computer-based simulations, STEM-specific software, data sets or spreadsheets, word processors, and online tools). A test of the full regression model compared to an intercept-only model was statistically significant (χ<sup>2</sup> (11) = 24.671, *p* = 0.010). The regression explained 11.3% of the variation of those likely to employ STEM pedagogies and correctly predicted 70.8% of all the cases. The analysis revealed that teachers who used online tools as a resource for their teaching were 2.4 times more likely to employ STEM pedagogies (probability = 0.71). Moreover, teachers who used conventional calculators (not graphing) when teaching their courses were statistically less likely to employ STEM pedagogies than the teachers who did not use traditional calculators (probability = 0.35). The remaining resources and materials were not statistically significant predictors of the likelihood of employing pedagogical approaches for STEM teaching.

These results reveal that online tools significantly affect the likelihood of teachers to use STEM pedagogical approaches in their teaching. Another interesting finding was that using traditional resources such as calculators decreased the possibility of teachers employing pedagogical techniques in their STEM teaching. This shows that online tools are more adaptable for teachers when using STEM pedagogical approaches. Further, from the other resources that were not statistically significant in the regression, one essential resource was the use of STEM-specific software. This is worrying as using STEM-specific software ideally should positively affect teachers' likelihood of using STEM pedagogies. The results are summarized in Table 10.


**Table 10.** Bivariate logistic regression of the relationship teaching resources and materials on teacher's likelihood to employ pedagogical approaches in STEM teaching.

A third bivariate logistic regression model was developed to determine whether teachers' use of learning resources for teaching challenging concepts correlated with their likelihood of having high teaching activity in classrooms. Four teaching resources were explored in the regression. The hypothesized regression model was the likelihood of using more pedagogical practices in STEM teaching (ODDS) = ƒ(colleagues, educational and research journals, online resources, secondary textbooks). A test of the full regression model compared to an intercept-only model was statistically significant (χ<sup>2</sup> (4) = 17.477, *p* = 0.002). The regression explained 8.6% of the variation of teachers who were likely to employ STEM pedagogies and correctly predicted 77.5% of all the cases. The analysis revealed that teachers who used educational and research journals for teaching challenging concepts were 2.4 times more likely have a high teaching activity (probability = 0.71). Moreover, the use of online resources to teach challenging concepts showed a 2.1 times higher likelihood of having high teaching activity (probability = 0.68). The remaining resources and materials

were not statistically significant predictors of teachers' likelihood of using pedagogical approaches for STEM teaching. These results indicate that using educational and research journals and online resources for teaching challenging concepts dramatically affects the likelihood of teachers to have a high teaching activity. The results are summarized in Table 11.

**Table 11.** Bivariate logistic regression of the relationship between the use of resources for teaching challenging concepts on teachers likelihood to have a high teaching activity.


#### *5.3. Differences in Teachers' Perceptions of STEM Teaching Based on Their Demographics*

Next, we assessed the statistical differences between teachers' measures based on their demographic differences (teachers' demographic distribution is given in Table 1). This was done by employing the Mann–Whitney U test between two groups and the Kruskal–Wallis H test in cases with more than two groups.

#### 5.3.1. Gender and Teaching Barrier

A Mann–Whitney U test was performed for SCTB scores based on gender groups. Results showed a statistically significant difference (U = 9704, *p* = 0.047) between the SCTB scores for male and female teachers. Female teachers (N = 136) had a higher mean rank of 160.15 than male teachers (N = 163), with a mean rank of 141.53. This illustrates that female teachers faced more barriers due to school-related issues than male teachers.

#### 5.3.2. Age Group and Teaching Barrier

A Kruskal–Wallis H test was performed for SCTB scores based on the age group of teachers. The analysis revealed a statistically significant difference in SCTB scores across teachers of different age groups, χ2(3) = 11.486, *p* = 0.009, between the mean ranks of at least one pair of groups. For the six pairs of groups, Dunn's pairwise tests were used. Teachers in the 30 and below age group (mean rank = 186.10) faced significantly higher schoolrelated barriers than those in the 51 or older age group (mean rank = 120.68) (*p* = 0.006, adjusted using the Bonferroni correction). There was no evidence that the other pairs were different. This indicates that the extent to which teachers face school-related teaching barriers statistically differs based on age. Hence, age is a factor that affects the teachers in creating a barrier to STEM teaching, and this barrier is due to school-related issues.

#### 5.3.3. Grade Level of Teaching and Teaching Barrier

A Kruskal–Wallis H test was performed for SCTB scores based on the teachers' grade level of teaching. The analysis found a statistically significant difference in SCTB scores between the different grades that teachers taught in, χ2(2) = 9.384, *p* = 0.009, between the mean ranks of at least one pair of groups. Dunn's pairwise tests were used for the six pairs of groups. There was a marked difference between teachers who taught grade 12 and teachers who taught both grades 11 and 12 (*p* < 0.01, adjusted using the Bonferroni correction). There was no evidence that the other pairs were different. This reveals that the extent to which teachers face school-related teaching barriers statistically differs based on the grades they teach. Thus, grade level of teaching is a factor that affects the teachers in creating a barrier to STEM teaching, and this barrier is due to school-related issues.

#### 5.3.4. Graduation University and Teacher's Pedagogy

A Kruskal–Wallis H test was performed for TP scores based on the university education of teachers. Teachers reported having completed their university or college studies inside Qatar, at an Arab university outside Qatar, an American or European university outside Qatar, or an Asian or African university outside Qatar. The analysis discovered a statistically significant difference in TP score between the different universities from which teachers obtained their degrees (χ2(2) = 11.862, *p* = 0.008) between the mean ranks of at least one pair of groups. Dunn's pairwise tests were used. There was a significant difference between the group of teachers who graduated from an American or European University outside Qatar and those who graduated from an Asian or African University outside Qatar (*p* = 0.015, adjusted using the Bonferroni correction). Moreover, there was substantial evidence (*p* = 0.033, adjusted using the Bonferroni correction) of a difference between the group of teachers who graduated from an Arab university outside Qatar and those who graduated from an Asian or African University outside Qatar. There was no evidence showing that the other pairs were different. This indicates that the extent to which teachers apply pedagogical approaches in their classrooms statistically differs based on the university from which they graduated. Therefore, teachers' academic background influences their STEM pedagogical approach to teaching students.

#### **6. Discussion**

This study highlights salient barriers facing high school teachers in Qatar when teaching STEM. In this study, our analysis utilized various factors that predict these barriers and the results revealed associations between teachers' demographic characteristics and environmental (contextual) variables, student-related barriers, school-related barriers, pedagogical approaches, and teachers' classroom activities.

#### *6.1. Barriers to STEM Teaching*

Social cognitive theory [49] and attribution theory [20,21] both provide a rationale for considering school context and other factors. As per the standardized beta weights, the context has a stronger relationship with teachers' perceptions than major background factors and personal opinions. This conclusion is corroborated by DeChenne and colleagues [50] in their study of 128 alumnus teaching assistants in STEM, which looked at the origins of teaching self-efficacy [50]. The study's findings showed that instructional self-efficacy is primarily influenced by the perception of the instructor's departmental and environmental aspects. In contrast to the current study's findings, the researchers discovered that environmental factors, such as the resources and allocated time, had a more significant impact than the peer-teaching relationship. Another study by [51] found a more profound link between instructors' self-efficacy and access to assets, as opposed to community support. Therefore, we assessed various environmental factors to understand the barriers teachers encountered in STEM teaching. This includes both school-related and student-related barriers.

The results derived from the present study disclosed three specific barriers to STEM teaching as reported by teachers: students' lack of the required skills, students' lack of the required knowledge, and students not having enough sleep. These results echo recent findings by [52–54]. These studies revealed that teachers noted that students often faced difficulty in solving STEM-related problems, did not perform well in academic areas, and were thus unable to apply their knowledge to self-directed STEM-related issues. While these problems may indicate that teachers felt their students lost interest in learning STEM, further empirical evidence is needed to explain how and why these challenges persist.

The decline in student interest was also reported to originate from a lack of parental and family involvement. These results corroborate findings of a study by [55], who concluded that parents' negative perceptions of STEM, particularly in communities bound by social or cultural norms, hamper teachers' STEM teaching. Indeed, several studies ascribe the decline in student STEM interest to a lack of parental and family involvement [56–61]. Prominent instances illustrating this decline include the absence of parental encouragement, lack of parental assistance with STEM subjects, and low parental aspirations or expectations [56].

The gains that parental involvement entails for students' STEM learning in particular are widely acknowledged in the literature [62–64]. Nevertheless, not all parents wanting to help their children can and know how to do so [57]. For example, parents' knowledge and understanding of the school's STEM curriculum may be limited. Different measures have effectively been used to bridge this gap between parents and children [65,66]. These possibilities for balanced STEM-related connections among children and households include school tasks, schoolwork responsibilities, after-school scientific associations, and trips to scientific centers. Community projects can also help build connections between children, parents, and educators. This provides added benefits to building constructive relationships with teachers, motivating them to be more competent in scientific training and teaching science more engagingly. Parent-teacher relationships can also be improved through such community-engaged STEM programs.

Grade level of teaching was found to yield substantial variances when analyzed against school-related teaching barriers. Our study's analyses indicated that the degree to which instructors experience school-related teaching barriers varies statistically depending on the grades they teach. Consequently, grade level of teaching is a factor that appears as a barrier to STEM teaching, which is caused by school-related concerns. This could be interpreted as implying that teachers who teach multiple grades are exposed to more teaching experience at school and are therefore more comfortable with the school-related activities.

The literature further indicates that teachers believe traditional school structures hinder effective implementation of STEM education [10]. School-related factors were measured as barriers to STEM teaching, as perceived by teachers, and assessed to determine significant differences between demographic factors. A Mann–Whitney U test for SCTB scores based on gender revealed a statistically significant difference between the SCTB scores for male and female teachers. Analysis estimates showed that female teachers faced more school-related barriers than their male counterparts. This finding has also been reported in previous work showing significant differences between female and male teachers' perception of STEM subjects [67–70].

Our study also examined whether school-related barriers facing teachers had any significant differences based on their age group. Results from a Kruskal–Wallis H test used to compare SCTB scores by teachers' age group demonstrated a statistically significant difference in school-related barriers teachers encountered based on their age groups. Consequently, teachers' age is a factor that can thwart STEM teaching. This could be due to younger teachers not being adapted to the school system or being perceived by the school in the same way as older teachers. Another possible reason could be due to young teachers being more critical of the school system as compared to older teachers. However, though previous literature has reported teachers' perception to be influenced by their experience and the time they have spent in the teaching profession [67,71–73], no reports have been made on the influence of school-related barriers based on the teacher's age.

#### *6.2. Barriers to STEM Pedagogy*

Previous studies indicate that differences in teachers' demographics can affect their implementation of pedagogical approaches in classrooms [74–76]. Our study revealed a statistically significant difference in TP scores across the universities teachers graduated from. Moreover, it was revealed that the university that conferred teachers' degrees has a substantial impact on their likelihood to use pedagogical techniques in teaching STEM. This could be due to American, European, and Arab Universities being more aware of innovative pedagogical approaches than Asian and African Universities due to educational research being more prevalent in the former. This provides implications for educationists in Qatar to emphasize employing qualified teachers from American, European, and Arab Universities. Further, emphasis on training and developing teachers to use pedagogical practices in teaching STEM could enrich high school teachers' efficacy in teaching STEM.

Also, regression analysis results indicated that teachers aged under 50 were more likely to employ pedagogical approaches in their STEM teaching. This is not to be confused with our previous finding on young teachers facing more school-related barriers. In one case, age is a predictor for employing pedagogical approaches. However, in the other case, age has significant impact on barriers to STEM teaching (this is general STEM teaching and not specific to using pedagogical approaches in STEM teaching). Though no previous literature has been reported on this, young teachers having a higher likelihood of using STEM pedagogies could be due to them having a higher passion and enthusiasm to use innovative pedagogies. On the other hand, older teachers are more adapted to traditional pedagogies and show less interests in taking up new pedagogies. These findings have implications for enhancing high school STEM teachers' ways of teaching.

Regression analysis showed that teachers who used online tools as a resource while teaching students were more likely to apply STEM pedagogies. This demonstrates that online resources were more adaptive for teachers employing STEM pedagogies. Evidence reveals that not using adequate online tools makes it difficult for teachers to integrate the technology component of STEM into their lessons [36]. In a study conducted by Yildirim and other researchers [77], teachers argued that the use of online tools in STEM classrooms piqued their students' curiosity and enhanced their inventiveness; it also encouraged enthusiasm towards learning, increased pupils' digital literacy, personalized the learning process, and simplified complex ideas.

Another regression model revealed that teachers who used educational and research journals to teach challenging concepts were more likely to employ STEM pedagogies. Using scholarly information to support teaching practices has become a standard expectation in many fields. In our study, only 34.56% of the teachers reported using educational and research journals to teach challenging concepts. While a wealth of research may be utilized to improve instructional practices, little can be found in the literature regarding how much educators search, acquire, read, employ, and disseminate research findings to help them teach [78]. A 2020 study carried out by Booher and other researchers [79] noted that teachers are interested in research and acknowledge its importance in informing their practice. However, the study also reported that teachers face difficulty identifying strong research materials and figuring out how to use that research to improve their teaching. Therefore, there is a need to change the culture and practice of research application in the classroom by increasing teachers' perspectives and practices. Further research in this area is needed in order for teachers to improve their efficacy and improve student learning using research evidence.

#### **7. Limitations**

The study's conclusions must be viewed in light of its limitations. One of the limitations of this study lies in its sole reliance on survey data of high school teachers' perceived barriers to teaching STEM in Qatar. The study's analyses, as presented above, disclosed associations between the barriers teachers reported and their demographic attributes, contextual (school-related) characteristics, and student-related factors. These results would be improved with additional qualitative data. For example, personal follow-up interviews with teachers who reported barriers related to the pedagogical approaches and classroom activities used would aid in getting an in-depth and informed understanding of these barriers. Another limitation of the present study is its focus on high school teachers' perceived barriers. Indeed, the study would benefit from looking at data from teachers in lower levels of schooling. For example, investigating data from teachers in preparatory school grades would enrich the study's findings by offering a comparative perspective.

Moreover, our findings are applicable to our sample population of Qatar-based, urban, mostly middle-class teachers. Different outcomes are expected for instructors from various demographics and ethnic backgrounds. To some extent, all of these factors may influence the interaction of the variables. To overcome this constraint, a substantially larger sample population is required. Moreover, due to the limited sample size (N = 199), there may be

minor fluctuations in the significant differences based on various variables. However, we believe that the use of logistic analysis (together with the very significant results obtained) encourages confidence in the findings of this study.

#### **8. Conclusions**

High school teachers' perceptions of the barriers that impede teaching STEM subjects, including student- and school-related influences, constitute the core of our study's analyses. Our findings revealed that although teachers reported a limited number of barriers, a few remain of concern. Student-related barriers included high school students' lack of skills, knowledge, and sleep which are perceived by teachers to affect STEM instruction. Moreover, gender-based differences existed in regard to teachers facing school-related barriers, with female teachers facing more barriers compared to their male counterparts. Age is another factor that determines teachers' perceptions of the barriers hindering STEM instruction: teachers aged 30 or younger tend to face more school-related barriers. Equally interesting, teachers' perceptions of the decline in student interest in STEM subjects seems ascribed to the lack of parental and family involvement.

The pedagogical approaches teachers adopted in STEM teaching appear to be affected by age, university education, and student-related factors. Teachers who employed more activities in their teaching process were more likely to use STEM pedagogies. In particular, teachers who used online tools and research journals are more likely to engage students through STEM-related pedagogies. These findings provide the direction to inculcate STEM education in Qatari high schools. Further research is required to investigate these important issues.

STEM teachers are an essential resource for the successful implementing of STEM education in Qatar. While student development is necessary to facilitate a harmonic STEM environment for teachers, training teachers is also critical. Teachers need to be empowered through professional development programs that target STEM-related pedagogies, especially for teachers with non-Western university degrees and those belonging to older age groups. Gender disparities among teachers need to be addressed.

Some of the student-related barriers reported by teachers can be overcome by revisiting the pedagogical approaches used in teaching STEM to motivate students and pique their interest in STEM [80]. Teachers can also use more STEM resources that could enhance students' STEM interest and improve their skills and knowledge [81]. Teachers also need professional development resources to effectively implement STEM teaching [82]. This will help teachers to develop a positive interaction with STEM concepts and methodologies. To enhance teaching integrity, instructional approaches related to STEM should be explicitly taught and demonstrated to teachers, especially those who graduated from Asian or African universities.

Furthermore, the efficient utilization and incorporation of online tools into STEM lessons necessitate collaboration between content developers and STEM educators, preferably at an early stage in the design process. This would help in the educational planning by implementing content compatible with STEM teachers' knowledge and requirements. Moreover, global business enterprises and educators now demand 21st-century skills. Shifting demographics and student diversity also necessitate a re-evaluation of instructional pedagogies and the role of technology in schools, homes, and communities. For both learners and instructors, regardless of their varying learning styles, digital resources present an opportunity for facilitating rational thought, investigation-based learning, problem-solving, and collaboration.

This study utilized questionnaire data to identify the barriers viewed to impede STEM education in high schools in Qatar from the teachers' perspective. The present study's analyses could be complemented and enhanced further with rich, in-depth qualitative information to gain real insights into the dynamics and complexities surrounding existing STEM teaching practices and the challenges that hinder effective STEM education.

**Author Contributions:** Conceptualization, A.S. and Z.A.; methodology, A.S. and Z.A.; formal analysis, M.A.; writing—review and editing, A.S., M.A. and Z.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** The project was funded by Qatar University (Reference: QUCG-SESRI-20/21-1).

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of Qatar University (QU-IRB 1424-EA/20) on 1 March 2020.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Data can be provided on reasonable request from the corresponding authors.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

