Keywords

1 Methodology - Systematic Literature Review

The principal objective of this review was to compile and present information of relevance for contextualizing the theme of gender inequality in STEM fields. The collection and analysis of articles aimed to procure precise and verifiable data, subsequently subjected to comparison with the outcomes obtained from an online questionnaire administered to students. This undertaking aimed to contribute to a deeper understanding of students’ perceptions regarding gender disparities in STEM fields. To address the central inquiry pertaining to gender inequality in STEM, particularly focusing on Technology, a Literature Review was conducted employing the Systematic Literature Review (SLR) methodology following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.

2 Systematic Literature Review

Gender inequality in the field of Technology is an issue of extreme relevance today, as women have historically been marginalized in their participation in the fields of Technology and Science. In this regard, the present study aims to analyze the perceptions of young individuals who are nearing the end of their educational journey regarding STEM education (Science, Technology, Engineering, and Mathematics) in Portugal.

The digital revolution has wielded a profound influence on our lives, ushering in opportunities while concurrently presenting substantial challenges. Ensuring equal opportunities in the labor market, fostering equitable treatment in the workplace, and striving for gender balance within the digital realm are pivotal. This pursuit not only augments the European Union’s economic growth in terms of GDP but also embodies a matter of justice for all exceptionally talented women choosing careers in STEM fields, as noted in [1].

As articulated in [1] and [2] women have made considerable progress in realms like higher education, politics, and the workforce since the early 20th century. Nonetheless, they continue to confront substantial barriers within STEM domains, traditionally dominated by men.

As highlighted in [3] gender inequality in the sciences remains a prevalent topic in the 21st century. Despite a marked expansion in studies addressing this matter in recent decades, the endeavor persists to convince society of the criticality of diversity and its correlation with enhanced scientific output. Overcoming this challenge may well hinge on the imperative need for data substantiating efficacious proposals to reshape the academic milieu. Despite strides towards gender parity, women remain significantly underrepresented in leadership roles within STEM, and progress in this regard has been sluggish since the 1960s. This phenomenon, often termed the “leaky pipeline”, underscores the departure of numerous women from STEM careers due to specific gender-related hurdles, such as biased research assessment metrics.

The goal is to understand how these young individuals are responding to campaigns promoting female involvement in these fields. The persistence of gender inequality in STEM fields is widely recognized and continues to be a concern for educators, teachers and the scientific community, as mentioned in [4]. This disparity is not just restricted to female participation, but also to perceptions and attitudes towards these disciplines. Even though the gender gap in STEM achievement is closing, there are still regions of the world where disparities persist.

The underrepresentation of women in STEM fields has significant implications for educational and socioeconomic gender disparities. Deeply understanding the reasons behind this phenomenon is one of motivations for this study.

The simplistic view that attributes the gender disparity in STEM to differences in mathematical skills has lost popularity, but still persists in the literature. This classic approach makes it difficult to understand the complex reasons for female underrepresentation in STEM, as mentioned by [5]. Current research and Portuguese-led initiatives in the STEM field highlight the importance of preparing young people for global competitiveness, emphasizing the need for integrated STEM education for multidisciplinary understanding and comprehensive skills.

3 Data Collection and Processing

As a general objective, intend to analyze the topic through data collection involving surveys of 9th and 12th classes of Professional and Scientific-Humanistic Education, from the Ponte de Sor Group, with the theme “Gender inequality in Technology - young people’s perception of STEM in Portugal”. Therefore, the main objective will be to identify gender differences in relation to the preferred training routes and the future expectations of these young people in relation to the STEM areas, placing greater emphasis on the technological area. In this context, it is intended that the visualizations of the insights resulting from this study can contribute to facilitating the understanding and sharing of the conclusions with the community and to understand in a graphic and interactive way the research and answer the key questions: “Does the evolution of young people in new technologies have an impact on their future choices and opinions regarding STEM areas? How is gender inequality manifested in this context? (depending on answers given by female or male audience)”First and foremost, it is important to highlight that data analysis is a process that examines data collected from specific questions and applies analytical techniques to extract meaningful information. The data analysis process involves various stages, such as data preparation, exploratory data analysis, statistical analysis, relationship analysis, and trend analysis.

As mentioned in [6] factors related to location, school ownership, and gender are considered important indicators when assessing educational effectiveness in terms of quality and equity. The following data analysis is based on the results of a sample, which is representative of the entire population of the Ponte de Sor municipality in Portalegre, Portugal. In this context, the data from the questionnaires for the secondary education population of the Agrupamento de Escolas de Ponte de Sor, which does not exhibit sampling errors, will be analyzed.

4 Survey Design

The use of questionnaires as a data collection instrument allowed for a more comprehensive insight into the opinions, perspectives, and experiences of the study group. Additionally, this technique enabled the quantification and systematization of responses, facilitating the interpretation and statistical analysis of results.

Therefore, it can be stated that the use of questionnaires played a crucial role in this research, providing a deeper understanding of the topic at hand and significantly contributing to the success and rigor of this study.

Developing an effective questionnaire, in this case on the LimeSurvey platform, involved several crucial steps to ensure that the questions were clear, relevant and capable of eliciting the desired information from respondents.

As mentioned in [2] and in [3] regardless of the chosen tool for creating the questionnaire, within each stage there are steps that should be followed to achieve the desired outcome, including:

  1. 1.

    Development: To create an effective questionnaire, establish research objectives, choose question types, organize questions, select response formats, and ensure clarity and impartiality.

  2. 2.

    Pre-Testing: Prior to launch, choose a survey platform, configure the questionnaire, customize its appearance, conduct a pre-test with a small sample to address comprehension issues, and maintain a conducive environment.

  3. 3.

    Administration: Publish and distribute the questionnaire through email, social media, or a website, and monitor responses for adjustments to boost the response rate.

  4. 4.

    Closing and Analysis of Responses: Close the questionnaire after achieving the desired sample or time limit, analyze data with statistical tools, and draw conclusions based on the collected information.

5 Survey Data

The development of the survey questions was based on the following papers: [7,8,8,9,10,11]. This contribution was of utmost importance since a significant portion of the reviewed papers consulted did not contain questionnaires that could serve as a reference for creating the questions to the survey for this study.

The survey was administered to students across various educational levels at the conclusion of the academic year. Specifically, it targeted students in the 9th year, 12th year of Scientific-Humanistic Education, and 12th year of Professional Education classes within the Municipality of Ponte de Sor, located in Central Portugal. A total of 61 students participated in the survey, encompassing 29.51% girls, 42.63% boys, and 27.87% who did not disclose their gender. The survey comprised a series of direct inquiries pertaining to the subject matter and was conducted online.

6 Data Preparation with Power Query and Power BI

Power Query is a data transformation and preparation tool developed by Microsoft. It is an ETL (Extraction, Transformation, Loading) tool used to extract data from sources, process it, and load it into one or more target systems, as referenced in [4].

Main steps taken in Power Query for processing data extracted in Excel from LimeSurvey:

  • Step 1 - Promote header rows: The “Promote Header” feature was used to transform the first row of the table into column headers.

  • Step 2 - Replace values for blank and N/A rows: The “Replace Values” option was used to find blank or N/A values and replace them with desired values, such as “N/A” (Not Available) or other relevant terms.

  • Step 3 - Create an ID for all respondents to identify each response and to anonymize each response: A custom column was added with a row counter to create unique IDs for each respondent using the “Add Custom Column” function.

  • Step 4 - Use filters: Filters were used to exclude responses that were not relevant for analysis. For example, respondent number 12 was the only one who provided a different (course) response at the 9th-grade level, making it irrelevant for the analysis.

  • Step 5 - Create new tables: Quick measure tables were created to present questions with multiple subparts and several possible responses for each subpart.

  • Step 6 - Table relationships: star schema model, as all tables are related to the “base data” table: after completing all the previous steps, relationships were configured between the tables according to the star schema model. This involved creating relationships between the “base data” table and the new tables created in steps 5 and 6, using columns that serve as relationship keys.

After performing all these data transformations procedures, Power Query allowed the creation of a star schema model in Power BI. This ensures that tables are related properly, enabling effective and efficient analysis.

Microsoft Power BI proved to be a valuable tool for the current data analysis, providing advanced visualization and analysis capabilities. With its intuitive interface and report generation capabilities, we were able to explore survey results in a more detailed and effective manner. This enabled us to identify trends, patterns, and relevant insights that significantly contributed to the understanding of the research topic.

For each questionnaire-related question, specific queries were formulated for which each dashboard should provide answers. This allowed for the effective and targeted visualization and analysis of the collected data. The use of dashboards assisted in extracting insights and important information from the questionnaire responses.

7 Data Analysis

Table 1 displays the main questions created resulting from the responses to the questionnaires as well as the presentation of the respective dashboards that were created to obtain these responses. An analysis is also carried out on each dashboard.

It is important to emphasize that this approach aims to distinguish between the responses given by female and male students.

Table 1. Dashboard analysis
Full size table

8 Discussion of Results

The discrepancy between the questionnaire results and the bibliographic data can be explained by differences in data collection periods and research focus. While the questionnaire focused on 9th and 12th-grade students who were already making university decisions, the bibliographic source [7] one of the main ones used, included information from younger students who were still a long way from making that decision.

9 Comparing with Related Works

It is essential to note that the findings drawn from this data may vary depending on the context and sample specifics. As such, it is crucial to interpret these results with care and take into account the differences in data collection methods and the samples involved. In summary, both sets of data underscore the need to promote an equitable and inclusive view of all professions related to science, encouraging students of all genders to pursue their interests and talents regardless of gender stereotypes. The data collected through the questionnaires identified career choices, familiarity with technology, the level of logical reasoning, primary reasons for avoiding careers in the Science and Technology field, self-efficacy related to computer literacy, and issues related to gender bias and barriers [9].

These conclusions align with what the article [5] examined, particularly the persistence of the “myth of the male mathematician” and the associated “myth that females are not good at mathematics.” This study highlights the complex interplay of social and cultural forces that sustain these stereotypes. By combining recent findings from various research areas, it becomes evident that the longevity of these myths results from the additive influence of two independent cognitive biases related to gender and mathematical stereotypes. This study emphasizes the importance of addressing these stereotypes holistically to promote a more accurate and equitable understanding of mathematical skills in all individuals, regardless of gender.

Furthermore, as mentioned in [12] understanding girls’ perceptions of self-efficacy in the field of computing and technology and its impact on career choices is essential for a deeper understanding of the experiences they face when striving for greater gender diversity in the technology field. Gender differences play a significant role in the career decisions of adolescent girls, influencing their choices of higher education courses and careers in computing and technology. Addressing this issue from the perspective of Career Development Psychology and considering the role of self-efficacy can help identify strategies to develop more inclusive support systems free from gender stereotypes in non-traditional fields like technology.

As highlighted in [8], despite women being pioneers in computing, the interest of high school female students in computing/technology and their participation in the academic and professional computing environment have been decreasing over time. This aligns with the conclusions drawn from the statistics, emphasizing the importance of providing clear information about careers in technology to young people and clarifying existing perceptions and ambiguities.

10 Conclusion

This study can serve as a guide for future initiatives and policies aimed at reducing gender disparities in this field and encouraging more young people to explore this subject. In conclusion, the aim is to address the key questions, namely the Research Questions: “Does the evolution of young people in new technologies have an impact on their future choices and opinions regarding STEM areas? How is gender inequality manifested in this context? (depending on answers given by female or male audience)”.

Based on the conclusions drawn from the analysis of young people’s responses regarding STEM fields and gender perceptions, we can infer the following in response to the research questions: 1) Impact of New Technologies on Future Choices - there appears to be a correlation between young people’s access to and familiarity with new technologies and their interest in STEM fields. Those who are more involved in technology tend to express greater interest and confidence in these areas. This suggests that new technologies play a significant role in shaping young people’s educational and career choices; 2) Gender Perceptions and Inequality in STEM Field - significant differences in gender perceptions related to STEM fields were observed. On average, girls tend to express more negative perceptions and less confidence in mathematics and science than boys. Additionally, the research identified persistent gender stereotypes, with some professions still being viewed as more suitable for a specific gender (e.g., nursing as a female profession and engineering as a male profession); 3) Gender Inequality and Stereotypes in STEM - differences in gender perceptions can contribute to gender inequality in STEM fields. Girls may be discouraged from pursuing careers in these fields due to negative perceptions or lack of confidence. At the same time, boys may be less encouraged to explore traditionally female fields, such as nursing.

While the results indicate progress towards a more inclusive outlook, there is still work to be done. The fact that some respondents still perceive certain professions as more masculine than feminine underscores the ongoing need for education and awareness regarding gender equality in STEM. As future work, it may also be interesting to apply questionnaires at the level of various teaching cycles, in different regions of Portugal (rural, urban…), which allow analyzing students’ sense of belonging within the academic community and studying to what extent the subjects influence student performance and self-efficacy as well as identifying areas where it is necessary to implement measures to improve gender equality at school, to help address problems related to sexism and discrimination.

The statistics obtained through the questionnaires will contribute for decision-makers such as educators, employers, and policymakers who wish to promote gender equality in STEM. They can use this data to guide recruitment initiatives, educational programs, and policies aimed at creating a more inclusive environment across all STEM fields.