Today, the importance and use of technology in various sectors (health, livestock, education, agriculture, etc.) is evident, as is the training of more people in STEM activities. As Oppenheimer [15] points out, “the risk of doing nothing will be enormous and will condemn the region to permanent backwardness because, in the coming years, there will be an extraordinary acceleration of scientific and technological advances that will further separate advanced countries from developing countries”. Therefore, today we need educated citizens who participate and contribute to technology-based innovation, including literacy and digital transformation using computer and mathematical skills [16]. From the experience of developed countries, such as Europe, it is necessary to generate computational thinking and problem-solving skills and abilities in students of all ages using computer tools and unplugged in the classroom [17]. Academic research has revealed that the activities carried out to strengthen computational thinking contributed to the understanding of the problem, planning, coding, and feedback that improved the daily activities of each student. As you know, nowadays the skills of algorithmic design, coding, programming, and problem solving acquire pre-eminence and are constantly changing according to the rapid advance of science and technology. In this context, computational thinking helps or contributes to a significant improvement in the generation of student competencies to be part of future jobs and be active citizens in proposing solutions for the benefit of society [18]. According to Puhlmann [19], the benefits that computational thinking brings are diverse and depend largely on what skills and competencies will be strengthened in students. In most of the studies reviewed, the competencies are related to employment, understanding of the functioning of the digital world, application in different areas, digital education, productivity, strengthening of computer programming, development of computer algorithms, gender equality, and work. team up; additionally, computational thinking is considered a tool for strengthening skills for the PISA exam in early-age students.

According to the literature reviewed, computational thinking originated with the development of skills in regular elementary school students; however, today there are successful interventions in university education, basically in the first years of college. Good practices suggest starting the strengthening of computational thinking in first-cycle students in ICT or computer science courses; moreover, applying computational thinking in courses that are not related to computer science [20,21]. In this way, by creating a referential framework for computational thinking that teachers could use to apply in their different courses, most authors agree in pointing out that “one has to go beyond training students to solve problems using a programming language” but should focus on awakening and strengthening their skills and motivations, which are considered fundamental aspects for the improvement of student performance. Terreni [22] states that computational thinking is characterized by a set of ordered skills that increase the cognitive capacity of students compared to computer programming. Moreover, he emphasizes that computational thinking is made up of various processes that lead to solving a problem, starting with understanding and problem statement, followed by the identification of alternatives to possible solutions, argumentation, use of technological resources, execution of activities and performance tests, and feedback. These processes can be applied in different disciplines according to the proposed curriculum.

Regarding the use of computer tools, there are several tools; the most common are: programming languages and environments or IDE, the Python language that is normally used in the first years of computer science or engineering careers in general; with this type of languages, students are inserted into the world of programming, interacting with computational concepts, developing applications to their needs; moreover, there are experiences of gamification before programming; thus, also, the teaching of algorithms, programming structures and programming variables through Ligthbot, mBlock and educational robots, where they focus on the fundamentals of programming and development of competences proper of computational thinking [23,24,25]. Since computational thinking is considered a cognitive process in problem solving, it involves the following skills: thinking algorithmically, thinking in decomposition, recognizing patterns, abstracting and presenting in a simplified way, and evaluating for decision-making [26,27,28]. Figure 1 shows the five key skills of computational thinking.

STEM disciplines are usually classified into two major parts: “applied” sciences (computer science, engineering, and engineering technologies) and “pure” sciences (biology, chemistry, physics, environmental sciences, mathematics, and statistics); therefore, people must follow these disciplines to be considered STEM people; in addition, the countries that have more STEM professionals are the ones that lead the world market and are considered first world countries [29].

Various organizations worldwide have the purpose of increasing the participation of more women in STEM disciplines; in this way, they mitigate the gender gap. In order to reduce the gender gap, they depend on many factors, not only the cultural and socioeconomic context but also factors such as self-efficacy, self-perception, and the educational experiences received from the school stage or regular basic education [30]. Six factors are indicated as a result of research carried out by various authors that cause the underrepresentation of women in STEM disciplines; these factors are: “(1) cognitive ability”, “(2) relative cognitive strengths”, “(3) professional preferences”, “(4) lifestyle and values”, “(5) field-specific ability beliefs”, and “(6) stereotypes and biases related to gender”. Factors 1 and 2 are related to mathematical and verbal reasoning, while factors 3, 4, and 5 are related to motivation that has an impact on personal and group interests, positive mentalities, positive goals, and positive personal values; finally, factor 6 is related to the sociocultural aspects that mainly affect the cognitive and motivational parts [31,32,33,34].

In most of the women who became interested in STEM careers, it was from personal experiences; for example, participating in extracurricular activities, seeking family support, contacting and interacting with stakeholders, and seeking appropriate information; meanwhile, those students who chose a STEM career based on self-efficacy performance from their personal experience of success in the classroom; finally, already being in the race, there are external factors that are related, such as infrastructure, technology, teachers, etc., that contribute to the validation of their objectives or otherwise lead to failure, such as student desertion. Ortiz-Martínez [35] proposes developing activities to maintain students’ interest in STEM careers; thus, continuously monitor until you achieve your goals, mainly in the first year of your professional career. In order to have concrete results, she recommends carrying out activities in a controlled context where feedback can be given and actions can be taken instantly. Regarding men, research indicates that for them there is greater dissemination to inculcate them in STEM careers; moreover, there are more funds or financing for technological activities than their female counterparts [32,36].

According to the scientific literature, there are eight effective pedagogical methods or techniques for girls to awaken their inclinations toward STEM careers: involvement and empowerment; diversification of culture; immersion in other languages; guidance; practice and iteration; synthesis; collaboration and communication; and reflection. These principles are made effective through practices or activities that use technological resources to solve problems that motivate girls to solve real-world problems that are of direct interest because they are related to the needs of their context, such as protection. and community service [37]; developing these experiences from an early age in girls are of vital importance to instill in STEM disciplines, as professional women scientists point out, indicating that 66% of women generated interests in science and technology before starting secondary education [38,39].

In the educational field, the most common factor to deal with is gender, which is related to the performance and attitudes of girls and boys; they even differ in common reading and writing activities. Regarding the development of computational thinking skills through the proposal of educational robotics activities in school education according to gender, it is scarce [40]. In the fields of computing or electronics, social stereotypes also negatively affect girls’ motivation [41].

In the studies carried out on the development of computational thinking skills, they state that gender took on greater importance in the field of regular basic education [42]; moreover, it is stated that computational thinking seems to have a moderate gender bias, since the activities are mostly more oriented to children [43]; moreover, it is evident that the proposals of activities or projects are preferred by children because they are of an implementation or construction and programming nature that is reflected in the computational thinking score than that of women; this shows us that there are gender differences due to the type of project proposal; therefore, the type of activity or project presumably influences the increase in the gender gap between boys and girls [44].

In the activities developed to strengthen computational thinking, there are representative differences in the use of strategies and approaches for boys and girls that are appropriate to them; the results indicate that the gender gap in the computational thinking competence is almost non-existent, because the proposal of the pedagogical techniques used in the process of teaching computational thinking skills inspired boys and girls to continue exploring; regarding the instructional design, they state that it must be adapted to the nature of the boy and girl; for example, the planning and implementation of prototypes and coding, since girls and boys have different strategies in the development of their activities; in this way, personalized tasks are provided according to gender in the execution of computational thinking activities [42,45,46]; additionally, tools such as educational interactive games, block-based programming, educational robotics, etc. are added. Together with an adequate strategy based on gender, significant improvements are achieved in the mitigation of the gender gap [47,48]. Another important aspect to consider is collaboration and teamwork between genders, where forming teams made up of both sexes benefited both men and women from teamwork when solving problems of the proposed activities, participating equally [49]. These demonstrated experiences are the basis for proposing innovative pedagogies in the development of computational thinking with a gender approach appropriate for the age at the school stage and the first years of higher education [50].

It is evident that in the scientific literature there are many initiatives in the concept, technological tools, evaluation instruments, and skills in relation to computational thinking, to a lesser extent with respect to gender in the achievement of computational thinking skills; however, this topic has been gaining more interest, especially in regular basic education, where it is desired to inculcate with educational activities or strategies so that more women bet on areas related to technology in the profession they wish to follow; in this scenario, computational thinking plays an important role in mitigating the gender gap.

To date, there are various instruments for the evaluation of computational thinking, both in the field of regular basic education and higher education; these instruments differ mainly in the complexity that is related to the ages of the students.

In the field of regular basic education, there is the proposal of Román-Gonzalez [51] that proposes the Computational Thinking Test (TPC) made up of 28 questions with programming characteristics based on blocks; Zhong [52] proposes the three-dimensional integrated assessment framework based on the work of Brennan [53], which contains six types of tasks based on three dimensions: directionality (forward task and reverse task), openness (open task, semi-open task, and open task). closed) and process (self-report or reflection report); with this type of task, computational concepts, computational practices, and computational perspectives are evaluated; Sáez-López’s [54] proposal is based on the evaluation of block-based programming syntaxes and also on the dimensions of computational concepts and computational practices; moreover, computational thinking skills have been evaluated using the Dr. Scratch software [55,56,57]. This tool evaluates programming logic, data structure, abstraction, modularity, parallelism, etc., basically the content of the program developed in the Scratch environment.

In the field of higher education, various researchers used the Román-González Computational Thinking Test (CTt) [51], an instrument validated in criteria and convergence by international experts [58,59]. It was used in basic education by regular students with ages ranging from 10 to 16 years in the European educational context [19]; it has also been used in research in the university environment, focused on university students who are starting their careers [25,60,61].

In a recent investigation, Román-González [62] points out that the construct of computational thinking has reached a state of maturity, concluding that to date, computational thinking is a type of cognition with a high level of abstraction that serves both to solve problems and to create and express ideas, and that for this purpose, it can rely on both traditional computer programming and model building. of “machine learning”; regarding the computational thinking assessment instruments, he points out that there are instruments for the different educational stages, the most representative being the “Beginners Computational Thinking Test” (5–10 years) [63], the “Computational Thinking Test” (10–16 years) [58], and the “Algorithmic Thinking Test for Adults” (>16 years) [64]. This last proposal, due to the age range, could be used in the university environment; however, only the test of algorithmic thinking would be evaluated.

In recent years, in the university environment, various instruments have emerged to assess computational thinking in university students, which involves the assessment of computational thinking skills and attitudes [65,66,67]. In this proposal, the skills of “abstraction, decomposition, generalization, algorithmic thinking, and evaluation” were evaluated, as were the attitudes of “problem solving, teamwork, communication, and spiritual intelligence.”