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Article

STEM and HASS Disciplines in Architectural Education: Readiness of FAD-STU Bachelor Students for Practice

by
Tomáš Hubinský
*,
Ján Legény
and
Robert Špaček
Faculty of Architecture and Design, Slovak University of Technology, 812 45 Bratislava, Slovakia
*
Author to whom correspondence should be addressed.
Educ. Sci. 2022, 12(5), 294; https://doi.org/10.3390/educsci12050294
Submission received: 15 March 2022 / Revised: 11 April 2022 / Accepted: 18 April 2022 / Published: 20 April 2022
(This article belongs to the Special Issue Design-Based Science, Technology, Engineering and Mathematics (STEM) Education)
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Abstract

:
Since the beginning of this century, STEM education has become increasingly important in preserving prosperity and economic competitiveness. Architecture has its own specific attributes. It overarches the STEM and HASS disciplines, and it should be perceived as a cultural phenomenon rather than as a field of study. The main objective of this article is to highlight the methodology based on the statistical method evaluating the correlation rate between the Bachelor’s student performance (SP) in design studio courses and STEM and HASS categories, represented by particular subjects of various areas of study. The relationship between the admission examination procedure and the academic performance of graduates in the DESIGN category was also analyzed. Although the level of knowledge and skills required based on the study results within the curricula was more significant in the HASS category, the direct correlation between subjects in the STEM category, especially engineering, and the quality of the design studio′s outputs as the main and fundamental part of the creative architectural work, was also confirmed. The authors of the article found that STEM knowledge and STEM skills do not reach the required level and, therefore, the emphasis should be placed on changing curricula, balancing the ratio of STEM and HASS categories, adjusting the credits assigned to STEM subjects, or reviewing the classification system.

1. Introduction

Humanity is characterized by the acquaintance of knowledge in various disciplines, to ensure its development, preserve culture, and respond to numerous challenges associated with its activities. It is the basis and assumption for progress, productivity growth, and value/wealth creation. In general, high-quality education allows young people to develop their knowledge, skills, and competencies. It provides them with the system to learn and comprehend the global/local background, as well as the possibility to become creative, active, and responsible citizens fully integrated into society, while meeting its requirements and expectations. In addition, it gives them the option to influence and shape the future and to participate in economic activities.
In the beginning of the 21st century, there emerged a strong need to emphasize the links between prosperity, knowledge-intensive jobs dependent mainly on science and technology (S & T), and continued innovation, especially incremental technological innovation of products and services, to address societal problems and achieve economic competitiveness. Therefore, S & T and its transfer from the education system to the economy play a central role in the modern society based on knowledge and technology [1,2,3]. In this context, traditional education in science, technology, engineering, and mathematics (also known, colloquially, as STEM education) is an inter-/multi-/transdisciplinary approach to curriculum in many educational systems connecting independent disciplines and represents the primary gateway of higher education to the field of STEM work [4,5]. In fact, the symbiosis of design/creative thinking grounded in inspiration, knowledge, and experiences, and the ability to apply the STEM principles is critical for rational and quantitative designs that meet performance specifications and requirements [6]. In this regard, there exist several educational approaches, articulated by numerous authors, for how to synthesize, evaluate, and formulate solutions to real-world problems such as problem-based, project-based, and design-centric learning [7,8,9,10]. For instance, design-build projects are very popular among students of the Faculty of Architecture and Design of the Slovak University of Technology in Bratislava, Slovakia (hereafter referred to as FAD-STU). These projects require/force students to think broadly. They must respect production proceedings as well as account for transport and time limitations, but they mainly investigate the features of various materials, deal with many technical issues, propose joints and pipe connections, and so on [11]. Another example of how to incorporate the STEM principles into design considerations of the problem represents the concept of reverse engineering [12,13].
In contrast to STEM disciplines, there are the so-called HASS disciplines, which are associated with the humanities, arts, and social sciences. One can argue that their significance lies mainly in developing and securing the cultural values of societies. However, the first group of STEM disciplines existed and succeeded throughout history only because of the latter. This statement confirms the famous book by Charles Percy Snow, titled “The Two Cultures and the Scientific Revolution”, and published in 1959. According to them, the great cultural is divided into two separate considerable areas of human intellectual activity: science and the arts. To advance human knowledge and benefit society, all practitioners, ‘scientists and non-scientists’ in both areas, should build bridges. Snow argues that scientific culture is really a culture, not only in an intellectual sense but also in an anthropological sense, with common attitudes, common standards, and patterns of behavior, common approaches, and assumptions. Apparently, it spreads, surprisingly, more and deeper across other mental patterns, such as those of religion, politics, or class [14].
The main objective of this study is to examine the correlation between the STEM and HASS disciplines represented by particular subjects within the Architecture and Urbanism Bachelor’s study program curriculum at the FAD-STU. Based on student performance (SP), the authors of the article evaluated their readiness for practice. In general, the SP usually includes class participation, various types of assignments, individual written work on papers and exams, or group activities such as projects and presentations. Bachelor’s graduates were investigated because they represent the first cohort of graduates that penetrates the architectural labor market. The results of an internal FAD-STU online survey on alumni in practice, conducted in 2021, revealed that most of the graduates worked in the field of study as practicing architects [15].

Specifics of Architectural Education

The relevance and importance of the HASS disciplines could be, predominantly, perceived through architecture, which has its own specific attributes. As one of the most ancient professions, architecture is a cultural arena based on ideas that jointly produce styles and individually generate outstanding buildings. It is also a product of the technological state of its time that is expected to innovate in its forms while ensuring perfect performance and adaptation to the environment [16]. Using Snow’s words, the authors of the article are of the opinion that architecture and architectural education deal with the issue of the bridge effect and are overarching multiple STEM and HASS disciplines. In contrast to many other built-environment disciplines, architecture is a multidisciplinary field of study and can be perceived rather as a cultural phenomenon. In essence, it combines artistic beauty with scientific and engineering precision. It draws on arts, sciences, and social sciences and, therefore, can be included in a multidisciplinary STEAM group of disciplines in which the letter A means the extension STEM by the arts and the ability to formulate ideas and present them convincingly [17,18]. Architectural design involves some concrete skills, including knowledge of drafting, architectural materials, and structural elements, as well as other abstract elements such as time, space, environment, and character [19].
The discussion on quality of architectural education and the ratio of STEM and HASS disciplines in curricula has been permanently discussed for ages in the academic sphere. At one pole there are faculties/universities mostly focused on artistic performance. In this case, the role of the architect seems to be reduced to that of a shaper, a form-giver, a designer completely decoupled from the realization of the building, its constructive conception, and with very limited responsibility for the outcome of the entire endeavor. On the other hand, there are institutions that force STEM and technical subjects. Historically, the master builder was a generalist architect who had the competence and capacity to design, construct, and build an edifice. Today, many architecture schools understand their role as a practitioner-generalist in relation to the economic and social impact of projects, the complexity of regulations, the development of building technologies, or the dangers of liability with respect to malpractice. Architecture, in practice, is a participative process that involves communication with many stakeholders such as clients, users, other architects, engineers, specialist consultants, construction managers, statutory authorities, etc. [20]. Stansfield Smith states that the key to a successful architectural profession is not only the ability of that profession to represent quality and deliver high standards, but also its ability to generate the demand for architecture and the qualities it represents [21].
Although the areas in the architecture syllabus and a practical training requirement vary from country to higher institution, in general, the main areas include architectural design, the cultural context of architecture, environmental design, construction and architectural technologies, communication skills, presentation, professional studies, and management [12]. The setup and control of these main areas and standards essential to produce graduates who have a solid educational foundation and are capable of leading the way in innovation, emerging technologies, and in anticipating the health, safety, and welfare needs of the public falls within the accreditation procedure competence of individual professional organizations, institutions, and architectural certification boards around the world. In Europe, architects’ training is subject to the regulation of Directive 2013/55/EU, which specifies a balance between theoretical and practical aspects to ensure the acquisition of specific knowledge, skills, and competencies [22]. Similar documents are in operation in Canada [23], the United Kingdom (UK) [24], and the United States of America (US) [25]. Their comparison according to various Student Performance Criteria (SPC) and specific skills categorized in the STEM and HASS disciplines shows Table 1.

2. Materials and Methods

The main data for this research study were extracted from the university-wide academic information system (AIS), which is used to monitor the course of each student’s achievement across various academic subjects (their academic performance) and makes a lot of information available to the academic community, university staff, and the general public. All data have been processed anonymously, and the authors of the article, as academics, are bound by professional secrecy.

2.1. Characteristics of the Analyzed Data Set

The investigated cohort of Bachelor’s graduates included the students who started their studies in the winter semester of the academic year 2014/2015. In this generation, the total number of students enrolled was 159, of which 110 successfully finished their studies, and 49 were unsuccessful. The analysis also considered those students who have repeated any of the subjects or exceeded the standard length of study. All subjects in the study program were analyzed, except one elective course completed without classified credit. A total of 64 subjects with 235 credits were analyzed out of a maximum of 240 compulsory credits to pass the 4-year Bachelor’s study [26]. Since the study included only successful graduates, the evaluation of their academic performance was classified by grades, from the best to the worst, as follows: A (1.0); B (1.5); C (2.0); D (2.5); and E (3.0). The arithmetic mean (AM) of this classification scale was C (2.0), which corresponded to the intermediate level of knowledge and skills required. The data included the results of the admission examination procedure, as well as the results of particular tasks within it.

2.2. Data Processing Method

The very starting point for the analysis was the creation of individual student profiles. Each profile included their overall academic performance, where their results in individual subjects, as well as the results of the admission examination procedure, were recorded. The student profile analysis enabled to evaluate the relationships between individual subjects or groups of subjects using a comparative method. The data processing method consisted of four stages.

2.2.1. Stage No. 1

In this stage, as presented in Appendix A, the authors of the study established the student profile data sets (Table A1 and Table A2) and the data sets of subjects (Table A3), with assigned credits and basic statistical data such as the number of graduates, the numbers and values of the grades, and their (AM). Furthermore, subjects were classified into 3 main categories, DESIGN, STEM, and HASS, including groups of subjects according to their field and the provided knowledge. The first was the DESIGN category, in which the design studio courses were included. Design studio courses colloquially form the backbone of the future architect in practice. In general, the subjects of DESIGN category simulate the real practice of an architect who simultaneously uses knowledge/skills from STEM and HASS subjects. In the second STEM category, the authors involved the MATH—mathematical, TECH—technical, and ENGI—engineering subjects. Since any subjects in the Bachelor study did not meet the characteristics of the Science group, this category was not established. Such subjects are an integral part of Master and Doctoral studies. The third HASS category covered HIST—historical, HUMA—humanities, ART—artistic, and SOCSCI social-sciences subjects. The SOCSCI group was represented only by a single-credit subject. Therefore, this group was evaluated as non-representative and excluded from the next steps of the analysis performed. Since some subjects in their syllabus covered several areas of study, they were classified according to the predominant focus.

2.2.2. Stage No. 2

Based on student profile data sets, student performance (SP) in individual subjects and categories/groups of DESIGN, STEM, MATH, TECH, ENGI, HASS, HIST, HUMA, ART and OVERALL was determined using a weighted arithmetic mean (WAM), weighted by the number of credits of the subject. WAM in the individual subject categories and groups was also converted to normalized values (NV), so that the student with the worst SP acquired a value of 0, while the student with the best SP received a value of 1. The SP between the best and worst was proportionally divided within the interval (0, 1). The NV conversion allowed the authors to compare the SP of classified subjects and tasks of the admission examination procedure (Section 3.10). At the same time, in Section 3.9, it enabled to minimize the differences in the middle level of the required knowledge and skills for a given category/group of subjects. Credits represented a mixed value consisting of learning outcomes that are attributed to individual educational components within the Architecture and Urbanism study program as a whole and the workload, which means that one credit corresponded to 25 to 30 h of work. In the next steps of the analysis, the sum of the credits of the subjects in particular categories and groups was not considered because the authors considered the acquired knowledge and skills important without preference for the architect in practice.

2.2.3. Stage No. 3

The AM of a given category of subjects was determined as the basic parameter characterizing the level of acquired knowledge/skills of the whole analyzed cohort of students. The average value of knowledge/skills in terms of the study program for a given category of subjects was 2.0 (grade C). For the STEM subject category, including the TECH, ENGI, and MATH groups, the AM value was greater than 2.0. This fact confirmed that the students did not reach the intermediate level of knowledge/skills required on average. The largest difference was recorded in the ENGI group of subjects.
For the HASS subject category and the subjects HUMA, HIST, and ART, the AM was lower than 2.0, which means that students exceeded the intermediate level of knowledge/skills required on average. The largest difference was recorded in the HUMA group of subjects.
From three main categories, the largest difference in AM was found in the DESIGN subject category, which meant that the students significantly exceeded the intermediate level of required knowledge/skills. The AM of the overall SP was below 2.0. The relatively high oscillation of the AM (lowest 1.52/highest 2.28) indicates the disproportion between the intermediate level of knowledge/skills required, the real initial knowledge/skills of the students, and the value of credits for individual subject categories/groups. This oscillation may have been caused by other factors. The reason for this oscillation was not the subject of this analysis. However, the balance between the required level of knowledge/skills and the real initial knowledge/skills of the students is a key to effective education.
Another parameter for evaluating the course of SP in the subject categories/groups was the AM of the best and worst SP in the given subject category or group (AMbw). The AMbw-AM difference determined the degree of proportionality of the achieved SP. If AMbw-AM > 0, then in the given category/group of subjects there was a higher number of worse SP than the better ones. The opposite was true if AMbw-AM < 0. The concrete data show Table 2.

2.2.4. Stage No. 4

The SP in the individual subject categories and groups or admission examination procedure results (AR) was ranked from best to worst and compared with the modified Gaussian curve. Modification of the Gaussian curve is based on the probability density of normal distribution of real SP of given category/group of subjects or AR determined by its AM and standard deviation, cumulatively spread to the whole number of the analyzed student cohort. This comparison evaluated the suitability of the proposed assessment. In all categories and groups of subjects or admission examination procedures, the assessment in terms of normal distribution was interpreted as adequate.
The SP or AR was divided into three levels: the first level (best), the second level (moderate), and the third level (worst), which were symmetrically divided around the AM of the SP of the given category/group of subjects or AR. Values outside these three levels were marked as ‘out of range’. The division into three levels resulted from the intention of the authors of the article to compare the SP of individual categories and groups of subjects at equal levels based on the real SP or AR determined by their AM.
The SP in the STEM and HASS categories were, subsequently, compared with the DESIGN category represented by the design studio courses. The correlation rate (CR) between the number of SP in levels of the STEM and HASS categories or the number of AR in levels of the admission examination procedure and the number of SP in levels of the DESIGN category of subjects was evaluated by geometric mean. Its value demonstrates the following Equation (1):
  i n t e r s e c t i o n   o f   n u m b e r   o f   S P   o f   g i v e n   l e v e l   a n d   c a t e g o r y / g r o u p   a n d   o f   n u m b e r o f     S P   o f   g i v e n   l e v e l   i n   D E S I G N   c a t e g o r y   n u m b e r o f   S P   o f   g i v e n   l e v e l   a n d   c a t e g o r y / g r o u p ×   i n t e r s e c t i o n   o f   n u m b e r   o f   S P   o f   g i v e n   l e v e l   a n d   c a t e g o r y / g r o u p   a n d   o f   n u m b e r o f     S P   o f   g i v e n   l e v e l   i n   D E S I G N   c a t e g o r y   n u m b e r o f   S P   o f   g i v e n   l e v e l   i n   D E S I G N   c a t e g o r y = C R   %
The comparative analysis was performed only in one direction, which means that the influence of the SP in the STEM or HASS categories on the SP in DESIGN was examined. The analysis was not performed in the opposite direction and, therefore, it was not evaluated how the SP in the DESIGN category affects the SP in the STEM and HASS categories. The partial findings are shown in Figure 1.

3. Results

As indicated in Figure 1, all other interrelationships were examined. The final findings on the correlation rate between STEM, HASS categories/groups of subjects, and the DESIGN category calculated according to Equation (1) are shown in the following paragraphs. The correlation rate (CR) is expressed as a percentage. The darker the cell, the higher the degree of correlation rate (Table 3). In Table 4, Table 5, Table 6, Table 7, Table 8, Table 9, Table 10 and Table 11 the integer represents the count of SP of a given level in category/group.

3.1. TECH vs. DESIGN

The correlation rate between the TECH and DESIGN categories was relatively low (Table 4). The most prominent CR was on the first level of acquired knowledge. It was assumed that students with above mean SP (1st level) from TECH subjects can adequately apply these knowledge/skills in design studio work. In general, the design studio work is characterized by an effort to establish the student’s ability to creatively implement the acquired knowledge/skills from theoretical subjects into the process of creation. From the comparison of the relationship between TECH and DESIGN, it is possible to assume that to be able to implement knowledge/skills from theoretical subjects into the creative processes within the design studio, it is necessary to acquire their critical mass.

3.2. ENGI vs. DESIGN

Table 5 confirms that SP in the ENGI and DESIGN categories correlates the most among all groups of subjects within the STEM category. Therefore, it is possible to assume that the level of knowledge acquired in the subjects of the ENGI category is directly related to the quality of design studio outputs that were included in the DESIGN category. At the same time, it is possible to conclude that the mastering of the design studio work from the engineering point of view, such as statics, the quality of project drawings, or building equipment, influences the evaluation of the works on a regular basis.

3.3. MATH vs. DESIGN

Student performance in MATH and DESIGN categories does not highly inter-correlate, respectively, MATH correlates more significantly with random levels as shown in Table 6. For example, the third level of MATH correlates with the first level of the DESIGN category. Based on this result, it can be assumed that MATH knowledge is not directly related to the quality of design studio outcomes. However, it is necessary to highlight the importance of mathematics for the study of subjects included in the TECH and ENGI categories. In this study, the relationship among MATH, TECH, and ENGI is not investigated.

3.4. STEM vs. DESIGN

According to the results shown in Table 7, student performance between STEM and DESIGN categories correlates well but less significantly than in the case between HASS and DESIGN categories. Based on the findings from the relationship among TECH, ENGI, and MATH groups of subjects and the design studio, it is possible to conclude that design studio work does not require such deep STEM knowledge and skills. However, practice through many shining examples shows that STEM knowledge and skills are irreplaceable, especially if the design is implemented. This difference was probably due to the detail level of the design studio work processing. Except for the Final Bachelor’s Project, in general, the output of the design studio courses is an architectural design, in which the construction and technical solution is documented at the level of a rough concept. Admittedly, another factor that influences the position of STEM in the design studio is the evaluation method. Design studio work is evaluated at FAD-STU using the peer-to-peer review method, where the output includes qualities in the entire spectrum of STEM and HASS subjects and is rated by one grade.

3.5. HUMA vs. DESIGN

Table 8 shows a quite significant correlation between the HUMA group of subjects and the DESIGN category. It is assumed that the knowledge and skills required within the HUMA group of subjects were applied in the creation of the design studio works and their evaluation.

3.6. HIST vs. DESIGN

The correlation between the HIST group of subjects and the DESIGN category was present with greater variance within the evaluation, as shown in Table 9. It is assumed that the knowledge/skills from HIST subjects are a good starting point, but in relation to other evaluation criteria of design studio work, they do not have a distinctly reserved position.

3.7. ART vs. DESIGN

The correlation between ART and DESIGN was most pronounced at the lowest level (Table 10). It is assumed that the aesthetic qualities acquired within the ART group of subjects were the most significant evaluation criteria for lower-quality design studio works. With good quality design studio works, the design was evaluated in a wider range of qualities.

3.8. HASS vs. DESIGN

The correlation between HASS and DESIGN was more significant compared to the STEM and DESIGN categories (Table 11). It is assumed that the degree of correlation reflects the overall better readiness of the students in the theoretical subjects of the HASS category, which corresponds to a lower AM of SP compared to STEM. At the same time, HASS qualities are the predominant assessment criteria in the design studio work evaluation.

3.9. The Relationship Overview of STEM-HASS vs. DESIGN

Using the NV of SP, Figure 2 and Table 12 show that the SP in the design studio (DESIGN) was in direct connection with SP in theoretical subjects, represented by the cumulative value of the STEM and HASS SP (calculated as AM of STEM NV of SP and HASS NV of SP). The ratio between knowledge/skills from the STEM and HASS categories was indirectly related to the SP in the DESIGN category and the cumulative value of the STEM and HASS SP. In the case of students with a worse SP in DESIGN, STEM knowledge/skills prevailed over HASS. On the contrary, for the students with good performance in subjects of DESIGN category, HASS knowledge/skills prevailed over STEM.
It is assumed that engineering creativity, which is closely related to STEM, is a natural skill of architecture students and is deepened and supplemented by HASS competencies through the educational process. For the best students characterized with the best cumulative SP values within the STEM and HASS categories, this corresponds to the required learning outcomes of STEM and HASS subjects within the study program curriculum and their AM within the analyzed cohort of students.

3.10. Admission Examination Procedure vs. DESIGN

An integral part of education is the determination of the requirements for applicants and a strategy for the selection of future students. In the European Union, there are no single selection strategies or requirements for applicants in relation to STEM and HASS disciplines in the field of study architecture and urbanism. On one pole, there exist universities with an open or no student admission examination procedure, and the selection process is managed naturally during the studies. The other pole represents universities with selective requirements for admission to study in accredited study programs. The nature of these requirements is diverse and follows the dynamic development of society and the role of the architect within it. Therefore, the intention of the authors of the article was also to confront admission requirements with student performance in the DESIGN category.
Admission examination procedures at the FAD STU are primarily focused on the assessment of applicant′s inborn talent. In the analyzed cohort of students, their STEM or HASS knowledge from secondary school was not identified during the admission procedure. Therefore, on the one hand, it is not possible to recognize a direct link between the acquired knowledge and skills from the STEM and HASS theoretical subjects and the explicit knowledge acquired from previous studies. On the other hand, it is possible to partially analyze how the degree and character of the applicant’s talent affect their study results. The admission examination procedure of the analyzed cohort of students consisted of four tasks. The first task dealt with composing the volume from geometrical bodies on a specified architectural topic in a spatial perspective. In addition to the ability to capture space with a linear drawing, this task also verified the applicant’s creativity and their perception 3D space. The maximum value of this assignment was 250 points. The second task tested the ability to realistically display the still-life model with a linear drawing and was assigned 250 points as well. The third task included the test of spatial imagining verifying the spatial sentience, respectively, it solved the intuitive tasks from projective and descriptive geometry. This task was evaluated with a maximum of 200 points. The fourth was the so-called SCIO test, provided by an external institution that assessed the general study prerequisites and the applicant’s abilities to complete university studies. The maximum value of points for this assignment was 300. The maximum point gained from the admission examination procedure was 1000 points, while the threshold to pass this procedure was set at 510 points. The first 200 applicants were given the opportunity to start the study. The fourth task was excluded from the comparison with student performance in the DESIGN category, since it was focused on the ability to complete university studies and the analyzed cohort consisted only of successful graduates. The results of individual tasks (1st task—Composing volume from geometric bodies—COMPO; 2nd task—Still life model drawing—STILL; 3rd task—Test of spatial imagining—SPAT) and the overall results (sum of the first to third task—ADMISS) were converted to NV and examined similarly to Figure 2. The relationship between the admission process and student performance in the DESIGN category of subjects is shown in Table 13, Table 14, Table 15 and Table 16 (the integer represents the count of SP of a given level in category/group).
The results of the admission examination procedure and student performance in DESIGN category, as shown in Table 13, Table 14, Table 15 and Table 16, do not correlate or correlate very slightly in the test of spatial imagining (SPAT) and composing volume from the geometric bodies (COMPO).
In fact, these tasks are inherently creative and directly related to the design studio courses. It is assumed that creativity is a natural competence of an architect. Besides the general and artistic creativity, today’s architect shall dispose with the engineering creativity, which is closely linked to flexibility, understanding of the context, and identification of the problem. The essence of engineering creativity can be perceived on the following three levels: (1) innovation in solving open-ended problems; (2) the ability to cross the boundaries of one’s own field and to expose oneself to the uncertainty of the unknown; and (3) to bring solutions that can be implemented today or in the future [27]. As a result, a more detailed focus on the creativity of the applicants could be a suitable complement to the admission examination procedure.

4. Discussion

The total value of the European construction market of 31 European countries (Europe-31) is estimated to be worth EUR 1914 billion in 2020. The sector study conducted by the Architects’ Council of Europe (ACE) on the architectural profession in Europe in 2020 also revealed that in the Europe-31 ACE member countries surveyed, there is an estimated total number of 559,070 architects contributing approximately EUR 17 billion to the economy of Europe. Of these, three-quarters of architects work full-time. As the sole principals working independently and providing a full range of architectural services to clients describe themselves as 33% of architects (more than 50% in Slovakia), making this the largest employment group in the profession. Architects working in private practice as associates or salaried architects comprise 29% of the profession, 7% describe themselves as freelance, 9% are partners and directors, and the public sector employs 13% of European architects. Architectural practices in 2020 remained strongly skewed toward the smallest size groups, as almost two-thirds are one-person practices [28].
This significant rate that reaches almost 80% in Slovakia goes hand in hand with knowledge level and great responsibility to secure the equilibrium/ultimate synthesis of three pillars of architecture introduced by Vitruvius, such as firmitas (commodity), utilitas (firmness), and venustas (delight). In addition, these requirements are nowadays broadening by highly demanded and forced restituitas (sustainability), to ensure the sustainable development and mitigate climate change and human activities on the environment.
In general, graduates are able to design simple architectural or urban proposals and structures under professional supervision, work effectively as team members, and are competent to independently prepare project documentation for constructions to a reasonable extent using computer and manual techniques. These graduates have the ability to independently prepare and evaluate materials for architectural or urban design as well as present and justify them to a professional audience.
Further investigation in this field could be conducted within the Master’s studies that enrich the previous studies with extensive professional knowledge, skills, and competences at the level of evaluation of theory, history of architecture and urban construction, fine arts, humanities and environmental sciences, construction, or technical problems, equipment, and technologies. Master’s graduates are versatile, practical, and, theoretically, prepared for independent architectural and urban planning. The authors are also interested in doctoral studies, and they consider examining the topics of dissertation theses in accordance with the STEM and HAAS disciplines. Such research forms the basis for more comprehensive analyses to properly understand the interrelationships among the subjects of different disciplines, to improve the education provided by higher education institutions, and to increase the employment of architecture students in various professional realms.

5. Conclusions

The results of this study authenticated the great importance of knowledge and skills in STEM disciplines for architects to carry out their practice. Despite the insufficient intermediate level of STEM knowledge and skills of the FAD STU Bachelor’s students, the direct relationship between student performance in STEM and DESIGN categories of subjects was confirmed. There is an assumption that balancing the ratio of the STEM and HASS categories should contribute, in the end, to higher quality of design studio outputs as the main and fundamental part of creative architectural work, which is primarily evaluated during the authorisation—the process of recognition of professional qualification.
Current discussions in the Slovak academic sector aim at the implementation of a compulsory upper secondary school leaving examination in mathematics, which is generally considered a starting point and a basic prerequisite for the successful study of STEM disciplines. In respect to knowledge productivity/creation of knowledge, beyond its acquisition and application, the STEM disciplines along with the HASS disciplines will enhance the expertise and will lead to success and maintaining a competitive advantage not only in architectural practice.
The conducted research by the authors of the article highlights a close link between the acquired knowledge/skills of the FAD-STU Bachelor’s graduates and the curriculum of the Architecture and Urbanism field of study, which emphasizes the university’s responsibility in educating architects. The sustainability of the balance between the competencies of the applicants entering higher education and the labor market and various social requirements is, indeed, a dynamic process. The statistical method applied in the analysis of the educational process, and using the student performance, proved in this study that it can be considered a suitable tool to assess the complex of interrelated factors entering the educational process. Furthermore, this analysis allows one to expand the complex of monitored parameters by the study results of the candidates before entering the higher education institution, respectively, the second or third cycle education. At the same time, the authors see a potential to supplement the student’s profile with the characteristics of their tacit knowledge, the evaluation of which is not considered in the classification system. Based on this analysis, the authors are of opinion that the traditional classification system using five grades (A–E) is insufficient, which is especially relevant for subjects such as design studio, which penetrate the content of several areas of study. This strictly scaling of the student performance does not allow to effectively evaluate the synergy of subjects from related areas. Therefore, it would be appropriate to evaluate such subjects with several grades, or to introduce a points system to difference student performance in STEM and HASS disciplines.

Author Contributions

Conceptualization, T.H., J.L. and R.Š.; methodology, T.H., J.L. and R.Š.; software, T.H. and J.L.; validation, T.H. and J.L.; formal analysis, T.H. and J.L.; investigation, T.H. and J.L.; resources, T.H., J.L. and R.Š.; data curation, T.H. and J.L.; writing–original draft preparation, T.H., J.L. and R.Š.; writing–review and editing, J.L. and T.H.; visualization, T.H.; supervision, J.L. and R.Š.; project administration, J.L.; funding acquisition, R.Š. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Slovak University of Technology in Bratislava under the contract SU:DE:EN-Sustainable Design of (Human) Environment.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy reasons.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Appendix A

Table
Table A1. Student profile data set.
Table A1. Student profile data set.
StudentAdmission Examination Procedure Results (Points Obtained)Composing Volume from Geometric BodiesStill-Life Model DrawingTest of Spatial Imagining12212_5B Descriptive Geometry I12214_5B Mathematics51103_5B Fundamentals of Architectural Design I51301_5B Construction I51401_5B Freehand Architectural Drawing51400_5B Introduction to Architecture and Urbanism51402_5B Drawing I12215_5B Descriptive Geometry II51104_5B Fundamentals of Architectural Design II51105_5B Residential Buildings51106_5B Architectural Composition51302_5B Construction II51403_5B Drawing II1_AN1_AU Design Studio I1_APR1_AU Architecture and Environment I1_DAU1_AU Architecture and Art History I1_OBC2_AU Civic Buildings II1_PPN_AU Computer Aided Design1_SF1_AU Building Physics I1_STAV3_AU Construction III1_TZB1_AU Technical Equipment of Buildings I1_AN2_AU Design Studio II1_CJ1_AU Foreign Language I1_DAU2_AU Architecture and Art History II1_KOM2_AU Composition II1_SF2_AU Building Physics II1_ST1_AU Statics I1_TZB2_AU Technical Equipment of Buildings II1_VIS_AU Manufacturing and Engineering Constructions1_VYTS_AU Art Seminar—Plein Air1_AN3_AU Design Studio III1_CJ2_AU Foreign Language II1_DAU3_AU Architecture and Art History III1_MOD_AU Modeling1_NK1_AU Structures I1_OBPA_AU Monuments Restoration1_ST2_AU Statics II1_STAV4_AU Construction IV1_URB1_AU Urban Typology I1_AN4_AU Design Studio IV1_AN5_AU Design Studio V1_DSM_AU History of Urban Planning
05802002401402.51.51.521.521.52.51.52.51.521112.51.5222.51.51.51.51.512122.51.51.51111.511.52.5111.51.5
13701609012011.52.5121.51112.51.511.51.51.521.5112.51111.51.521.51222111.51.511.52.5111.51
227060160503322.521.522222322.5231.51332.52.51.52.523222.522211.52.5322.51.51.51.52
33459010015511.521.5212111.511.52111.51.511.521111.511.51.51.522.51.51.511.52.51232111.5
4271140607131.52.531.51.52322.51.52.52.511.52.51.51.5332111.51.5322.5321111.52.522.52.51.51.51.51.5
5284130501042.53232.512.53232.532.52.522.521.52.532.51.52.521.52.5312.52.51.51.52.5232.5331.511.52.5
621960100592.52.52321.52.53221.52.52.51.5232.51.52.52.532331.533331.51.51.52231.53311.512
739314019063332.5322232.52.52.531.51.512212.52.532.51.511.522331.51.51.52.51.532332112.5
85301501901902312.51.511311.512.51.511.521.51.52.52.51.51211.52.521.531112.51.52.52331.5212
96902502402002.52.5221.51131.521.5211.51.522.51.51221.51.5212.53231232.51.52.52.5232122.5
1038217014072331211.5131.52.51.52.5221.532.512.532.52.51.531332311.5221.53331.52122
1134519010055111.531.51221.51.51.52211.52.52122.511.52112.51.522.511131.522221.5211
12478190170118221.532122.511.5132.511.5211.52.52.51.51111.52121.51.511121.51.5131112
13515110220185111.521.51111.51.5131112.51.51.512.51111.51.5211311.512.513323211.52
143951001501452.5312.52.512.531.5222.5311.5211.52.523111.51.52.533321.52.521.5322.52.51.511.52.5
153201209011032.512.52.512.531.51.51.532.511.531.5122.52.511.5112332.51.5112.52.532.531.5111.52.5
1626012080601.512321321.522.522.511.5312.52332.51.521.532.51321211.532331.511.52
1742520021015332.52.522221.52.5222223222.52.51.531.51.52322.531.5232.51312.5321.522
183055019065121.51.51.51.52111.521.521.5111.5111.5111111111.51111111111.5111
19265604016521.52.52.5212.512221.52.521.521.5132.52.51.522.522.5212.51.51.51.52.5221.51.52.52122
2023560160152.5322.5212312.51.53211.521.51232122132.52.531.51112.52.51.51.52.52122
212159070552.51.532212.531.521211.512.5212.533111122331111.52111.531.5121.5
22345120200251.52222122.52221.511.52211321.511112213111.512211.52.511.521
233321201803232321.512.532.5323321.52.51.51.51.533221233332.5211.5232.52.5321.52.52
2423512050652.51.52.5321.5232333322.531.512.52.5322.5221.52.5231.51.52.51.52.5322.52.52122
25390110220602.531.522122.51.51.52.532.511.531.513331111.5322.52.51.521.5212.52.52.52.51.511.52
26515110210195112.521.512.511.52.51.51.521121.511111211.51112.511.511.51.51.5112.51.5111
272703020040232.5321221.5222.521.51.52.51.5132.51111221.52.531.51.51.511.5211.531.5111.5
282201005070321.51.51.5232.5121.52.52221.511.522.5221213213221.51.51.532.52.532.5112.5
29355110160852.521.52.5212.52.51.52.5231.511312.5232.512.51.51.532.521.51.5232.51.52.52.53321.51.52
3036517012075111.52.51.51221.51.51.5221121121.51111111.51.52.51.51.51121.511.521111.5
313459011014522.52.52.531.5232222.522.532212.52.52.51.51.52231.52.52.5221.51.52.532333122.5
32325110130852.512.5321211.5213211.52.521.5231.5121.512212.51.51.53223232.51112
33255601603511.5232.512.51.51.51.522221221.51.531.5211.522.521.51.522121.52.51.5321.51.522
34485180200105332.532111.51.52231.52.51.52.521332.52112.52.52331.52.511.5231.52.52.51.512.52.5
35245505014532.52.53121.531.51.522.51.51.51.52.52.5222.52.51.522.52222.5311.512.51.52.52.51.52.51.511.52
36252601306221.5232.511.51.511.5221.5112.51.512.51.521111.52.51.52.5311.51312.5231.5211.52
3723760110671122.5212111.51.521.51.51.521.51.5221.51111.53222.521.511121231.511.51.5
38330170100603222.52.512.51.51.5222.521.512.51.51.522.52.511.521.52.52.52.51.5211.51.51.52.51.532.5211.52
393201908050122.5221211.5222.522121.512.52.5111112.51.53212.5111.5212.53212.51.5
403258014010523232121.51.522.52.511.51.52.51.522.53111.51.522.522.52.511112322.531.51.521
4133319070731322.51.511.52.51.51.52321.511.51.512211.521.511.51.512.5121.51222122111.5
4228012060100111.51.51122.5121.5211121.512.5111111.51.5222.5111.511221.52.51.5112
4356520020016511.52.53211.51.52.52.52311231.512321221.51212.51.51.53112.51.52.521.51.51.51.5
445401402401602.52.51.52.512122.52.512.5112321332.51.52.5232.51.532.51.51.51.52.5132.5322132.5
4543018018070132.52.522.5232.52.5231.511.521.511.52.511.51.51.5212221.5121121.532.521.51.52
466422002501921122111.511.51.5221.511.521.5121.51.521.51.51211.5212.51.51111131.512.52
473506015014022.51.52211.51.51111.51.5112111.52.511.512111.511.51.511112111.51111
486252102201951112.51.5112.5111.51.5111111.51211.51.51.51.5222.52111111.51.521.51.5112
493651401507532.52.52.521.5221.522.52.521.522.5212.52.522.521.522.52.52.5221.51.51.51.532.5332.5212.5
50260301201103231.5212.531.522.52.531.512.51.52332221.52222.531.51.521.52.52.51.51.531.51.51.52.5
51280601507022.52.52.51.512211.51.522112211.51.521211.52.51.51.52.521.51.51.51.5211.51.52111.5
524152001605531.51.52.51.521.52.5222.53122.531.51.523222.5222.522.51.5211223232.521.51.52.5
5326060110901.5122.5212.531.531.522111.51122.52.511.51.522.52231.51.512.52322.531.5112.5
5452320017015331.52.532.522.52.51.51.5232.521.531.512.52.5211.52231.5121.521.52.5222.5231.5113
55450200180702.52.5331.52231.52.52.531.51.521.52.52.52.5332111.522331.51112312.521.5221
565101502301301.531.521111.511.51.51.51112212.52.511221.52.5222.51111132.521.51.511.52
572355050135212.5311.52.5111.52.5321.5121.5112.5221.51.511.51.52.51.521.511121.5221.5111
58405200150552.532321.51.52.51.5222.51.5322.52132.5311.511.52.5212.522.52.52231.52.531.5112
595201601602001.531211221121.51111.5211.5211211.52.5312.5111212.51.5311.511.52
60300110130601.5122.5211.532222.5121.51.5222.5211.51.511.522.52212211112.521.5111.5
6127150200212.51.522.51.51.523232.52.51221.521.52211.511.51.5322211.522.51.5211.521.51.512
6249322016011311.51.531.51221122.51.51.51.51.5111.52111.5122.51.51.51.51.51.51112.511.52.51111.5
632235014033212.53232.521.52.52.52.521.521.512232.52.521.522.51.522.51.511.532.52.512.531.5222.5
6428012011050212.52.5212111.522.5211.511.5112.511111.512.512.511.51.5121.511.52.51121
655952202101651.51.51.531.51.522.51.5222.51.521.51.51.51.532.521.522232.532.5221.51.522.522.532.5222.5
66338190509821.52.531.5122.5112.52.521.5131133212.511.522331.511.52.51.52.522.52.5111.52
67237501107732.532.52.512.531.52.532.5212.52.511.52.532.531122.5332.52212.52.532.5321.5122
68342130160522.522.52112.51.51.51.52.5221.5231.5122.52.51.51223132.5211.53221.51.521.521.52.5
6923870709832.52.52.5211.5211.532.511.511.51.5132.512211.52.52.5331.51.51322.522.522222
703926023010222.52.533123222.52.5322.52.5211.52.53121132.532.51132.51.531232.522.51.5
71454180901841.51.52.52.521.5231.52.52.52.521.5132.51.51.532.51.511.52.51.52.52.532211.51.532331.5222
72382200110721.52232.51.52.51.51.532.52322321.52231112.51.51.51.52.5211.51.51.5322.5211.532
733871301609733132.522.52.51.531.52.521.51.52.511.5222.52111.522.52.521111.51.53132.51111
74365180170153212.52.512.532.51.52.532.51.51.5221.532.52.512112.51.531.5111.521.522321112
7536015012090111.532121.522.522.52.511.521.5112211.51.51.51.512.51.5211.511.5211211.521.5
764056021013512.5221.511112222.51.51.52.521.51.52.51.5111.522.52.5331.51.5121.5211.5211.52.52.5
77325901508511.51221.52111.52211111.511.51.5111212.5211.511111.51.5121.51111.5
783256016010532.51.53222.52.51.53232.51.522.52132.512.511.52.52222.52.5212.522.51.51.52.51.51.51.52
792827014072332.522.51221.5332.51.51.512211.5221.5211.53332.51.51.51.52.51.52.51311.5112.5
8032020050702.531.522.51.52.511222211.52211.52.51.52121.521.511.5211112.51.51.521112
813808010020032.5222122.513232122.52.51.51.52.531.521232.5331.521.52133321.51.51.52
823601101609023231.521.51.512.52.52.5211221321.52.511.5231.52.5321.52312.5221.51.512.52
833401301803032.51.52212.532222.521.51.511.512.51.51.51.5111.522.512.51.51.5121.52122.51.5111.5
8451817025098321.511.511.51.511.52.521.5212111.51.5211.511.51.52.511.51.511.511212.5111.51.51
85428230160382.531.532.52.51.53132.52.52122.5223332122.532.51.52.51122.51.532.5331222
864371701401271.5322.51.5122.512.5222.521.52.52.5121.51.5121.522.52.522.521121.52.512.521.51.51.52
8729770701572.5321.52.51.52.5212.52.52.52.51.51.521132.531.522232.5131.51.51.52.5232.531.51.51.512.5
882996012011912.522.5212.5322.52.532.51.51.52212.5321.51.5222.5232.5111.52.522.512.52.51.51.52.52
893421701106212.52.532.522.532.52.52.52.52.52.51.53112.5332.51.51.52.5332.5311.52.521.522.5331.5222.5
903657011018532.52.532.512312.5221.5112.52122.52.51.522132321.51.51.52.51.531.52.51.51.5112.5
91297501401071.51.52.53212.51222.522112.5212211.523231.53321121.51.52.51.531.51.522
92305100110951.5222112.5322.522.522.511.5212.53221.51.51.5223321.52.521.52.52.5331.5222.5
933852101304532.51.531.511.521.521.52121.52.51.51.532.521.51.51222.52.51.511121.52.5133111.52
94410130130150111.51.5112.5112.522211211.511.511111.51112.51.5111.51.51111.51111.5
9527013060802.5322.5212.52.512.52.5322131.51.532.51.51.52.5122.52.532.51.51.5211.52.51.52.531.51.52.52
964411601401412.5222.5211.522.522.52.511.51.51.51.522.52.5221.51.51.522.532111122.52221.51.521.5
973201501205012.52.531.5121.52.52.51.531.531211.51.52.511.5111.51.5111.51211.51.5221.5311.52.52.5
9827370140631.51.52.52.5222222.51.5311.5121.5121.521.5222.522.52.52.511111.52.522.5311.51.52
994331701709321.5231.51.51.51.51.522.5311222.51.52.52.531.512.52.52.52.51321.5121.52.5232.51.51.51.52
100544210200134232.52.51.512.52.5122.52.52111.5212.51.521111.51.522.531.51111.5212.52.51.51.51.51.5
10122312090131222.5211.51.51.52221.52.51.511.51.52.522.51.51111.511.5321.5111.521221.511.51.5
1022753015095212321221.521.521.51111.5232.531.51.51.5222231.51121.522.52.531.51.51.51
103348200100481.51.52.531.51.51.521.5322.511.51.531.512.52.522121.533321.522.511.52.52.52.531.5112
104210401007021.52.52.5212222.52.532111.51.51.52.5321.51.51221.522.52.5211.51.52.512.521.51.51.51.5
105358110200483332.52.51231.533311.51.52.52.512.522.51.52.51.522.533322.522.51.52.52.532.51.51.52.53
106315201701252322.51.511.5112.52221.51.51.5111.521.511211.51.511.511.5111.51.51.52.521111.5
107264604016421.51.522131121.522.51.5111.5122.521.51122213111.511.52.51221111.5
1083101401106032.52321231.52.52321.51221.52.532.51.52.5222.52.523211.511.521.5231.51.521.5
1092565016046231.53222.52.51.52.51.532.521.5321.5333231.51.532.5231.51.5322332.531.51.512
Table
Table A2. Student profile data set.
Table A2. Student profile data set.
Student1_INT_AU Interior Design1_KPA_AU Landscape Architecture1_NK2_AU Structures II1_PBB_AU Fire Safety of Buildings1_STAV5_AU Construction V1_URB2_AU Urban Typology II1_AN6_AU Design Studio VI1_AN7M1_AU Design Studio VII (module M 1-8)1_AN7M1_AU Design Studio VII (module M 1)1_AN7M2_AU Design Studio VII (module M 2)1_AN7M3_AU Design Studio VII (module M 3)1_AN7M4_AU Design Studio VII (module M 4)1_AN7M5_AU Design Studio VII (module M 5)1_AN7M6_AU Design Studio VII (module M 6)1_AN7M7_AU Design Studio VII (module M 7)1_AN7M8_AU Design Studio VII (module M 8)1_AS1M1_AU Studio Seminar I (module M 1-8)1_AS1M1_AU Studio Seminar I (module M 1)1_AS1M2_AU Studio Seminar I (module M 2)1_AS1M3_AU Studio Seminar I (module M 3)1_AS1M4_AU Studio Seminar I (module M 4)1_AS1M5_AU Studio Seminar I (module M 5)1_AS1M6_AU Studio Seminar I (module M 6)1_AS1M7_AU Studio Seminar I (module M 7)1_AS1M8_AU Studio Seminar I (module M 8)1_DAU4_AU Architecture and Art History IV1_PS_AU Building Project1_UNA_AU Universal Design1_VKM1_AU Selected Chapters I (module M1-8)1_VKM1_AU Selected Chapters I (module M1)1_VKM2_AU Selected Chapters I (module M2)1_VKM3_AU Selected Chapters I (module M3)1_VKM4_AU Selected Chapters I (module M4)1_VKM5_AU Selected Chapters I (module M5)1_VKM6_AU Selected Chapters I (module M6)1_VKM7_AU Selected Chapters I (module M7)1_VKM8_AU Selected Chapters I (module M8)1_AN8_AU Design Studio VIII1_ASA2_AU Studio Seminar II—A1_ASU2_AU Studio Seminar II—U1_ULG_AU Introduction to Legislation in Architecture and Urbanism11499_5B GeodesyB_AU_SS State Exam—Theory and Design of Building StructuresB_AU_SS State Exam—Theory and History of Architecture and UrbanismB_AU Final Project Defense51119_5B Civic Buildings I
011.51.51.52111.5 1.5 1 1 1111.5 1.5 1.51 1.511.5212
1111.521.511.51 1 1 1 111.51 1 11 211.521.51.5
21.52.5332.5222 2 1 1 1.52.52.51.5 1.5 1.51 212222.5
311.51.5111.51.51.51.5 11 1.51.511.51.5 11 11211.52.5
41.532.52311.52 2 1 1 222.51.5 1.5 1.51 1.5232.511
51322.51.511.51 1 1.5 1.5 2.5222 2 11 1.51.52.5231.5
61.533231.51.51 1 1 1 21.52.51.5 1.5 1.51 21.522.51.51.5
722332.51.521 1 1.5 1.5 11.521 1 11 1.521.521.52.5
81332311.52.5 2.5 1 1 2.5311 1 11 12221.53
91.5232.521.51.51 1 1 1 1222 2 1.51 2222.523
101.51.52.522.511.511 11 12311 11 1.51.52.51.51.53
11122.521.51.5111 11 11111 1.51 1.51.51112
12111.512111 1 1 1 1111.5 1.5 11 1.51.51211.5
131331.53311 1 1 1 1.5122 2 11.5 212.51.513
141.52.532321.52 2 1.5 1.5 1.5231 1 21.5 2.5221.52.53
151.51.5322.5211 1 1 1 1.51.521.5 1.5 1.51.5 212111
16223231.511.5 1.5 1 1 1.51.532 2 12 2231.513
1711.52231.51.51 11.5 1.5212.51 11.51.5 1.52.532.523
181111.51111 1 1 1 1111 1 11 111111.5
191.52.521.521.521.5 1.51 12.5211 11.51.5 21.531.523
201.512.51.531111 22 11111 11.5 1.5122.513
21121.522111 1 1 1 1.5111 1 1.5 121.51.51.522
221121.5211.51 1 1 1 11.52.51 1 11 1.51.511.513
231.5331.52.51.521.51.5 1.51.5 21.5111 32 221.51.533
24112.51.521.51.511 11 22211 1.51 232223
251.52.532.52.511.51 1 1.5 1.5 11.531.5 1.5 11 12.52.5222
2611.51.51.5111.511 11 112.511 1 11.51.52122
2711.5222111.5 1.5 1.5 1.5 1.5212 2 11 21.5211.51.5
2811.52.52.5211.52.5 2.5 1.5 1.5 132.51.5 1.5 1 12321.513
291.52.531.52.51.51.511 11 22111 21 1.532123
301.51.51.51.511.5111 11 11111 2 11.512.51.51.52
312232.52.52.51.51.5 1.5 1.5 1.5 2222.5 2.5 31 232221.5
321.51.521.52.51.51.51.51.5 11 1.52211 11 132.5111.5
3322.531.522.51.52 21 1111.51.5 1.51.5 11.52.511.52
342.52.53331.523 3 2.5 2.5 321.52 2 2.5 1.51.522.5233
352322.5311.51.5 1.5 1.5 1.5 2211.5 1.5 11 1.532223
3611.52221.51.51 1 1 1 211.51.5 1.5 11 1121.51.51.5
371.5221.521.521 1 2 22222 211 213111.5
38232.51.52211 1 2 2 1.51.511.5 1.5 11 1.52.521.51.51.5
391.521221.51.522 1.51.5 21.5211 21 11.522.521.5
4022.53221.521 1 2 2 222.52.5 2.5 21 1.511.512.52
4112222.51.511.5 1.5 1.5 1.5 111.51 1 11 21.52322
42121.522.511.51 1 1 1 2111 1 1.51 111.51.522
431.523221.511 1 1 1 21.51.52 2 11 121.51.51.52
4413223111 1 1.5 1.5 2.522.52 2 1.51.5 1.51.51.521.53
451.51.5322111 1 1 1 21.51.51 1 11 1.51.51.51.51.51
461.51.512211.51.51.5 11 1.51122 11 1.511.5111
4711.5111.51111 11 11.52.511 1 111.51.5112.5
4811.5221.5111.51.5 11 2221.51.5 1.51 1.51211.51
491.52.52.52.52.5322 2 1 1 2.51.522 2 21 22.52.5223
501.52.531.53121.5 1.5 1.5 1.5 2121 1 2 2131.5223
511.511.51.521.51.511 11 21311 1.51 1.521.51.51.53
52122.52331.52.5 2.5 1.5 1.5 2.5322.5 2.5 21.5 123333
5311.52.5221.51.51 11 112.521 11 11.521.52.51.53
541.52.532.52.5321.5 1.5 2 2 21.52.51.5 1.5 21.5 21.52222.5
551.51.5322.51.51.52 2 1 1 2131.5 1.5 12 1.531.51.523
56123221.51.51.5 1.5 1.5 1.5 1.51.511 1 1.51 111212
5711.5212.511.511 11 21211 1.51 1.5221.51.51.5
58223231.51.51.5 1.5 1 1 21.51.53 3 2.51 1.51.521.51.51.5
591.51.5321.521.51.5 1.5 2 2 122.51 1 11 111.5221.5
601.521.51.51.511.51 1 1 1 22.51.51 1 12 1.5211.52.52
611.522.52.5221.51.5 1.5 1.5 1.5 22.51.52 2 12 1.523322.5
6211222111 1 1 1 2121.5 1.5 11 11211.53
631.52.532.531.51.51.5 1.5 1.5 1.5 1131.5 1.5 11 1.5231.52.53
64111221111 11 111.51 2.51 1.51111.51.5
652331.53221 1 1.5 1.5 1.532.52.5 2.5 11.5 1.532323
6611.52.51.521.511 1 1 1 21.52.52 2 1.5 11.51.5121.52.5
672.522.532.5212 2 1 1 222.51 1 1.51.5 11.521.51.52.5
681.52.531.531.51.52 2 1.5 1.5 221.52 2 11 21.531.522.5
691.52.531.52.511.51 1 1.5 1.5 1.51.512.5 2.5 11 1.51231.53
7022322.521.51.5 1.5 1.5 1.5 22.511.5 1.5 1.52.5 1.51.511.521.5
712.53112.521.51 1 1.5 1.5 22.52.51 1 12 23211.53
722231.52.5222 2 2 2 2.5331.5 1.5 11 1.51.52.5221.5
73122.51.51.5111.5 1.5 1.5 1.5 1111 1 1 111.51.51.51.53
741.522.51.52.5211 1 1 1 1.5121 1 11 1.522112.5
751.522.51.52111 1 1 1 121.51.5 1.5 11 11.521.52.52
7612.521211.51 1 1 1 1.5211 1 11 1.51.52.5212
77111.51.51.51111 11 11111 11 1.511111.5
7812.52.5221.51.522 11 331.511 11 121.5112
791.53322221 1 1 1 11.531.5 1.5 11 231.51.52.52
8012.522211.51 11 11131 11 11.51.51.52.51.52
811.5332.52.52.511 1 1 1 21.52.51 1 31 12.511.522.5
82123222.51.51.5 1.5 1.5 1.5 11.51.51 1 11 11.52.521.53
831231.51.5111 1 1 1 112.51 1 11 11.51.511.52
8411.531.521.511 1 1 1 3121 1 31 221.5113
851.51.5323211.5 1.5 1.5 1.5 2.5323 3 11 232.532.52.5
8612221111 11 11.5111 11 211.51.511.52.5
8712.532.5221.51.5 1.5 1 1 1.5221 1 2.51 1.5222.51.52.5
881.5231.52.51.51.522 1.51.5 21.51.522 1.51 1.51.53322
8912.52.52321.51.5 1.5 1.5 1.5 1.522.51.5 11.5 12.52.52.522
901.52.532211.51 1 1 1 121.52 2 11 222.5223
9122.521.52.51.521.5 1.5 2 2 2.5222 2 1.51 21.52.51.523
921.52.52.52.5321.51.5 1.5 1.5 1.5 221.52 2 1.51.5 1.512.52.51.52
9311.521.52111 1 1 1 2111.5 1.5 1.51 1221.511.5
9411111.5111.5 1.51 11111.5 1.51 11.51111.51.5
9513322.51.51.522 11 2131.51.5 11 11.51.522.53
9612322.51.51.51 1 1 1 1.5111 1 1.51 1.51.51.5112.5
971.51.51.51.5221.51 1 1 1 122.52 2 11 1.51.52.51.51.52
98122.51.52.511.51.5 1.5 1.5 1.5 1.51.51.51.5 11 1.5121.511
991.51.52.522.5111 1 1 1 1.51.511.5 1.5 11.5 11.5221.52.5
100232.51.52.521.51 1 1 1 111.52 2 11 11.511.522
10111.51.51.51.511.51.5 1.51 112.511 11 1211.51.512.5
1021.52.52.51.52.51.51.51.5 1.51.5 1.512.51.51 111 11.51.5123
1031.523222122 11 1.51.51.511 1.51 1.51.52.51.51.52.5
10411.522211.51 1 1 1 21.511 1 11.5 1.521.5222
1051.533221.522 2 1.5 1.5 2.52.512 2 11 232222
1061121.5111.51 1 1 1 1111 1 1.51 2111.511.5
1071.52321.5111 1 1 1 11.511.5 1.5 1.51 11.5111.51.5
1081.5231.52.51.51.51 1 1 1 2222 2 1.51 12.511.523
10911.532.5322.51.5 1.5 2 2 22.533 3 1.5 222322.53
Table
Table A3. Data set of subjects with basic statistical data.
Table A3. Data set of subjects with basic statistical data.
SubjectCategoryCreditsSemesterYear of StudyTotal GraduatesA Grade CountB Grade CountC Grade CountD Grade CountE Grade CountAM
12212_5B
Descriptive
Geometry I		MATH4winter111025142221282.06
12214_5B
Mathematics		MATH4winter111018241623292.10
51103_5B
Fundamentals
of Architectural
Design I		DESIGN8winter1110926343562.01
51301_5B
Construction I		ENGI5winter1110272337412.49
51401_5B Freehand Architectural
Drawing		ART4winter11101130491821.86
51400_5B
Introduction
to Architecture and Urbanism		HUMA1winter1110722015211.27
51402_5B Drawing IART4winter11101020443332.00
12215_5B
Descriptive
Geometry II		MATH2summer111019162121332.15
51104_5B
Fundamentals
of Architectural
Design II		DESIGN8summer1110354719901.51
51105_5B
Residential
Buildings		HUMA4summer11105253532132.10
51106_5B
Architectural
Composition		ART5summer1110825423052.00
51302_5B
Construction II		ENGI4summer1110192939322.42
51403_5B Drawing IIART3summer11102321411961.84
11499_5B GeodesyENGI1summer11102640247131.73
51119_5B Civic Buildings IHUMA3summer11106232618372.26
1_AN1_AU Design Studio IDESIGN10winter2110433820721.49
1_APR1_AU
Architecture
and Environment I		HUMA3winter2110424419411.45
1_DAU1_AU
Architecture and Art History I		HIST3winter21108173529212.17
1_OBC2_AU Civic Buildings IIHUMA3winter21102146331001.65
1_PPN_AU
Computer Aided Design		TECH2winter2110633410301.29
1_SF1_AU Building Physics ITECH2winter211010182733222.18
1_STAV3_AU
Construction III		ENGI3winter21102122046302.41
1_TZB1_AU
Technical
Equipment
of Buildings I		TECH3winter211027162622191.95
1_AN2_AU Design Studio IIDESIGN10summer2110453519921.49
1_CJ1_AU Foreign Language IHUMA1summer2110432927921.54
1_DAU2_AU
Architecture
and Art History II		HIST3summer2110443326431.50
1_KOM2_AU
Composition II		ART4summer2110244235811.64
1_SF2_AU Building Physics IITECH2summer21108132833282.27
1_ST1_AU Statics ITECH3summer211011233627132.04
1_TZB2_AU
Technical
Equipment
of Buildings II		TECH3summer211027102223282.07
1_VIS_AU
Manufacturing and Engineering
Constructions		HUMA3summer21100171438412.47
1_VYTS_AU Art seminar-plein airART1summer2110354030501.52
1_AN3_AU Design Studio IIIDESIGN10winter3110434121501.45
1_CJ2_AU Foreign Language IIHUMA1winter3110523411671.46
1_DAU3_AU
Architecture and Art History III		HIST3winter31104118212461.71
1_MOD_AU
Modeling		ART3winter3110235426701.58
1_NK1_AU
Structures I		TECH2winter31105102636332.37
1_OBPA_AU
Monuments
Restoration		HUMA3winter31103721262151.71
1_ST2_AU Statics IITECH2winter31108211832312.26
1_STAV4_AU
Construction IV		ENGI3winter31104122528412.41
1_URB1_AU Urban Typology IHUMA3winter3110275820411.52
1_AN4_AU Design Studio IVDESIGN5summer3110623513001.28
1_AN5_AU Design Studio VDESIGN5summer31103834251121.57
1_DSM_AU History of Urban PlanningHIST3summer31101325472321.89
1_INT_AU Interior DesignHUMA3summer3110494513301.36
1_KPA_AU
Landscape
Architecture		HUMA3summer311012283123162.01
1_NK2_AU
Structures II		TECH4summer31107132123462.40
1_PBB_AU Fire Safety of BuildingsTECH1summer3110739461441.86
1_STAV5_AU
Construction V		ENGI3summer31106133931212.22
1_URB2_AU Urban Typology IIHUMA3summer3110443721441.49
1_AN6_AU Design Studio VIDESIGN4winter4110365815101.41
1_AN7M1_AU
Design Studio VII (module M 1-8)		DESIGN13winter4110593215311.34
1_AN7M1_AU
Design Studio VII (module M 1)		 13winter4221255001.34
1_AN7M2_AU
Design Studio VII (module M 2)		 13winter4241572001.23
1_AN7M3_AU
Design Studio VII (module M 3)		 13winter43201001.33
1_AN7M4_AU
Design Studio VII (module M 4)		 13winter419963101.39
1_AN7M5_AU
Design Studio VII (module M 5)		 13winter410451001.35
1_AN7M6_AU
Design Studio VII (module M 6)		 13winter4191142111.39
1_AN7M7_AU
Design Studio VII (module M 7)		 13winter44210101.50
1_AN7M8_AU
Design Studio VII (module M 8)		 13winter49441001.33
1_AS1M1_AU
Studio Seminar I (module M 1-8)		DESIGN2winter411068329101.24
1_AS1M1_AU
Studio Seminar I (module M 1)		 2winter4221831001.11
1_AS1M2_AU
Studio Seminar I (module M 2)		 2winter4241194001.35
1_AS1M3_AU
Studio Seminar I (module M 3)		 2winter43300001.00
1_AS1M4_AU
Studio Seminar I (module M 4)		 2winter4191180001.21
1_AS1M5_AU
Studio Seminar I (module M 5)		 2winter49522001.33
1_AS1M6_AU
Studio Seminar I (module M 6)		 2winter4191071101.32
1_AS1M7_AU
Studio Seminar I (module M 7)		 2winter44310001.13
1_AS1M8_AU
Studio Seminar I (module M 8)		 2winter410721001.20
1_DAU4_AU
Architecture and Art History IV		HIST3winter41103623381031.64
1_PS_AU Building ProjectENGI1winter41103628291071.65
1_UNA_AU
Universal Design		HUMA2winter411033222320121.80
1_VKM1_AU
Selected Chapters I (module M1-8)		HUMA2winter4110512922531.45
1_VKM1_AU
Selected Chapters I (module M1)		 2winter4211632001.17
1_VKM2_AU
Selected Chapters I (module M2)		 2winter4249114001.40
1_VKM3_AU
Selected Chapters I (module M3)		 2winter43300001.00
1_VKM4_AU
Selected Chapters I (module M4)		 2winter418584101.53
1_VKM5_AU
Selected Chapters I (module M5)		 2winter49103232.33
1_VKM6_AU
Selected Chapters I (module M6)		 2winter418628201.67
1_VKM7_AU
Selected Chapters I (module M7)		 2winter44310001.13
1_VKM8_AU
Selected Chapters I (module M8)		 2winter410721001.20
1_AN8_AU Design Studio VIIIDESIGN4summer411062319441.35
1_ASA2_AU Studio Seminar II-ADESIGN2summer49472156101.16
1_ASU2_AU Studio Seminar II-UDESIGN2summer4151113001.23
1_ULG_AU Introduction to Legislation in Architecture and UrbanismSOCSCI1summer4110324829101.50
B_AU_SS State Exam-Theory and Design of Building StructuresENGI3summer411016293519111.91
B_AU_SS State Exam-Theory and History of
Architecture
and Urbanism		HUMA3summer41102737281171.70
B_AU Final Project DefenseDESIGN20summer4110263734941.67

References

  1. Hallinen, J. ‘STEM’. Encyclopedia Britannica. Available online: www.britannica.com/topic/STEM-education (accessed on 5 March 2022).
  2. Avsec, S.; Ferk Savec, V. Predictive modelling of pre-service science and technology teachers’ innovative behaviour. J. Balt. Sci. Educ. 2021, 20, 171–183. [Google Scholar] [CrossRef]
  3. World Economic Forum–WEF. The Global Competitiveness Report 2019. Available online: https://www3.weforum.org/docs/WEF_TheGlobalCompetitivenessReport2019.pdf (accessed on 10 March 2022).
  4. Le, L.T.B.; Tran, T.T.; Tran, H.N. Challenges to STEM education in Vietnamese high school contexts. Heliyon 2021, 7, e08649. [Google Scholar] [CrossRef] [PubMed]
  5. Schelfhout, S.; Wille, B.; Fonteyne, L.; Roels, E.; Derous, E.; de Fruyt, F.; Duyck, W. How interest fit relates to STEM study choice: Female students fit their choices better. J. Vocat. Behav. 2021, 129, 103614. [Google Scholar] [CrossRef]
  6. Tan, D.Y.; Cheah, C.W.; Lee, C.H. Reverse engineering pedagogy as an educational tool to promote symbiosis between design and physics. In Proceedings of the 2021 IEEE International Conference on Engineering, Technology & Education (TALE), Wuhan, China, 5–8 December 2021; pp. 780–784. [Google Scholar] [CrossRef]
  7. Telenko, C.; Wood, K.; Otto, K.; Rajesh Elara, M.; Foong, S.; Leong Pey, K.; Tan, U.-X.; Camburn, B.; Moreno, D.; Frey, D. Designettes: An approach to multidisciplinary engineering design education. J. Mech. Des. 2016, 138, 022001. [Google Scholar] [CrossRef] [Green Version]
  8. Klukken, P.G.; Parsons, J.R.; Columbus, P.J. The creative experience in engineering practice: Implications for engineering education. J. Eng. Educ. 1997, 86, 133–138. [Google Scholar] [CrossRef]
  9. Kazerounian, K.; Foley, S. Barriers to Creativity in Engineering Education: A Study of Instructors and Students Perceptions. J. Mech. Des. 2007, 129, 761–768. [Google Scholar] [CrossRef]
  10. Cheong, K.H.; Koh, J.M. Integrated virtual laboratory in engineering mathematics education: Fourier theory. JEEE Access 2018, 6, 58231–58243. [Google Scholar] [CrossRef]
  11. Gregor, P.; Legény, J. Role of research in architectural education at FA-STU. World Trans. Eng. Technol. Educ. 2019, 17, 140–145. [Google Scholar]
  12. Ogot, M.M.; Okudan, G.E.; Simpson, T.W.; Lamancusa, J.S. A framework for classifying disassemble/analyse/assemble activities in engineering design education. J. Des. Res. 2008, 7, 120–135. [Google Scholar] [CrossRef]
  13. Hodge, B.K.; Steele, W. Experiences with a Curriculum with Balanced Design in All Stems. ASEE Annu. Conf. Proc. 1995, 1, 211–225. [Google Scholar]
  14. Snow, C.P. The Two Cultures and the Scientific Revolution; The Syndics of the Cambridge University Press: London, UK, 1959; pp. 10–15. [Google Scholar]
  15. Špaček, R.; Legény, J.; Brašeň, M.; Hubinský, T. Architect-a fateful mission or everyday work? World Trans. Eng. Technol. Educ. 2022, 20, 6–12. [Google Scholar]
  16. Goldschmidt, G. Architecture. In Encyclopedia of Creativity, 2nd ed.; Pritzker, S., Runco, M., Eds.; Academic Press: Cambridge, MA, USA, 2020; pp. 50–56. [Google Scholar] [CrossRef]
  17. Skowronek, M.; Gilberti, R.M.; Petro, M.; Sancomb, C.; Maddern, S.; Jankovic, J. Inclusive STEAM education in diverse disciplines of sustainable energy and AI. Energy AI 2022, 7, 100124. [Google Scholar] [CrossRef]
  18. Jablonský, T. STEM. In Studia Scientifica Facultatis Paedagogicae; Universitas Catholica Ružomberok: Ružomberok, Slovakia, 2018; Volume 17, pp. 67–72. [Google Scholar]
  19. Yürekli, İ.; Yürekli, H. Mimari tasarım eğitiminde enformellik. İtü Derg. /A Mimar. Plan. Tasarım 2004, 3, 53–62. [Google Scholar]
  20. Nicol, D.; Pilling, S. Changing Architectural Education: Towards a New Professionalism; Taylor & Francis Ltd.: Abingdon, UK, 2005; pp. 2–22. [Google Scholar]
  21. Stansfield Smith, C. Review of Architectural Education; RIBA: London, UK, 1999. [Google Scholar]
  22. Directive 2013/55/EU of the European Parliament and of the Council of 20 November 2013 Amending Directive 2005/36/EC on the Recognition of Professional Qualifications and Regulation (EU) No 1024/2012 on Administrative Cooperation through the Internal Market Information System (‘the IMI Regulation’) Text with EEA Relevance; Article 46 Training of Architects. Available online: https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=celex%3A32013L0055#d1e2735-132-1 (accessed on 5 March 2022).
  23. CACB—Conditions and Terms for Accreditation for Professional Degree Programs in Architecture. Available online: https://cacb.ca/wp-content/uploads/2019/11/2017-CACB_Conditions_and_Terms_for-Accreditation.pdf (accessed on 10 March 2022).
  24. RIBA. 2021 Procedures for Validation and Validation Criteria for UK and International Courses in Architecture. Available online: https://www.architecture.com/knowledge-and-resources/resources-landing-page/validation-procedures-and-criteria (accessed on 8 March 2022).
  25. NAAB—National Architectural Accrediting Board, Conditions for Accreditation 2020 Edition. Available online: https://www.naab.org/wp-content/uploads/2020-NAAB-Conditions-for-Accreditation.pdf (accessed on 10 March 2022).
  26. European Commission, Directorate-General for Education, Youth, Sport and Culture. ECTS Users’ Guide 2015; Publications Office: Luxembourg, 2017; p. 10. [Google Scholar] [CrossRef]
  27. Belski, I. Engineering creativity–How to measure It? In 28th Annual Conference of the Australasian Association for Engineering Education (AAEE 2017), Proceedings of the Annual Conference of the Australasian Association for Engineering Education, Sydney, Australia, 10–13 December 2017; Huda, N., Inglis, D., Tse, N., Town, G., Eds.; School of Engineering, Macquarie University: Sydney, NSW, Australia, 2017; pp. 321–328. [Google Scholar]
  28. ACE: The Architects’ Council of Europe. The Architectural Profession in Europe 2020: A Sector Study; Mirza & Nacey Research Ltd.: London, UK, 2021; pp. 11–14. [Google Scholar]
Education 12 00294 g001a 550Education 12 00294 g001b 550
Figure 1. Vertical axis—value of student performance (SP), Horizontal axis—students rank. (a) SP in the ENGI group of subjects ranked from best to worst and divided into three basic levels and SP comparison with the modified normal distribution curve. (b) SP in the DESIGN category ranked from best to worst and indicated division into three basic levels by SP levels boundaries and SP comparison with the modified normal distribution curve. (c) SP in the ENGI group of the level ‘out of range’ and their projection into the SP of DESIGN category. (d) SP in the ENGI group of the 1st level and their projection into the SP of DESIGN category. (e) SP in the ENGI group of the 2nd level and their projection into the SP of DESIGN category. (f) SP in the ENGI group of the 3rd level and their projection into the SP of DESIGN category.
Figure 1. Vertical axis—value of student performance (SP), Horizontal axis—students rank. (a) SP in the ENGI group of subjects ranked from best to worst and divided into three basic levels and SP comparison with the modified normal distribution curve. (b) SP in the DESIGN category ranked from best to worst and indicated division into three basic levels by SP levels boundaries and SP comparison with the modified normal distribution curve. (c) SP in the ENGI group of the level ‘out of range’ and their projection into the SP of DESIGN category. (d) SP in the ENGI group of the 1st level and their projection into the SP of DESIGN category. (e) SP in the ENGI group of the 2nd level and their projection into the SP of DESIGN category. (f) SP in the ENGI group of the 3rd level and their projection into the SP of DESIGN category.
Education 12 00294 g001aEducation 12 00294 g001b
Education 12 00294 g002 550
Figure 2. The primary vertical axis (left) represents NV of SP (0—worst SP, 1—best SP). The secondary vertical axis (right) shows the ratio of STEM NV of SP vs. HASS NV of SP. The horizontal axis shows the students ranked from the best to the worst according to the NV of SP (0—best NV of SP). The cumulative SP of the STEM and HASS categories was calculated as the AM of NV of SP of STEM and HASS. Trend lines were calculated by linear regression using the least squares method.
Figure 2. The primary vertical axis (left) represents NV of SP (0—worst SP, 1—best SP). The secondary vertical axis (right) shows the ratio of STEM NV of SP vs. HASS NV of SP. The horizontal axis shows the students ranked from the best to the worst according to the NV of SP (0—best NV of SP). The cumulative SP of the STEM and HASS categories was calculated as the AM of NV of SP of STEM and HASS. Trend lines were calculated by linear regression using the least squares method.
Education 12 00294 g002
Table
Table 1. Comparison of architectural knowledge and skills required according to various professional entities and their categorization in STEM and HASS disciplines.
Table 1. Comparison of architectural knowledge and skills required according to various professional entities and their categorization in STEM and HASS disciplines.
Student Performance
Criteria (SPC)		Specific SkillProfessional EntityDiscipline
EUCanada
(CACB)		UK
(RIBA)		US
(NAAB)		STEMHASS
DesignDesign Theories, Precedents, and Methodsxxx--x
Design Skillsxxx-x-
Design Tools-x--x-
Program Analysisxxxxxx
Site Context and Designxxxxx-
Urban Designxxxxxx
Detail Designxxxxx-
Design Documentationxxxxx-
Aesthetic and Technical
Requirements		x-xxxx
Culture,
Communications,
and Critical Thinking		Critical Thinking and
Communication		xxx--x
Architectural Historyxxx--x
Architectural Theoryxxx--x
Cultural Diversity and Global Perspectivesxxxx-x
Ecological Systems and
Environment		xxxxx-
Social Factorsxxxx-x
Technical
Knowledge		Regulatory Systemsxxxxx-
Materialsxxxxx-
Structural Systemsxxxxx-
Envelope Systemsxxxxx-
Environmental Systemsxxxxx-
Comprehensive
Design		Comprehensive Designxxxxxx
Professional
Practice		Architectural Professionxxx-xx
Ethical and Legal
Responsibilities		xxxx-x
Modes of Practicexx-xxx
Professional Contractsxx-x-x
Project Managementxxx-x
Overall ratio of STEM and HASS knowledge 169.23%53.85%
1 This overall ratio is only illustrative, as it varies by country and according to particular areas set by the professional organization/institution. Explanatory note: “x” included/“-“ not included.
Table
Table 2. Basic statistical data of individual categories and groups of subjects.
Table 2. Basic statistical data of individual categories and groups of subjects.
Statistical DataSubjects by Category/Group
OverallDESIGNSTEMHASSTECHENGIMATHHUMAHISTART
Best SP1.281.001.151.311.021.261.001.171.001.17
Worst SP2.442.422.722.282.792.933.002.262.502.44
AM of SP 11.791.522.141.812.102.282.091.761.781.82
AMbw of SP1.861.711.941.801.912.102.001.711.751.80
Credits235975780242310411524
Difference AMbw-AM0.070.19−0.20−0.01−0.19−0.18−0.09−0.04−0.03−0.02
Standard deviation0.260.260.360.250.410.350.580.260.360.29
1 The light gray cells represent the AM of all SP in subjects by category or group that are above the intermediate level of required knowledge and skill, while the dark cells represent those below this level.
Table
Table 3. The graphical scale of degree of CR according to Equation (1).
Table 3. The graphical scale of degree of CR according to Equation (1).
CR According to Equation (1)
Degree of correlationLow0%10%20%30%40%50%60%High
Table
Table 4. The correlation rate of the SP between TECH and DESIGN subjects.
Table 4. The correlation rate of the SP between TECH and DESIGN subjects.
Group LevelOut of Range1st TECH2nd TECH3rd TECHOut of RangeΣ
Out of range00%00%00%00%00%0
1st DESIGN424%1350%1232%39%00%32
2nd DESIGN419%619%2043%1945%00%49
3rd DESIGN16%28%1235%1238%00%27
Out of range00%00%00%224%00%2
Σ 9 21 44 36 0 110
Table
Table 5. The correlation rate of the SP between ENGI and DESIGN subjects.
Table 5. The correlation rate of the SP between ENGI and DESIGN subjects.
Group LevelOut of Range1st ENGI2nd ENGI3rd ENGIOut of RangeΣ
Out of range00%00%00%00%00%0
1st DESIGN536%1452%1128%26%00%32
2nd DESIGN16%721%2654%1537%00%49
3rd DESIGN00%14%1028%1653%00%27
Out of range00%115%00%112%00%2
Σ 6 23 47 34 0 110
Table
Table 6. The correlation rate of the SP between MATH and DESIGN subjects.
Table 6. The correlation rate of the SP between MATH and DESIGN subjects.
Group LevelOut of Range1st MATH2nd MATH3rd MATHOut of RangeΣ
Out of range00%00%00%00%00%0
1st DESIGN214%310%930%1847%00%32
2nd DESIGN317%1744%1232%1736%00%49
3rd DESIGN18%1035%829%823%00%27
Out of range00%00%00%221%00%2
Σ 6 30 29 45 0 110
Table
Table 7. The correlation rate of the SP between STEM and DESIGN subjects.
Table 7. The correlation rate of the SP between STEM and DESIGN subjects.
Group LevelOut of Range1st STEM2nd STEM3rd STEMOut of RangeΣ
Out of range00%00%00%00%00%0
1st DESIGN635%1654%620%411%00%32
2nd DESIGN314%822%1745%2145%00%49
3rd DESIGN00%311%621%1852%00%27
Out of range00%00%00%221%00%2
Σ 9 27 29 45 0 110
Table
Table 8. The correlation rate of the SP between HUMA and DESIGN subjects.
Table 8. The correlation rate of the SP between HUMA and DESIGN subjects.
Group LevelOut of Range1st HUMA2nd HUMA3rd HUMAOut of RangeΣ
Out of range00%00%00%00%00%0
1st DESIGN435%1652%1131%13%00%32
2nd DESIGN00%1437%1943%1638%00%49
3rd DESIGN00%00%928%1857%00%27
Out of range00%00%00%223%00%2
Σ 4 30 39 37 0 110
Table
Table 9. The correlation rate of the SP between HIST and DESIGN subjects.
Table 9. The correlation rate of the SP between HIST and DESIGN subjects.
Group LevelOut of Range1st HIST2nd HIST3rd HISTOut of RangeΣ
Out of range00%00%00%00%00%0
1st DESIGN113%1445%1434%310%00%32
2nd DESIGN110%1334%2448%1131%00%49
3rd DESIGN00%311%1335%1142%00%27
Out of range00%00%110%114%00%2
Σ 2 30 52 26 0 110
Table
Table 10. The correlation rate of the SP between ART and DESIGN subjects.
Table 10. The correlation rate of the SP between ART and DESIGN subjects.
Group LevelOut of Range1st ART2nd ART3rd ARTOut of RangeΣ
Out of range00%00%00%00%00%0
1st DESIGN225%1243%1740%13%00%32
2nd DESIGN00%1029%3057%925%00%49
3rd DESIGN00%28%923%1659%00%27
Out of range00%00%19%114%00%2
Σ 2 24 57 27 0 110
Table
Table 11. The correlation rate of the SP between HASS and DESIGN subjects.
Table 11. The correlation rate of the SP between HASS and DESIGN subjects.
Group LevelOut of Range1st HASS2nd HASS3rd HASSOut of RangeΣ
Out of range00%00%00%00%00%0
1st DESIGN424%2063%1026%13%00%32
2nd DESIGN419%1128%2757%1128%00%49
3rd DESIGN16%13%823%1862%00%27
Out of range00%00%110%113%00%2
Σ 9 32 46 31 110
Table
Table 12. Overview of the relationship among the parameters in Figure 2 calculated as AM of these parameters for each level of SP in DESIGN category.
Table 12. Overview of the relationship among the parameters in Figure 2 calculated as AM of these parameters for each level of SP in DESIGN category.
1st Level
DESIGN		2nd Level
DESIGN		3rd Level
DESIGN
AM of NV of DESIGN SP0.840.640.42
AM of ratio of STEM NV of SP vs. HASS NV of SP0.820.841.19
AM of STEM and HASS cumulative value of normalized SP0.600.390.24
Table
Table 13. The correlation rate between COMPO and DESIGN SP absolute values.
Table 13. The correlation rate between COMPO and DESIGN SP absolute values.
Group LevelOut of Range1st COMPO2nd COMPO3rd COMPOOut of RangeΣ
Out of range00%00%00%00%00%0
1st DESIGN00%1339%926%1030%00%32
2nd DESIGN220%1331%1944%1536%00%49
3rd DESIGN00%826%928%1033%00%27
Out of range00%112%111%00%00%2
Σ 2 35 38 35 0 110
Table
Table 14. The correlation rate between STILL and DESIGN SP absolute values.
Table 14. The correlation rate between STILL and DESIGN SP absolute values.
Group LevelOut of Range1st STILL2nd STILL3rd STILLOut of RangeΣ
Out of range00%00%00%00%00%0
1st DESIGN113%724%1639%826%00%32
2nd DESIGN110%1234%2039%1642%00%49
3rd DESIGN00%519%1745%518%00%27
Out of range00%228%00%00%00%2
Σ 2 26 53 29 0 110
Table
Table 15. The correlation rate between SPAT and DESIGN SP absolute values.
Table 15. The correlation rate between SPAT and DESIGN SP absolute values.
Group LevelOut of Range1st SPAT2nd SPAT3rd SPATOut of RangeΣ
Out of range00%00%00%00%00%0
1st DESIGN317%831%1233%926%00%32
2nd DESIGN732%1031%1840%1432%00%49
3rd DESIGN00%313%1030%1444%00%27
Out of range00%00%111%111%00%2
Σ 10 21 41 38 0 110
Table
Table 16. The correlation rate between ADMISS and DESIGN SP absolute values.
Table 16. The correlation rate between ADMISS and DESIGN SP absolute values.
Group LevelOut of Range1st ADMISS2nd ADMISS3rd ADMISSOut of RangeΣ
Out of range00%00%00%00%00%0
1st DESIGN524%419%1540%823%00%32
2nd DESIGN831%623%1839%1739%00%49
3rd DESIGN15%315%926%1443%00%27
Out of range00%119%111%00%00%2
Σ 14 14 43 39 0 110
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Hubinský, T.; Legény, J.; Špaček, R. STEM and HASS Disciplines in Architectural Education: Readiness of FAD-STU Bachelor Students for Practice. Educ. Sci. 2022, 12, 294. https://doi.org/10.3390/educsci12050294

AMA Style

Hubinský T, Legény J, Špaček R. STEM and HASS Disciplines in Architectural Education: Readiness of FAD-STU Bachelor Students for Practice. Education Sciences. 2022; 12(5):294. https://doi.org/10.3390/educsci12050294

Chicago/Turabian Style

Hubinský, Tomáš, Ján Legény, and Robert Špaček. 2022. "STEM and HASS Disciplines in Architectural Education: Readiness of FAD-STU Bachelor Students for Practice" Education Sciences 12, no. 5: 294. https://doi.org/10.3390/educsci12050294

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