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Architectural Design Process and its Impact on Studio Class Performance among Architecture Students of a Private University in Nigeria

  • Ejiga Opaluwa
  • Oluwagbenga Oso
  • Akintunde Onamade
  • Moyinoluwa Ola
  • Tolulope Falusi
  • Feranmi Ajana
  • Rasaki Ibrahim
  • 3910-3925
  • Sep 27, 2024
  • Agriculture

Architectural Design Process and its Impact on Studio Class Performance among Architecture Students of a Private University in Nigeria

Ejiga Opaluwa1, Oluwagbenga Oso2, Akintunde Onamade3*, Moyinoluwa Ola4, Tolulope Falusi5, Feranmi Ajana6, Rasaki Ibrahim7

1,2,4,5,6Department of Architecture, Afe Babalola University, Ado-Ekiti.

3Department of Architecture, Caleb University, Lagos.

*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2024.803283S

Received: 13 September 2024; Accepted: 18 September 2024; Published: 27 September 2024

ABSTRACT

Architecture, a field that addresses the organization of space and the resolution of spatial, aesthetic, and social challenges, involves a complex design process that bridges conceptual ideas and tangible realities. This study investigates the effects of understanding the architectural design process on architecture students’ performance in design studio classes. Conducted among students at the Department of Architecture, Afe Babalola University, Ado-Ekiti, the research utilized a historical research design to evaluate performance across three academic sessions. The study aimed to describe the architectural design process, assess students’ comprehension of the process, and examine how this understanding impacted their design studio performance. Data were collected from student records and analyzed using Microsoft Excel 2010 for trend analysis. Results indicate a positive correlation between students’ comprehension of the design process and their academic performance. Specifically, students who demonstrated greater participation in design studio activities showed incremental improvement in both their understanding of the architectural design process and their grades. The iterative nature of the design studio, where students repeatedly engage with the design process, fosters deeper understanding and enhanced performance through deliberate practice. The study concludes that reinforcing students’ understanding of the architectural design process throughout their education is essential for producing architects capable of solving real-world design challenges. It recommends that architecture programs emphasize the benefits of mastering the design process to equip future architects with the skills necessary for sustainable environmental planning and design.

Keywords: Architectural Design Process, Design Studio Class, Performance, Trend Analysis,

INTRODUCTION

Renowned architect Frank Gehry describes architecture as a discipline that, although it should aspire to timeless design, architecture should speak of its place and period (Gehry, 2020). Architecture is one of the most comprehensive fields of human activity, dealing with the organization of space and solving any spatial aesthetic and social problems (Maksymova, 2022). Architecture is the art and science of designing and constructing various buildings, structures, and complexes necessary for human life, diverse space organisation, and solving activities (Maksymova, 2022; Opaluwa et al., 2022). From the design ideas stage to the design reality stage, architecture can be considered a subject that presents several design challenges. The design process is how an architect’s concept is transferred from their thoughts to the canvas and subsequently given reality (Oluwatayo et al., 2017).

Architects must follow a structured design process, which differs for each person and organization, to solve design issues. The output and subsequent performances of students in their design studio are determined by the design process adopted(Asaju, Adewumi, Onamade & Alagbe., 2024). As a result, this study looks at the design process and how it affects students’ performance in design studio classes. When a new understanding, knowledge, or experience is gained, there is an effect and impact on practices and procedures. The architectural design process mentioned above must have been acquired, developed, and understood for something or someone to have an impact (Oluwatayo et al., 2017). The primary goal of the architecture programme in tertiary institutions is to prepare students for the conditions and challenges of practice and to acquaint them with actual circumstances involving design, building, and coordination.

A few key components in the study of architecture are taught in the classroom (Anindita et al., 2022). Courses taught in the architecture programme in Nigeria have been divided into 8 modules by the regulatory bodies – the National Universities Commission (NUC) and the Architects Registration Council of Nigeria (ARCON). These include Architectural Design, Communication, History, Theoretical, and Technical Studies Module courses (Okojie, 2014). Others are Arts and Humanities, Environmental Studies, Physical Sciences and Information Technology and Management, and Entrepreneurial and Professional Studies modules (Okojie, 2014). Architectural Design Studio is one course that embraces the knowledge gained from courses taught in the other 7 modules (Asaju et al., 2023). In essence, architectural design is an interactive course where students work together with their instructors and supervisors to solve a design problem by drawing on several subjects.

The contribution of architecture to society’s physical and socioeconomic progress is substantial and unquestionable. In addition to its primary role in boosting the visual appeal of the environment and the structural integrity of city structures, it also serves to advance the sense of national identity and pride in the society that created it. Students have developed their designs throughout time by using a variety of approaches. In some way or another, architecture students must have used a step-by-step process to address the difficulties they encountered when putting their thoughts and ideas on paper (Oluwatayo et al., 2017; Maksymova, 2022). The above description is often referred to as the architectural design process. It is possible to think of the architectural design process as a notion in architectural education that student architects attempt to adapt to approach designing studio projects to have a clear grasp of the project and improve their performances (Oluwatayo et al., 2017; Anindita et al., 2022).

Architecture students have benefited from this process in that when they create their final designs; learning must have taken place because whenever these students face design difficulties, they attempt to apply strategies they have used to overcome similar challenges in the past to come up with solutions (Oluwatayo et al., 2017; Anindita et al., 2022). Learning is typically understood as the process of converting information into knowledge and competencies, according to O’Dwyer (2022). When learning, people do not behave like AI robots. On the flip side, we selectively develop knowledge and competencies and assess their relative worth when presented with various informational and data sources. In a prior study, Oluwatayo et al. (2017), Akah et al. (2021), and O’Dwyer, (2022) noted that Architectural education is distinct from other disciplines by being one that is grounded on internship.

The future professional interning is modelled and modelled within the design studio’s walls. The design studio, which is at the centre of most industrial design curricula, is where students learn to graphically describe and visualise many parts of a problem and how to think like a designer. The students’ learning experiences could continue beyond the studio and have either a positive or negative effect on how well architecture students do in their design studio projects (Oluwatayo et al., 2017). The purpose of this study is to describe the architectural design process and performance in design studio projects. This study is crucial because it gives teachers and studio instructors pertinent information about the design process, how students’ performance is affected by their comprehension of it and the decisive ways they approach design problems.

Goals of the Study

The purpose of this study was to describe the architectural design process, evaluate how well architecture students understood and applied the method to create their designs and evaluate how well they performed in the design studio over three (3) academic years. At Afe Babalola University Ado-Ekiti in Ekiti State, Nigeria, the research only looked at architecture students who had completed levels 200 and above.

LITERATURE REVIEW

A few direct publications have focused on the impact of the architectural design studio process on students’ performance over the past ten years. However, research has been done on things like learning styles and student performance to other, closely related topics. The complexity of design issues in architecture has increased over time. The social demands of people, sustainable energy conservation, population, and economic growth, and environmental conditions are a few of the issues considered during architectural design (Adewumi et al.,2023). Since these issues have evolved, it would be crucial and extremely advantageous to educate young, bright, and inexperienced designers to approach designs utilising the design processes and support them as they face new obstacles and solve more complex issues (Dash, 2021).

Oluwatayo et al. (2017) and Holubchak (2020) concluded that the architectural design process is the scientific study of already existing ideas, thoughts, and thinking to arrive at detailed solutions for an architectural design from their studies on understanding concepts, creativity, and innovation and approach to the Architectural Design Process. It has been stated that the distinction between the scientific approach and architectural design process is that the former is concerned with how things are, whilst the latter focuses on how things should be done.

The Architectural Design Process Described

On the premise of the above statement, the architectural design process can be summarised as a sequential and cyclic process of iteratively analysing, synthesizing, and evaluating the information, insights, and potential solutions that are accessible until a successful fit between what is actual and what is desired is established (Ching & Binggeli, 2018).

Analysis

An analysis is a capacity to characterise and comprehend the nature of the design challenge accurately. Examining what is already there entails recording the physical and cultural background, describing the elements already present, and identifying what can and cannot be changed (Ching & Binggeli, 2018; Hettithanthri et al., 2022). By differentiating between the needs and preferences of users and establishing goals that address functional requirements, aesthetic image and style, psychological stimulus, and meaning, it goes on to define what is desired. At some point, it acknowledges what can and cannot be changed, controlled, or permitted and then investigates what is possible (Hettithanthri et al., 2022;Adeboyejo et al., 2022). These options are understood when technological, legal, economic, and time constraints are established.

Synthesis

Synthesis is logical reasoning that is built on information and comprehension learned through research and experience. Equal parts of intuition and imagination contribute the creative dimension to the synthesis process. Synthesising is the process of combining and integrating answers to numerous problems and challenges into a cohesive whole. By focusing on one or two main concerns that have a value or priority attributed to them and creating solutions around them, one can approach idea generation and possible solution synthesis for a problem (Ching & Binggeli, 2018; Hettithanthri et al., 2022).

Additionally, by researching comparable circumstances and using them as models to create potential answers to the current issue. Synthesis can also be accomplished by creating ideal solutions for specific aspects of the issue, which can subsequently be combined into complete answers and adjusted to account for the reality of what already exists (Ching & Binggeli, 2018; Hettithanthri et al., 2022). It is crucial to remember that coming up with an excellent idea is difficult without having several in the beginning.

Evaluation

In designing a building, evaluation entails a critical analysis of the possibilities and a comprehensive comparison of each proposal’s advantages and disadvantages (Ching & Binggeli, 2018; Hettithanthri et al., 2022). It is necessary to assess each potential solution in light of the standards outlined in the problem statement and made explicit in the problem analysis. The options for design development should be made more manageable by continued examination of the issue and evaluation of potential solutions (Ching & Binggeli, 2018; Hettithanthri et al., 2022). While the architectural design process’ analysis and synthesis stages promote divergent thinking, the evaluation step necessitates converging emphasis on a particular design solution. The design proposal is created, improved, and ready for implementation once a decision has been reached (Hettithanthri et al., 2022).

No design process is complete until the effectiveness of a design solution that has been put into practice is assessed. This critical evaluation broadens the pupils’ knowledge base, hones their instincts, and offers priceless insights applicable to subsequent works. The fact that there isn’t always a single, evident correct answer is one of the peculiarities of the architectural design process. In actuality, there are frequently several good answers to a design problem.

Architecture Students’ Performance and the Architectural Design Process

Students’ performance in the design studio is influenced by their understanding of the architectural design process (Asaju et al., 2024). In this area, some earlier researchers have conducted research. According to a study, student’s performance may be impacted by the limitations placed on a design challenge and their preferred learning methods (Enwerekowe & Dassah, 2021).

Concerning their learning preferences and the many restrictions imposed on design issues, they investigated the performance of undergraduate architectural students at the University of Jos in Nigeria. Their research discovered that design performance, as assessed by conceptual development, form and spatial configuration, structural innovation, ergonomics, and craftsmanship, changes with design settings defined by various forms of limitation  (Enwerekowe & Dassah, 2021).

Abdullah & Hassanpour (2020) investigated performance based on design while taking into account present studies and practices. The study’s objective was to examine the applicability of performance-based design in practices, given that its field was established through an analysis of firm practices and projects. The research project involved investigating a case study from an experimental digital design. Following the discovery that performance-based simulations for architectural design can incorporate both generating and evaluative capacities. In the field of architectural design education, Pirdavari & Ribeiro (2022) investigated cognitive types and student growth. This study looked at how students with specific cognitive types perform during specific phases of architectural education when working on design projects. It concerns how information is perceived and processed by students.

The indicator of successful learning is the student’s performance. The student’s grades frequently reflect this. The design jury is a significant method of evaluation for students studying architecture. Jury frequently occurs as a result of design studio being a major course the student is enrolled in and taking up the majority of their practice time (Oluwatayo et al., 2017).

METHODOLOGY

Both primary and secondary data were employed as the sources of information for this study. Researchers from Afe Babalola University in Ado-Ekiti, South-West Nigeria, collected the primary data by analysing data from Architectural Design Studio I, II, III, IV, and V (ARC 201, 202, 301, 401, and 402) grades over three (3) sessions of architecture students. The articles cited in this study’s theoretical framework served as the secondary data source. First and second-semester grades for the academic years 2020–21, 2021–22, and 2022–23 were the primary data collection. Meanwhile, the literature included as secondary data in this study covered the period of nine (9) years, from 2014 to 2023. This was done to guarantee the relevancy and currency of the information.

This study makes the supposition that a student’s comprehension of the architectural design process improves with increased participation in all activities in the architectural design studio class. This presumption is based on the idea that deliberate practice leads to perfection (Ericsson & Moxley, 2012).

Research Methods

The research design used in the study was historical. Since historical design allowed researchers to collect, verify, and synthesize information from the past (approved student results), facts supporting the idea that a greater comprehension of the architectural design process results in higher performance may be established (Author, 2001). The historical research design is ideal for this study due to its not disruptive nature, which means that the research process had no impact on the study’s findings (Author, 2001). The historical design was utilized since the records (approved student results from three sessions) provide crucial contextual knowledge that is required to comprehend and analyse the relationship between student performance and comprehending the architectural design process. It was also chosen as a methodology since there is no chance of subject or researcher interaction that could influence the results. The data set (primary and secondary) is frequently helpful in exploring different research problems related to cognitive learning and duplicating a prior study; therefore, the historical design was also appropriate for the research. However, the situation is different in this instance. Using Microsoft Excel (MS Excel) 2010 software, approved results for two levels (300, and 400) of architecture students in ARC 201, 202, 301, 401, and 402 spanning three sessions (2020/21 – 2022/23) were subjected to trend analysis.

Trend analysis

In other words, trend analysis is the process of studying statistical data and documenting current behaviour over a certain time to produce insightful subject matter using this data for future planning and strategy (“Trend Analysis,” 2014). An improvement in knowledge throughout the duration of a course can be seen by comparing quiz or exam scores using horizontal trends, or by seeing a pattern in data sets for a regularly conducted satisfaction survey. Fundamentally, trend analysis is a way to comprehend how and why things have changed through time, or why they will continue to change (“Trend Analysis,” 2014).  For the sake of clarity, it can be defined as a method of analysis that gathers information before attempting to identify patterns or trends in that information to comprehend or predict behaviour.

DATA PRESENTATION, ANALYSIS, AND DISCUSSIONS

Approved results and grades in architectural design studio courses from two hundred (200L) to four hundred levels (400L) were obtained from the departmental examination and records office. A total of forty-two (42) students’ grades, with nineteen (19) in 400L and twenty-three (23) in 300L as of the end of 2022-2023 academic sessions, were analysed. The result data collected for 400L students spanned three (3) sessions (2020/21 – 2022/23) and data gathered for 300L students encompassed two sessions (2021/22- 2022/23). In line with ethical standards, the names and matriculation numbers of the students were expunged from the data set and were simply referred to as candidates.

Table 1: Assessment criteria employed in the grading of Architectural Design Studio Class

Source: Authors 2023

ASSESSMENT CRITERIA TOTAL 100 Marks
Site Analysis & Case Study 10 Marks
Conceptual Development & Synthesis 20 Marks
Structural Stability & Design Development 15 Marks
Construction Details & Methods 20 Marks
Drawing Presentation & Graphics 10 Marks
Oral Presentation, Dressing & Composure 10 Marks
Models (Virtual and Physical) 15 Marks

It is important to note that the score obtained by individual students in a studio course is through a jury system. The jury grading system employed in the assessment of architectural design studio courses relies on averaging the score of at least three (3) critics per student. The critics are distributed in ranks from lecturer II to professor. An architectural design solution produced by a student is examined based on one hundred (100) maximum obtainable marks. Table 1 shows the assessment criteria and allotted marks according to the stages of the architectural design process.

Table 2: Scores and average score of student group one in five Architectural Design Studio courses between 20/21-22/23

Source: Authors 2023

COURSES GRADES PER CANDIDATE
  Candidate 1 Candidate 2 Candidate 3 Candidate 4 Candidate 5 Candidate 6 Candidate 7 Candidate 8 Candidate 9 Candidate 10 Candidate 11 Candidate 12 Candidate 13 Candidate 14 Candidate 15 Candidate 16 Average Score
SCORE ARC 201 – 20.21 67 60 53 65 60 71 50 48 52 53 70 65 65 51 48 45 58
SCORE ARC 202 – 20.21 67 60 53 65 60 71 50 48 52 53 70 65 65 51 48 45 58
SCORE ARC 301 – 21.22 66 68 63 65 50 73 60 54 71 57 64 57 75 65 70 64 64
SCORE ARC 401 – 22.23 61 62 46 63 47 60 51 45 62 45 52 45 64 56 61 67 55
SCORE ARC 402 – 22.23 62 60 60 62 60 65 60 58 65 60 57 55 64 62 50 75 61

Table 2 shows the results of nineteen (19) candidates in Architectural Design Studio I, II, III, IV, and V (ARC 201, 202, 301, 401, and 402) who were admitted into the institution in the 2019/2020 academic year. The average score of the 2019/20 class in the five studio courses within the time frame were 58, 58, 64, 55, and 61 respectively. As can be deduced from Table 2, the average score grew incrementally between the 2020/21 – 2021/22 academic sessions. A sudden dip was observed in the first semester of the 2022/23 academic year but later continued in an upward trend in the second semester of the same year. The dip experienced may have resulted from the break in the academic calendar necessitated by numerous public holidays declared by the government of the country as the period in question – October 2022 to March 2023 – was the season climax of the electioneering year in Nigeria. These average scores over the three (3) sessions support the earlier supposition that a student’s comprehension of the architectural design process improves with increased participation in all activities in the architectural design studio class, hence improving grades. This simply means that the trend is positive and the assumption is true.

Table 3 displays the trend analysis of the architectural design studio course of the sixteen (16) students admitted in the 2019/20 session through the duration of their undergraduate programme. It was observed that nine (9) of the sixteen (16) students had a positive trend and seven (7) of them had a negative trend. The outcome of the trend analysis with the positive trend being significantly more than the negative trend supports the previous hypothesis. Figures 1 to Figure 3 are line charts that graphically display the trend over time of the performance of ten (10) students. Three (3) of the line charts depict some with a positive outlook and two (2) represent some with a negative outlook.

Table 3: Trend forecast for student group one in five Architectural Design Studio courses

Source: Authors 2023

NAMES COURSES        
  Score ARC 201 – 20.21 Score ARC 202 – 20.21 Score ARC 301 – 21.22 Score ARC 401 – 22.23 Score ARC 402 – 22.23
Candidate 1 67 67 66 61 62
Trend Forecast 67.8 66.2 64.6 63 61.4
Candidate 2 60 60 68 62 60
Trend Forecast 61.6 61.8 62 62.2 62.4
Candidate 3 53 53 63 46 60
Trend Forecast 53.6 54.3 55 55.7 56.4
Candidate 4 65 65 65 63 62
Trend Forecast 65.6 64.8 64 63.2 62.4
Candidate 5 60 60 50 47 60
Trend Forecast 58 56.7 55.4 54.1 52.8
Candidate 6 71 71 73 60 65
Trend Forecast 72.6 70.3 68 65.7 63.4
Candidate 7 50 50 60 51 60
Trend Forecast 50 52.1 54.2 56.3 58.4
Candidate 8 48 48 54 45 58
Trend Forecast 47.2 48.9 50.6 52.3 54
Candidate 9 52 52 71 62 65
Trend Forecast 53.2 56.8 60.4 64 67.6
Candidate 10 53 53 57 45 60
Trend Forecast 52.4 53 53.6 54.2 54.8
Candidate 11 70 70 64 52 57
Trend Forecast 71.4 67 62.6 58.2 53.8
Candidate 12 65 65 57 45 55
Trend Forecast 65.4 61.4 57.4 53.4 49.4
Candidate 13 65 65 75 64 64
Trend Forecast 67.2 66.9 66.6 66.3 66
Candidate 14 51 51 65 56 62
Trend Forecast 51.6 54.3 57 59.7 62.4
Candidate 15 48 48 70 61 50
Trend Forecast 52 53.7 55.4 57.1 58.8
Candidate 16 45 45 64 67 75
Trend Forecast 42.8 51 59.2 67.4 75.6

The table 3 presents the performance of 16 candidates in various architecture courses over three academic sessions (ARC 201 and ARC 202 in 2020-2021, ARC 301 in 2021-2022, and ARC 401 and ARC 402 in 2022-2023), alongside their trend forecasts.

Performance Patterns:

One of the key observations is the consistency in performance seen in candidates like Candidate 1 and Candidate 4. These students maintain relatively stable scores across all courses, indicating a consistent understanding of the material. On the other hand, some candidates, such as Candidate 5 and Candidate 16, exhibit notable fluctuations in their performance. For instance, Candidate 5 experiences a decline in scores from ARC 201 to ARC 401 before improving in ARC 402, while Candidate 16 shows a dramatic improvement, particularly between ARC 301 and ARC 402. A second pattern is the distinction between improvement and decline across candidates. Candidates like Candidate 9 demonstrate steady improvement, especially in ARC 301 (71) and ARC 402 (65). In contrast, Candidate 11 starts with high scores in ARC 201 and ARC 202 (70) but experiences a gradual decline, culminating in a score of 57 in ARC 402.

The trend forecasts for each candidate offer a predictive model of future performance based on past scores. For example, Candidate 6 is forecasted to experience a decline from a high of 72.6 in ARC 201 to 63.4 in ARC 402, which aligns with their actual performance trajectory. Conversely, Candidate 16 outperforms their forecast, particularly in the later courses, signaling a trend of exceeding expectations. There are also instances of sharp fluctuations in performance. Candidate 5’s scores drop from 60 in ARC 201 and ARC 202 to 50 in ARC 301 and further to 47 in ARC 401, before recovering to 60 in ARC 402. This pattern suggests possible external factors affecting performance or varying levels of engagement with specific courses. Additionally, some candidates, such as Candidate 8, consistently underperform relative to their forecasts, indicating a gap between expected and actual outcomes. For example, in ARC 301, Candidate 8’s forecast was 50.6, but the actual score was 45.

Implications:

The consistent performance of candidates like Candidate 1 and Candidate 4 indicates that these students may have effective learning strategies and a strong understanding of the curriculum. Identifying the factors that contribute to their stability could provide valuable insights for enhancing the performance of other students. Also, Candidates with significant score fluctuations, such as Candidate 5 and Candidate 16, may benefit from academic support or mentorship to address their inconsistent performance. These variations could be due to external factors, personal challenges, or difficulties with particular courses, which may require tailored interventions. Trend forecasts offer a valuable tool for predicting potential declines or improvements in student performance. For candidates like Candidate 6, whose performance is forecasted to decline, proactive academic interventions could help mitigate this drop and provide the necessary support to sustain high performance.

Finally, students like Candidate 9 and Candidate 16, who show strong potential for growth, demonstrate that even students with early struggles can achieve success in later courses. These cases highlight the importance of providing ongoing support and encouragement, as significant improvement is possible with the right resources.

Similarly, the grades of Twenty-three (23) students in Architectural Design Studio I, II, and III (ARC 201, 202, and 301) who were accepted into the architectural programme during the 2020–2021 academic year are represented in Table 4.

Diagrammatic depiction using a line chart showing the trend analysis of some selected students in group one

(a) Line chart showing negative trend on candidates 1&4          (b) Line chart showing positive trend on candidates 2&3

Figure 1(a&b): Diagrammatic depiction using a line chart showing the trend analysis of some selected students in group one

Source: Authors 2023

The three studio classes within the period had average scores for the 2020/21 class of 65, 65, and 64, respectively.

(a) Line chart showing positive trend on candidates 9&10             (b) Line chart showing negative trend on candidates 12&1

Figure 2(a&b): Diagrammatic depiction using line chart showing the trend analysis (positive & negative) of some selected students in group one

Source: Authors 2023

Figure 3: Diagrammatic depiction using a line chart showing the positive trend analysis of two selected students in group one

Source: Authors 2023

Table 4: Scores, average score, and trend forecast of student group two in three Architectural Design Studio courses between 21/22-22/23

Source: Authors 2023

S/No NAMES SCORE ARC 201 – 21.22 SCORE ARC 202 – 21.22 SCORE ARC 301 – 22.23
1 Candidate 17 60 60 74
  Trend Forecast 57.7 64.7 71.7
2 Candidate 19 61 61 55
  Trend Forecast 62.0 59.0 56.0
3 Candidate 21 47 47 46
  Trend Forecast 47.2 46.7 46.2
4 Candidate 22 74 74 75
  Trend Forecast 73.8 74.3 74.8
5 Candidate 23 66 66 79
  Trend Forecast 63.8 70.3 76.8
6 Candidate 24 57 57 57
  Trend Forecast 57.0 57.0 57.0
7 Candidate 25 56 56 67
  Trend Forecast 54.2 59.7 65.2
8 Candidate 26 71 71 76
  Trend Forecast 70.2 72.7 75.2
9 Candidate 27 63 63 76
  Trend Forecast 60.8 67.3 73.8
10 Candidate 28 72 72 72
  Trend Forecast 72.0 72.0 72.0
11 Candidate 29 79 79 87
  Trend Forecast 77.7 81.7 85.7
12 Candidate 30 73 73 67
  Trend Forecast 74.0 71.0 68.0
13 Candidate 31 76 76 71
  Trend Forecast 76.8 74.3 71.8
14 Candidate 32 73 73 53
  Trend Forecast 76.3 66.3 56.3
15 Candidate 33 60 60 48
  Trend Forecast 62.0 56.0 50.0
16 Candidate 34 60 60 50
  Trend Forecast 61.7 56.7 51.7
17 Candidate 35 66 66 51
  Trend Forecast 68.5 61.0 53.5
18 Candidate 36 71 71 67
  Trend Forecast 71.7 69.7 67.7
19 Candidate 37 50 50 57
  Trend Forecast 48.8 52.3 55.8
20 Candidate 38 63 63 60
  Trend Forecast 63.5 62.0 60.5
21 Candidate 39 73 73 71
  Trend Forecast 73.3 72.3 71.3
22 Candidate 40 70 70 70
  Trend Forecast 70.0 70.0 70.0
23 Candidate 41 54 54 46
  Trend Forecast 55.3 51.3 47.3
  AVERAGE SCORE 65 65 64

Table 4 provides performance data for 23 candidates in ARC 201 and ARC 202 (2021-2022) and ARC 301 (2022-2023), along with trend forecasts for their expected scores. The interpretation of the data reveals several key trends and implications regarding the candidates’ academic performance over time.

Performance Trends:

A number of candidates, such as Candidate 24 and Candidate 40, demonstrate consistent performance across the three courses. For instance, Candidate 24 scored 57 in all three courses, which exactly matches the trend forecast. Similarly, Candidate 40’s performance remains stable at 70 across all courses, reflecting the accuracy of their forecasted scores. This suggests that these candidates have maintained a steady grasp of the curriculum, without significant fluctuations in their performance. Also, some candidates, such as Candidate 17 and Candidate 27, show marked improvement. For example, Candidate 17 increased their score from 60 in ARC 201 and ARC 202 to 74 in ARC 301, outperforming the forecasted scores. Candidate 27 follows a similar trend, improving from 63 in ARC 201 and ARC 202 to 76 in ARC 301, also exceeding the trend forecast. This demonstrates that some students are able to improve significantly as they progress through their studies, possibly due to increased engagement with the material or enhanced learning strategies. Whereas, a few candidates, including Candidate 32 and Candidate 33, show a noticeable decline in performance. Candidate 32’s score drops from 73 in ARC 201 and ARC 202 to 53 in ARC 301, which is lower than the forecasted decline. Candidate 33’s performance also falls from 60 in ARC 201 and ARC 202 to 48 in ARC 301, underperforming relative to the forecast. These declines might suggest difficulties in adapting to the increased complexity of the coursework or external factors affecting academic performance.

However, candidates like Candidate 29 and Candidate 23 outperformed their trend forecasts significantly. Candidate 29’s score increased from 79 in ARC 201 and ARC 202 to 87 in ARC 301, well above the forecast of 85.7. Similarly, Candidate 23’s score improved from 66 to 79, surpassing the forecast of 76.8. These results indicate that some students not only meet expectations but also surpass them, showcasing their potential for academic growth and success. While some candidates, such as Candidate 21 and Candidate 41, consistently underperformed. Candidate 21’s score remained low, hovering around 47, and fell slightly below the forecast. Candidate 41 also underperformed, scoring 46 in ARC 301, below both the actual and forecasted scores in previous years. This indicates that certain candidates may struggle with the material or face other challenges that prevent them from improving or meeting expected outcomes.

This implies that the overall average scores across the three courses (65 for ARC 201 and ARC 202, and 64 for ARC 301) suggest a slight decline in performance as the courses become more advanced. This could imply that students generally find ARC 301 more challenging, or that additional support may be needed to help them adapt to the increasing complexity of the coursework. For candidates who demonstrate consistent improvement, such as Candidate 17 and Candidate 27, the data suggests that these students are benefitting from their learning experiences and might be developing stronger problem-solving and analytical skills over time. These students may serve as examples of how targeted academic interventions and engagement with course materials can lead to significant academic growth.

On the other hand, students showing decline, such as Candidate 32 and Candidate 33, highlight the need for early identification of struggling students and timely interventions to prevent further academic decline. Mentorship, personalized tutoring, or additional resources may be required to help these candidates regain their academic footing.

Finally, students like Candidate 29 and Candidate 23, who exceed expectations, highlight the potential for high achievers to push beyond standard academic goals. These students may benefit from advanced learning opportunities or challenges to further nurture their abilities. The data reveals varied patterns of academic performance, from consistent scores and improvement to declines and underperformance. The trend forecasts provide useful insights into how students are expected to perform, and deviations from these forecasts can help educators identify students who may need additional support or further challenges. Overall, personalized academic interventions, coupled with ongoing monitoring of student progress, can help ensure that students maximize their potential throughout their architectural studies.

The three studio classes within the period had average scores for the 2020/21 class of 65, 65, and 64, respectively. The average score in the academic year 2021/22 is nearly constant, as can be seen in Table 4. A modest decline in the academic year 2022–2023 results was seen. The decline might have been brought on by the election-related incident already mentioned. The earlier assumption that a student’s understanding of the architectural design process improves with increasing engagement in all activities in the architectural design studio class, resulting in better results, is not supported by these average scores throughout the two (2) sessions. This merely indicates that the assumption is false and that the trend is downward. The trend analysis of the twenty-three (23) students admitted in the 2020/21 session who are enrolled in their undergraduate degree is shown in Table 4 for the architectural design studio course. Twenty-three (23) students were observed, with eleven (11) showing a positive trend and twelve (12) showing a negative trend. The results of the trend analysis do not support the earlier premise, with the negative trend being slightly greater than the positive trend. The line charts in Figure 4 to Figure 6 graphically depict the performance pattern of ten (10) students over time. Two (2) of the line charts show those with a positive trend, while three (3) show some with an unfavourable outlook.

(a) Line chart showing trend on candidate 19 in group two

Figure 4(a&b): Diagrammatic depiction using a line chart showing the negative trend analysis of two selected students in group two

Source: Authors 2023

(a) Line chart showing trend on candidate 29 in group two     (b) Line chart showing trend on candidate 37 in group two

Figure 5 (a&b): Diagrammatic depiction using a line chart showing the positive trend analysis of two selected students in group two

Source: Authors 2023

The data presented in this study came from two groups of students. The first having completed their first-tier training in architecture presented a positive trend as expected. The second still undergoing training within the first tier of the programme presented a negative trend contrary to the hypothesis. The negative trend experienced in the second group could have arisen from the fact that they are still in training. For this study to be truly representative and provide sound inductive reasoning using trend analysis, it would be best to have a rich heritage of data. Data that presents more than two sets that have completed their training programme in architecture. This study did not examine physical, emotional, and economic factors, which significantly impact students’ performance other than comprehension of the architectural design process. These factors can be responsible for the negative trend experienced in the analysis presented. On the strength of the above, it is reasonable to conclude that an increased understanding of the architectural design process has a positive impact on student’s performance in the design studio.

CONCLUSIONS AND RECOMMENDATIONS

The purpose of this study was to determine the relationship between student performance in the architectural design studio and their comprehension of the architectural design process. To evaluate the effects of ongoing involvement in all design studio activities and the significance of such basic training. The outcomes and the architectural design process were two elements that this study examined and took into account, to provide accurate and identifiable findings.

The analysis, synthesis, and evaluation phases of the architectural design process are discussed in the majority of Nigerian architecture schools. The learner repeats these steps throughout their design process, which results in a deeper understanding, perfection, and improved performance thanks to this technique of deliberate practice. Institutions must consequently strengthen the architectural apprenticeship programme in the architectural design studio class. This can be accomplished by improving the educational process and including teaching tools like field trips (both domestically and abroad), practical sessions, and a friendly learning atmosphere. To improve performance and help students become architects, schools and professional organisations can also improve the grading system to particularly address and measure steps involved in the architectural design process.

It is suggested that:

  1. To solve real-world design and planning problems for a sustainable environment, it is advised that architecture students be better informed of the advantages of the architectural design process.
  2. Schools of architecture should include the architectural design process and its results within their curriculum.
  3. Schools should provide tutoring for students to enhance and extend their grasp of the architectural design process.
  4. It is best to promote consistency in the evaluation of architectural design studios at all levels to address the stages of the design process.
  5. It is important to teach and encourage students to approach design challenges by thoroughly examining, synthesising, and assessing them to find the best possible solution.

REFERENCES

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