Content area
Purpose
An undergraduate civil engineering programme is assessed for its relevance to the building construction sector. Its contrast from the existing curriculum structure is also highlighted.
Design/methodology/approach
The curriculum is clustered into 15 groups based on disciplines. A pairwise comparison of groups is conducted by experts from the building construction sector. Expert judgements are analysed using Fuzzy Analytical Hierarchy Process (Fuzzy AHP) to determine the groups' order based on their importance in preparing students for a career in the building construction sector.
Findings
Concrete Technology, Structural Design and Analysis, and Building Technology and Town Planning emerged as the three most essential course groups, followed by Construction and Project Management, Surveying, and Geotechnical and Allied. Foundational Science and Math, Hydrology/Water Resource Engineering, and Computing and Programming came last in the order.
Research limitations/implications
Relying on a curriculum from a specific region, generalizability to other geographical areas is limited. The perspective of building construction sector professionals excludes the views of other stakeholder groups within the sector.
Practical implications
The study allows universities in general to enhance students' job prospects in construction by calibrating course group priorities and aligning skills with industry needs, thereby potentially improving employability, and boosting the industry-academia relationship.
Originality/value
Fuzzy AHP has been utilized by building construction industry experts to assess the relevance of an undergraduate civil engineering curriculum. Findings serve as a valuable reference for implementing positive curriculum changes to potentially enhance student employability.
1. Introduction
Engineering education equips individuals with the skills essential to address basic human needs, alleviate poverty, promote and secure sustainable development, respond to emergency situations, reconstruct infrastructure, and promote intercultural cooperation (UNESCO, 2023). Engineering education sector has emerged as a significant domain, attracting and empowering students globally. In India, since the establishment of the first engineering college in 1847, Thomason College of Civil Engineering (IIT-Roorkee, 2023), engineering education has expanded rapidly. The sector shoulders the responsibility of providing a skilled workforce to industries like construction, manufacturing, science, IT, etc. Premier institutes like the Indian Institute of Technology (IIT) consistently rank among the top engineering institutions globally (QS, 2023). In the academic year 2022–2023, 5,873 approved engineering institutions reported an overall intake of 2,374,205 candidates, more than 50% of whom were enrolled in engineering undergraduate courses (AICTE, 2023).
The engineering programme, like all programmes, needs to make sure that its structure is adaptable to student and market demands (Shahid et al., 2022). Despite its prominence, the sector faces several challenges. An employability report revealed that approximately 50% of national students across various prominent disciplines were not competent enough to be employable in the industry (Wheebox, 2023). The employability of engineering students in 2023, according to the same report, stood at 57.44%. In the same year, while the employability of engineering programmes like Information Technology, Computer Science, Electronics and Communication Engineering exceeded 60%, that of Civil Engineering stood at a mere 34%. This disparity is noteworthy, particularly given that the construction sector, which employs a significant portion of civil engineers, contributes about 9% to the national Gross Domestic Product (GDP) (Kumar, 2023). The real estate sector (housing, retail, hospitality, and commercial) is expected to contribute 13% to the national GDP by 2025 (IBEF, 2023). Real estate players invest in centralized processes to source organized manpower and hire qualified professionals in areas like project management, architecture, and engineering (IBEF, 2023). Institutes with Civil Engineering departments across the Middle East and the United States also find themselves recording enrolments that have decreased by 60% over the last five years (Yehia et al., 2023). Researcher Sander Valstar (San Diego, California) confirmed this industry preparation gap from personal experience as he found writing software (CS Sector) in the industry to be highly different from what he had learned in university (Valstar, 2019).
Revision of the present curriculum to make the students more equipped for industries will improve the employability of budding engineers internationally (Mishra and Sethi, 2019). In this context, it is imperative that the curriculum in engineering institutions aligns with industry needs and bridges any gap between skills imparted by institutions and those required by the industry. A robust collaboration between industry and academia could help graduates possess these necessary skills. It is, therefore deemed important to assess if the existing curriculum is relevant to the needs of the respective industrial sector.
This paper aims to assess the curriculum of an undergraduate civil engineering programme in alignment with the needs of the building construction sector. The Bachelor of Engineering (B.E.) (Civil) programme at Savitribai Phule Pune University, India (Formerly the University of Pune) is a 4-year programme aimed at preparing students for various sub-sectors of the construction industry (SPPU, 2023). Authors, with the support from credible professionals, have clustered the courses in this curriculum into 15 groups based on shared disciplines. Fuzzy Analytical Hierarchy process (FAHP) is utilized to perform pairwise comparisons of these groups. Respective weights of the course groups are derived using expert inputs, and the groups are ranked according to their importance in preparing candidates/students for long-term careers in the building construction sector. The order is also compared to the one resulting from the academic credit allocation of the groups within the curriculum to ascertain any significant disparity between the two.
1.1 Research questions
To address the issue of curriculum alignment with industry within the context, this research seeks to answer the following key questions:
The study aims to support the alignment of academia with industry needs and encourage collaboration between the two sectors. By addressing identified gaps, universities could enhance the relevance of their curriculum, ultimately improving graduates' employability and contributing to economic growth in both academia and the industry.
2. Literature review
2.1 Engineering education
Among the 5,873 approved engineering institutions in India, 662 are in Maharashtra (AICTE, 2023). Additionally, a total of 234 institutions and 705 colleges are affiliated with Savitribai Phule Pune University (SPPU, 2023). A study reported that the primary priority of students admitted to first-year engineering is their aspiration to secure a good job, followed by the ambition of gaining admission to higher studies (Pramod et al., 2021). A few students dream of becoming entrepreneurs, while very few aspire to explore careers in civil services (Pramod et al., 2021). The government is indeed focusing on engineering and manufacturing investment and innovation, recognizing it as one of the most rapidly expanding and significant industries (Wheebox, 2023). According to the India Skills Report 2023, Maharashtra boasts the highest concentration of highly employable candidates in the undergraduate engineering domain, with 69.03% responding students scoring above 60% on the Wheebox National Employability Test (Wheebox, 2023). Where disciplines such as Computer Science, Information Technology, and Mechatronics have shown a trend of increasing student enrolment over the last few years, traditional disciplines including Civil, Mechanical, and Electrical have experienced a decline over the same period, with enrolment in 2018 reaching only around 40% (Reddy, 2017). As mentioned earlier, it is noteworthy that only 34.41% of the civil engineering students are employable (Wheebox, 2023). The impact of lesser student enrolment and minimal student employability is surely likely to amplify and impact the effective industry requirement fulfilment inversely.
It is certain that the student group should be educated and trained within their education journey with the support of credible institutions and competent teachers. Engineering institutes typically recruit faculty for teaching and conducting research (Irfan et al., 2018). Consequently, the number of faculty members with industry experience is declining. With a significant emphasis on research over education, innovative teaching and learning practices become unsustainable and fail to adequately prepare students for industry-ready professional skills (Irfan et al., 2018). On the other hand, the shortage of skilled manpower in the construction industry has now become one of the growing concerns, especially for project stakeholders. Beyond the skilled labour workforce, there is a higher demand for qualified and skilled construction professionals (such as entry-level civil engineers and project managers) than there is available supply. The challenge of finding workers with the appropriate skills to match the rapidly growing number of jobs in the sector remains an emerging concern. At a time when the construction industry is experiencing substantial growth, this acute shortage is adversely affecting project performance and delivery (PMI and KPMG, 2019).
2.2 Curriculum analysis
A significant challenge in engineering education is that of bringing the largely separate worlds of engineering education and engineering practice closer together (Buckley et al., 2022). A professional education programme like civil engineering should be integrated with the civil engineering industry, interacting with industry alongside society without any exception. Moreover, a longitudinal culture of developing meaningful industry-institute relations within the context of professional education must be promoted (Chakrabarti, 2016). A study was conducted to assess graduate attributes across six different institutes with civil engineering programmes involved inputs from both academicians and industry professionals. It yielded reporting suggestions such as revising syllabi to align with industrial needs, incorporating compulsory internships and industry exposure, hosting guest lectures by industry professionals, modifying evaluations to include industrial knowledge, and providing industrial training for faculty members (Beena and Suresh, 2022). The Civil Engineering Education system must incorporate quality improvement initiatives to prepare both the students and teachers for local as well as global employment (Sunita and Gupta, 2021).
Continuous evolution of university education in civil engineering is essential to ensure students' readiness for an evolving industry (Turnbull, 2019). Graduates working in the industry without the requisite expertise, qualifications or experience may prove catastrophic for society. In 2014, an 11-storey apartment building collapsed while still under construction in the suburb of Chennai, Tamil Nadu, India (Radhakrishnan et al., 2017). A lightning strike to the building and to the ground via reinforcement bars was believed and reported to be the initial cause of the failure of support columns and subsequent collapse of the structure. It was later assessed that proper construction procedures were not followed for such high-rise building. Materials did not meet the required quality standards, recommended soil testing procedures were neglected, and the quality of the concrete mix was subpar. Most importantly, the site engineers and supervisors lacked the necessary qualifications for such construction projects (Radhakrishnan et al., 2017). The study also recommended strict measures against individuals lacking the requisite qualifications and experience venturing into the construction business. This underscores the critical role of the civil engineering curriculum and the significance of its effective, practical, and professional delivery by institutions. Furthermore, training to become a professional civil engineer is not solely the responsibility of undergraduate institutions. Cultivating diligent and curious students at the undergraduate level necessitates fostering an early interest in the diverse subject options at the senior secondary school level (Sunita and Gupta, 2021).
A perspective plan for the development of civil engineering education should be an ongoing endeavour. It is recommended that an industry body or a consortium of industry bodies, and education-focused consultants should be included intensively in periodic curriculum planning exercises undertaken by institutional bodies, possibly every two years (Sunita and Gupta, 2021).
2.3 Fuzzy analytical hierarchy process
Analytic Hierarchy Process (AHP) is the theory of prioritization that derives relative scales of absolute numbers known as priorities from judgements expressed numerically on an absolute fundamental scale (Saaty, 2005). Using AHP, measurements of element properties on a standard scale can be converted to a relative scale measurement subjected to normalization (Saaty, 1990). Therefore, upon the adoption of AHP, it becomes possible to capture and quantify subjective judgements by pairwise comparisons of the elements, resulting in a priority list based on mutual importance.
Fuzzy AHP readily extends to situations where multiple experts are involved in the ranking process, and the expert judgements could be uncertain (Buckley, 1985). Fuzzy numbers are generally utilized to capture the vagueness of the judgements of the experts involved (Buckley, 1985). These numbers can be converted into triangular numbers to use Fuzzy AHP using pairwise comparison matrices (van Laarhoven and Pedrycz, 1983). The lack of certainty in expert decisions is thus interpreted into fuzzy numbers. For example, if an expert compares two elements, X and Y, for their relative importance and believes that X is more important than Y, they may express the same in different scale such that X is approximately twice as important as Y (Buckley, 1985).
Subsequently, the geometric mean procedure is applied to a fuzzy comparison matrix to obtain the fuzzy weights, which are then combined to calculate the final fuzzy weights for the alternatives. This process provides an order of the elements based on their mutual weights (importance for this study) (Buckley, 1985).
3. Methodology
3.1 Method
A quantitative approach is employed, wherein the courses, both mandatory and elective, are classified into 15 homogeneous groups based on shared disciplines. The B.E. (Civil) programme at Savitribai Phule Pune University was selected due to its potential to produce a significant number of local building construction professionals, who can provide most responses through physical visits.
The fuzzy Analytical Hierarchy Process (FAHP) has been utilized to (1) rate groups against each other, (2) fuzzify the pairwise comparison matrix, (3) calculate fuzzy weights, (4) apply defuzzification, and (5) normalize weights to rank groups in the building construction sector.
The research design adopted for this study follows the following steps:
Selection of the B.E. Civil Engineering programme at Savitribai Phule Pune University.
Clustering courses into 15 homogeneous groups based on common disciplines.
Involving senior building site/project executives as industry experts (excluding specialists).
Conducting pairwise comparisons by industry experts to assess the importance of course groups.
Establishing fuzzy pairwise comparison matrices using Triangular Fuzzy Number.
Computing fuzzy weights of groups using the geometric mean method proposed by Buckley.
Employing the Center of Area method for defuzzification and normalization of weights.
Collating and analysing normalized weights.
Presenting group ranks based on weights and making recommendations.
3.2 Sample
Similar studies are referred to, and it is found that the number of experts ranges anywhere from 5 to 34 (Bekesiene et al., 2021; Jiang and Liu, 2021; Mehta et al., 2019; Prascevic and Prascevic, 2017; Sharma et al., 2022). Consequently, 15 senior site/project professionals, primarily with extensive experience in overall project management of building construction projects, were selected. While most of the experts hailed from the Pune region, support for course assessment was also sought from experts in other geographical locations (states). All selected experts had either currently or previously held positions within reputable construction organizations, overseeing large-scale building construction projects with experience spanning from 7 to 26 years. Ethical guidelines of the University Institutional Ethics Committee were followed before and during the data collection process and the participants were informed clearly, and in writing, about the objectives of the study, process of data collection, and method of data processing to generate the result of their inputs. Only upon agreeing, were they required to enter their personal details (name, affiliation, experience, domain, etc.) before proceeding for data collection. Finding an opportunity to provide industry inputs for the scope of academic betterment, all participants readily agreed to support in data collection process.
Simple random sampling was primarily utilized in the selection process. The population from which the sample was drawn comprised professionals from the building construction sector. It included professionals working in various capacities, such as Project Manager, Assistant General Manager, Senior Vice President, etc. Most of the experts were approached through random physical visits to projects/offices while others were connected through LinkedIn, and trusted references. Industry professionals specializing in specific sub-domains of the building construction sector (quality, cost, billing, safety, contract, risk, procurement, etc.) were deliberately excluded. This exclusion was aimed to ensure a comprehensive assessment of course groups, encompassing the relative importance of each domain/discipline necessary for overall project success, and to mitigate the possibility of any education/experience-induced bias towards or against any discipline.
3.3 Data analysis
3.3.1 Civil engineering curriculum
The undergraduate civil engineering programme at Savitribai Phule Pune University (Formerly the University of Pune), Pune, India requires a student to earn a total of 170 credits. There are 112 courses, including mandatory and elective courses, in the 4-year (8-semester) programme (SPPU, 2023).
Table 1 shows the distribution of courses.
3.3.2 Course categorisation
112 courses are categorized into clusters (Saaty, 1990) sharing a common discipline. Though it is advised that the number of the clusters in such approach not exceed 9 (Saaty, 1990), it was necessary to include all the courses in the assessment to present a comprehensive judgement of the curriculum in its true entirety. Consequently, Table 2 shows group contents of 15 comprehensive clusters/groups describing all the courses that fall under the respective group. In addition to the expertise of the authors, the groups were formed after taking inputs and validation by an Assistant Professor (Civil Engineering) from a prominent private university, a Head of Department (Civil Engineering) from a Unitary Public University, and an Industry Professional from the Public Works Department.
3.3.3 Fuzzy analytical hierarchy process
The Analytical Hierarchy Process requires a comparison of clusters/groups in a pairwise manner, comparing the mutual importance within one context (Saaty, 1990). In this study, 105 such pairs are judged by each expert to exhaust all the possible pairwise combinations. Fuzzy AHP is adopted to extend the benefits of conventional AHP by introducing fuzzy logic to handle the potential fluctuations in subjective judgements that may have possessed any uncertainty or imprecision.
The following steps are adopted while using Fuzzy AHP for this study.
Step 1: Selecting a Rating Scale
Saaty Scale ranging from 1 to 9 is selected (Saaty, 1990). Judgment definitions and the associated crisp and fuzzy values are taken for pairwise comparisons (Felisoni et al., 2022) as present in Table 3.
Step 2: Construction of pairwise rating form.
Experts were required to rate the groups in a pairwise manner by assessing their mutua importance in preparing a student for a long-term career in the building construction sector.
Step 3: Weights calculation using the Geometric Mean Method in Fuzzy AHP (Buckley, 1985).
Pairwise comparison of 15 groups results in a 15*15 pairwise decision matrix.
Here, a1,2 denotes the importance of Group 1 when compared to Group 2.
Step 4: The fuzzy pair-wise comparison matrix [] is constructed by collation of pair-by-pair fuzzy scores as follows (Lin, 2010; Wang and Chin, 2011):
Sample of a comparison matrix formed between two of the elements/groups is presented in the analysis section.
Step 5: For each group, fuzzy geometric mean values are calculated.
Matrix [] is aggregated by fuzzy geometric mean using the expression (Buckley, 1985):(1)
Step 6: Calculation of Fuzzy Weights
Fuzzy weights are computed by multiplying each fuzzy geometric mean by a vector summation as follows (Wang and Chin, 2011):
Step 7: The fuzzy weights now must be defuzzified by the Center Of Area (COA) method as follows (Kwong and Bai, 2003):
Step 8: This step is followed by normalizing net defuzzified weights using the expression:
This resulted in the final weights of each group as per individual experts.
Step 9: Average weights and group ranking
Normalized weights of groups, as calculated through responses of individual experts, are collated, and averaged out to arrive at the final weights. The same are then ranked with the highest weight, ranked 1st, and so on. This provides an order of importance of groups.
4. Analysis
The hierarchy model for the study is presented in Figure 1. The developed model has two levels. The first level comprises the goal of the study, and the second is the groups identified/formed.
Expert opinions in comparing pairwise groups for mutual importance resulted in formation of pairwise comparison matrices. Sample pairwise comparison matrix generated through responses of an expert (E1) is shown below.
These values are then fuzzified using the fuzzy judgement scale (Table 3), which results in the fuzzy pairwise comparison matrix as indicated below.
Table 4 (below) depicts the fuzzy geometric mean calculated using (1)
Fuzzy weights for each group are calculated, which are defuzzified using the Center of Area method, (2). The new weights are then normalized to arrive at the final normalized weights. These weights, when sorted in descending order of their value, provide the importance ranks to the course groups (Table 5).
Similarly, the normalized weights of groups are calculated using the responses of all the experts separately and are then collated to arrive at the final mean defuzzified normalized weights of the groups.
5. Results
The mean defuzzified normalized weights from all experts were utilized to determine the overall weightage of each group. These weights signify the relative importance of the discipline-based groups in preparing students for careers in the building construction sector. Figure 2 illustrates the resulting order of the course groups based on their relative importance which is calculated as percentage of mean normalised weights of each group. This order can be interpreted as the ranking of the course groups, aiding the university in the curriculum redesign process. Figure 2 serves as a representation of the industry’s perspective.
In the collective perspective of the experts, three course groups emerge as paramount for long-term career preparation in the building construction sector: Concrete Technology (11.9% weight), Structural Design and Analysis (11.8% weight), and Building Technology and Town Planning (11.1% weight). Following closely, are Construction and Project Management (10.2% weight), Surveying (9.8% weight), and Geotechnical and Allied (9.7% weight). Together, these six disciplines account for approximately 65% of the total weight, underscoring their significance to the sector.
It is noteworthy that some experts identified the Experiential Learning group (5.3% weight) as the most crucial. Courses within this group focus on acquiring knowledge and skills through practical means such as internships and cross-disciplinary projects. However, it ranked lower in the final assessment due to other experts emphasizing on the importance of other groups.
Conversely, the curriculum’s assigned credit weight to the groups indicates the amount and relative weight of options available to students to delve within such disciplines. The weights of the same groups, when examined from an academic perspective, can also be compared with the weights resulting from Industry/Expert inputs. Figure 3 illustrates this comparison-the industry’s perspective compared with the curriculum group credit weight, encompassing both mandatory and elective courses.
6. Discussion of the results
The findings of this study underscore the critical importance of emphasizing specific courses within civil engineering curricula to ensure that graduates possess the necessary skills and knowledge demanded by the industry. Industry experts overwhelmingly emphasize the significance of comprehensive knowledge in Concrete Technology, encompassing mix designs, testing procedures, procurement practices, and handling techniques. Similarly, they underscore the essential role of a Building Technology and Town Planning discipline in providing students with foundational understanding of structural properties and general construction practices before delving into specialized areas.
While Construction and Project Management area is recognized as crucial for holistic project understanding and execution, it was ranked slightly lower compared to other course groups, primarily due to the necessity of prior considerable hands-on experience. Surveying Techniques and Geodetic Principles are deemed vital in the initial project stages, while expertise in Geology, Soil mechanics, and Foundation Engineering is indispensable for tackling excavation and sub-structure challenges effectively.
Although certain course groups received lower rankings in this study, it is imperative to acknowledge their importance within the curriculum. The focus of this research being specifically on the building construction sector may have impacted the rankings of such groups (Computing and Programming, Hydrology/Water Resource Engineering, and Foundational Science and Math) which nonetheless hold significant relevance in civil engineering education.
Comparing the industry-assigned importance of course groups with their curriculum-assigned credit weightage reveals notable disparities. For instance, Concrete Technology, which is ranked highest by industry experts, recorded second lowest credit allocation in the curriculum. This observation does not necessarily imply a disparity between course group importance and curriculum credit allocation, as the latter may already encompass necessary and comprehensive knowledge and skills delivered through effective pedagogical methods regardless of the credit allocation.
Studies suggest that the use of hands-on application, collaboration, teacher guidance, and real-world analysis of problems (data collection, analysis, and suggestion) increases students' understanding and confidence in the application of concepts (Mishra and Sethi, 2019). Activity-based Learning (ABL) using Problem-Based Learning (PBL) is one such documented approach reported to have enhanced the teaching-learning process within engineering education (Katageri and Raikar, 2022). Additionally, innovative curriculum redesign strategies, such as the model adopted by the University of Nottingham’s Department of Civil Engineering, focusing on core technical knowledge in initial years and specialized optional knowledge in later years, offer valuable insights for enhancing curriculum effectiveness (Turnbull, 2019).
Considering these findings, it is recommended to review and reinforce teaching methodologies especially within the course groups exhibiting significant disparities between curriculum credit allocation and industry importance. Such efforts are crucial for ensuring that civil engineering graduates are adequately prepared to meet the evolving demands of the industry and contribute effectively to societal development.
7. Limitations of present study and suggestions for future research
The aim of this study is to evaluate the significance of a curriculum for undergraduate students in civil engineering specific to a particular geographical region (Pune, India). Consequently, the findings of this study may be applicable in India, but their universality may be limited. Variations in specific educational practices and diverse curricula across different universities and educational programmes also restrict the generalizability of the results. Additionally, this study specifically focuses on assessing the civil engineering curriculum primarily from the viewpoints of professionals in the building construction sector, excluding perspectives from other stakeholders in the industry, such as those from the infrastructure and industrial sectors, as well as those belonging to sub-domains such as quality, safety, procurement, sustainability, etc. While being purposeful, this selective focus limits the assessment’s comprehensiveness and may not fully capture a holistic evaluation of the subject matter by incorporating diverse perspectives.
Future research in this area could involve incorporating multiple curricula from different geographical regions and other educational programmes within or beyond engineering, while also gathering inputs from a wider/different array of stakeholders within the respective industry. The findings of such a study could be more effectively generalized, benefiting a broader academic network. Additionally, the curriculum has been assessed based on its content and not by the relevant personal skills and attributes required by the industry, such as Computational Thinking, Social Intelligence, Design Mindset, etc. (Siti Rashidah et al., 2019). A study focusing on curriculum assessing the curriculum for presence and level of imparting such attributes could provide a new approach to curriculum design.
8. Conclusion
The study presents an assessment of an undergraduate civil engineering curriculum by distributing its courses into 15 homogeneous groups based on shared disciplines. Experts from the building construction sector provided inputs on the relative importance of these groups, which were then analysed using the Fuzzy Analytical Hierarchy Process to determine an order of the groups based on their significance in preparing students for long-term careers in the building construction sector. The findings reveal that Concrete Technology, Structural Design and Analysis, and Building Technology and Town Planning are the three most crucial areas, followed by Construction and Project Management, Surveying, and Geotechnical and Allied disciplines. The study suggests special attention to comprehensive curriculum development and delivery for teaching courses in these high-importance areas. Additionally, it recommends that groups ranking lower should not be removed from the curriculum but could either be provided as optional sub-streams or assigned relatively lesser curriculum weightage.
This study serves as a valuable reference for academia to align its structure with industry needs and fosters collaboration between educational institutions and the industry. By addressing identified gaps, universities can enhance curriculum relevance, improve graduates' employability in the building construction sector, and contribute to reduced unemployment rates and economic growth in both sectors. It emphasizes the importance of adapting educational programmes to industry demands ensuring graduates preparedness for the workforce, ultimately benefiting both academia and the industry.
Figure 1
Hierarchy model
[Figure omitted. See PDF]
Figure 2
Final combined weights of groups in descending order
[Figure omitted. See PDF]
Figure 3
Contrast between expert response analysis and curriculum group credit weight
[Figure omitted. See PDF]
Table 1
Curriculum structure (Savitribai Phule Pune University)
| Year | Semester | Total courses | Total credits | Mandatory courses | Elective courses | ||
|---|---|---|---|---|---|---|---|
| Number | Credits | Number | Credits | ||||
| 1 | 1 | 7 | 22 | 7 | 22 | – | – |
| 2 | 8 | 22 | 8 | 22 | – | – | |
| 2 | 3 | 13 | 22 | 10 | 22 | 3 | – |
| 4 | 10 | 22 | 10 | 22 | – | – | |
| 3 | 5 | 17 | 21 | 8 | 17 | 9 | 19 |
| 6 | 16 | 21 | 7 | 17 | 9 | 19 | |
| 4 | 7 | 21 | 20 | 5 | 8 | 16 | 39 |
| 8 | 20 | 20 | 7 | 13 | 13 | 37 | |
Source(s): Compiled by authors
Table 2
Curriculum categorisation in groups
| Group no. | Group name | Group content |
|---|---|---|
| G1 | Building Technology and Town Planning | Civil Engineering Practices, Building Technology, Architectural and Town Planning, and Formwork and Plumbing engineering |
| G2 | Computing and Programming | Python programming, Soft computing techniques including Neural Networks, Data Analytics with Machine Learning, and Statistical analysis |
| G3 | Concrete Technology | Concrete properties, Testing, Mix Design, Equipment, Admixtures |
| G4 | Construction and Project Mgmt | Project and Construction Mgmt, Financial Mgmt, MIS, Industrial Safety Auditing, Quantity Surveying, Contracts and Tenders, and Total Quality Mgmt |
| G5 | Engineering Fundamentals | Basic Electrical, Electronics, Mechanical Engineering, Workshop, Engineering Graphics with CAD, Fluid mechanics, Flow dynamics, Hydraulic machines |
| G6 | Environmental Engineering | Water supply engineering, Wastewater engineering, and Rural water supply and sanitation |
| G7 | Environmental Sciences | Conservation, pollution control, sustainable energy systems, solid waste management, air pollution control, Green structures and Smart cities |
| G8 | Foundational Science and Math | Engineering mathematics, physics, chemistry, engineering mechanics, advanced mathematics, research methodology and intellectual property rights, and operation research |
| G9 | Geotechnical and Allied | Geology and Geotechnical engineering, Soil and Rock mechanics, Coastal Engineering, Foundation engineering, and geosynthetics for pavements, retaining walls, and ground improvement |
| G10 | Personal Development | Physical education, Professional ethics, Leadership, Communication Etiquette, Stress Management through Yoga, Human rights, Social responsibility |
| G11 | Experiential Learning | Internship, Project-based learning in Interdisciplinary contexts, Reporting on Seminars, Technical Writing, Research and Publication in civil engineering |
| G12 | Structural Design and Analysis | Analysis, design, and mechanics of steel, R.C., prestressed concrete, precast and composite structures, foundations, bridges, Seismic analysis and design, Matrix analysis, Finite element method, and Structural Audit for retrofitting |
| G13 | Surveying | Surveying and geodetic techniques, advanced surveying with geospatial technologies, Remote sensing, and GIS for geospatial data analysis mapping |
| G14 | Transportation Engineering | Road safety management, airport and bridge engineering, Highway engineering, Transportation planning, Technology, Traffic systems, Pavements |
| G15 | Hydrology/Water Resource Engineering | Integrated water resources planning, Dams and hydraulic structures, Hydropower engineering, Irrigation and Drainage systems in Agriculture |
Source(s): Compiled by authors
Table 3
Rating scale adopted for the study
| Judgment definition | Saaty’s crisp values | Triangular fuzzified Saaty’s values | Triangular fuzzified Saaty’s reciprocal values |
|---|---|---|---|
| Equally important | 1 | 1, 1, 2 | 1/2, 1, 1 |
| Moderate importance | 3 | 2, 3, 4 | 1/4, 1/3, 1/2 |
| Strong importance | 5 | 4, 5, 6 | 1/6, 1/5, 1/4 |
| Very strong importance | 7 | 6, 7, 8 | 1/8, 1/7, 1/6 |
| Absolute importance | 9 | 8, 9, 9 | 1/9, 1/9, 1/8 |
| Intermittent values | 2, 4, 6, 8 | (x−1), (x), (x+1) x = 2, 4, 6, 8 | 1/(x+1), 1/x, 1/(x−1) x = 2, 4, 6, 8 |
Source(s): Felisoni et al. (2022) and Saaty (1990)
Table 4
Fuzzy geometric mean of a sample expert response
| Groups | l | m | u |
|---|---|---|---|
| Group 01 | 3.139 | 3.785 | 4.966 |
| Group 02 | 0.228 | 0.246 | 0.334 |
| Group 03 | 1.925 | 2.293 | 3.250 |
| Group 04 | 1.431 | 1.652 | 2.316 |
| Group 05 | 0.445 | 0.508 | 0.655 |
| Group 06 | 0.854 | 1.058 | 1.382 |
| Group 07 | 1.404 | 1.787 | 2.236 |
| Group 08 | 0.395 | 0.444 | 0.586 |
| Group 09 | 2.055 | 2.438 | 3.069 |
| Group 10 | 0.256 | 0.306 | 0.391 |
| Group 11 | 0.231 | 0.287 | 0.370 |
| Group 12 | 2.630 | 3.344 | 3.911 |
| Group 13 | 1.487 | 2.001 | 2.417 |
| Group 14 | 0.354 | 0.496 | 0.613 |
| Group 15 | 0.673 | 0.935 | 1.127 |
| Total | 17.507 | 21.581 | 27.623 |
| Inverse | 0.057 | 0.046 | 0.036 |
| Increasing order | 0.036 | 0.046 | 0.057 |
Source(s): Compiled by authors
Table 5
Fuzzy and normalised group weights and ranking
| Group name | # | l | m | u | Average | Normalized | Rank |
|---|---|---|---|---|---|---|---|
| Building technology and town planning | G1 | 0.114 | 0.175 | 0.284 | 0.191 | 0.178 | 1 |
| Computing and programming | G2 | 0.008 | 0.011 | 0.019 | 0.013 | 0.012 | 15 |
| Concrete technology | G3 | 0.070 | 0.106 | 0.186 | 0.121 | 0.113 | 4 |
| Construction and project mgmt | G4 | 0.052 | 0.077 | 0.132 | 0.087 | 0.081 | 7 |
| Engineering fundamentals | G5 | 0.016 | 0.024 | 0.037 | 0.026 | 0.024 | 10 |
| Environmental engineering | G6 | 0.031 | 0.049 | 0.079 | 0.053 | 0.049 | 8 |
| Environmental sciences | G7 | 0.051 | 0.083 | 0.128 | 0.087 | 0.081 | 6 |
| Foundational science and math | G8 | 0.014 | 0.021 | 0.033 | 0.023 | 0.021 | 12 |
| Geotechnical and allied | G9 | 0.074 | 0.113 | 0.175 | 0.121 | 0.113 | 3 |
| Personal development | G10 | 0.009 | 0.014 | 0.022 | 0.015 | 0.014 | 13 |
| Experiential learning | G11 | 0.008 | 0.013 | 0.021 | 0.014 | 0.013 | 14 |
| Structural design and analysis | G12 | 0.095 | 0.155 | 0.223 | 0.158 | 0.147 | 2 |
| Surveying | G13 | 0.054 | 0.093 | 0.138 | 0.095 | 0.089 | 5 |
| Transportation engineering | G14 | 0.013 | 0.023 | 0.035 | 0.024 | 0.022 | 11 |
| Hydrology/water resource engineering | G15 | 0.024 | 0.043 | 0.064 | 0.044 | 0.041 | 9 |
| Total | 1.071 | 1.000 | |||||
Source(s): Compiled by authors
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