A Structured Framework for Proficiency Calculation of Engineering Graduates.
1. IntroductionMillions of engineering graduates pass every year, specializing in various core branches like Computer Science, Information Technology, Electrical, Electronics and Communication Engineering. But the graduates lack skills and capabilities as per the industry expectation [1]. Because of lacking in skills, many pass-out graduates are unemployed [2]. Therefore, there is an immediate need for skill development courses for students pursuing engineering and other professional courses [3]. These courses can be self-chosen or can be a part of curriculum.
When graduates join industry, they undergo rigorous training, so that they can contribute to industry effectively and efficiently [4-5]. While companies seek a group of general skills in applicants, e.g., effective communication, administrative proficiency and technical skills, every engineering profession and occupation has a defined set of skill requirements [6]. Engineering skills require creativity and innovation, which is also a call for industrial revolution, can be inculcated by implementing projects [7-8]. Skills would enhance confidence in fresh graduates providing them better employment opportunities [9]. Industry expects their employees to continue learning to keep up with the fast technological change [10]. Engineering institutions should take the responsibility of improving employability by imbibing skills in the curriculum [11].
Also reports highlight that less then one third of workers entering the workforce in the world are fulfilling the specifications laid down by employers [12]. Rapid technological changes create faster skill gaps, both cognitive and non-cognitive skills as a part of lifelong learning is the requirement of the future [13]. Though engineering students are getting professional learning, but still the gap exists [14]. There is always a need of identifying future demands and updating skills [15-16], and e-learning tools can be used for self-development and skill enhancement if time is a constraint in a teaching institute [17]. Economic growth and generation of employment add to the problem of skill gap [18-19], as the shortage of sufficiently "skilled" applicants intensifies [20-21]. Lack of skills surfaces the problem of adaptability of graduate engineers [22].
Due to the gravity of the problem, several initiatives have been taken. To fill this skill gap, Government also has launched several development initiatives nationwide for enhancing proficiency, believing in the requirement of skill education at the tertiary education level. Outcome Based Education (OBE) has also come out as a solution to above problem, in the field of engineering education. OBE prioritizes the program outcomes over the precise curriculum with emphasis on the knowledge, skills, competencies, and employability of the graduates [23]. To attain these objectives, the curriculum must be carefully crafted with provisions for ongoing improvement. For every course, course outcomes (COs) are systematically designed to align with the program outcomes (POs). POs therefore come into play in the assessment of proficiency of graduate [24]. The key objectives include mapping Program Outcomes (POs) to proficiency attainment, combining academic and professional information within a single evaluation system, and providing actionable recommendations for curriculum and individual development [25]. Holistic Curriculum should include competency or skill-based courses to improve upon the employment rates and thus the growth of nation [26-27].
Studies identify the increase in the inclusion of competency-based courses in universities that cater to students opting degrees in Science, Technology, Engineering, and Mathematics (STEM) disciplines in the last few years [28-29]. The competency-based education model has been examined from the perspectives of knowledge and learning outcomes, which are enhanced by the STEM related courses [30]. Skill based or competency-based learning promotes lifelong learning and prepares students for engineering and technological adaptability [31-33]. Academic success is related to better learning, as per the set guidelines of program outcomes also termed as graduate attributes [34,36]. Use of innovative pedagogical practices to enhance learner's skills can help in meeting industrial requirements [35]. The results show that such education is an outcome-driven model for contemporary engineering education, emphasizing the preparation of students for their capabilities following graduation more than merely their existing knowledge [37-39]. Furthermore, data-driven examinations of academic success and entrepreneurial skills have been studied and checked for applicability to competency [40-41].
Therefore, this paper proposes a proficiency assessment framework to measure four domains, academic skills, technical skills, soft skills, and life skills. The program outcome attainments are used to calculate proficiency to guarantee a comprehensive assessment of student performance using direct assessment tool i.e. academic transcript. The framework categorizes proficiency into three clear levels, which are: Basic, Intermediate, and Advanced. This helps enabling in-depth analysis of proficiency attainment.
2. Methodology
This section elaborates on the POs attainment and proficiency levels calculation. The eleven POs established under ABET guidelines ensure that engineering graduates acquire a balance of technical expertise, ethical grounding, and societal responsibility. These outcomes focus on both immediate academic achievements and long-term professional success. Therefore, a holistic curriculum should be adopted to ensure that all POs are attained. This section elaborates on the POs attainment and proficiency levels calculation. Prior to performing correlation and covariance analyses, the data were examined for basic statistical assumptions. Given the sample size (n = 124), the distribution of proficiency scores was considered appropriate for parametric analysis under the Central Limit Theorem.
Conventional proficiency assessment practices rely on CGPA-based evaluation, aptitude testing, and placement-readiness indices, which primarily emphasize academic achievement and limited technical competencies. Although some Outcome-Based Education (OBE) models incorporate Program Outcome (PO) attainment into evaluation, these approaches often do not quantify overall student proficiency in a comprehensive manner [42-44]. To address this limitation, this study proposes a structured proficiency evaluation framework based on four key criteria: Academic Skills, Technical Skills, Soft Skills, and Life Skills, in which proficiency is systematically computed using students' academic performance and its alignment with Program Outcomes. The weightages assigned to individual Program Outcomes in the proficiency calculation were validated using the Nominal Group Technique (NGT), a structured expert-based consensus-building method [45].
The NGT panel comprised of 24 experts, including Board of Studies (BoS) Chairpersons from 15 engineering departments, 8 Deans and Principal. The validation process was conducted over 1 round and followed standard NGT stages:
* Independent Idea Generation: Experts independently proposed PO weightages based on their relevance to the four proficiency domains.
* Round-Robin Presentation: Each participant presented their inputs to ensure equal contribution.
* Clarification and Discussion: Group discussion focused on alignment with NBA defined POs, Program Educational Objectives, and employability expectations.
* Consensus Formation: Final weightages were finalized based on a consensus threshold of 80% agreement among panel members.
The validated PO weightages were subsequently incorporated into the proficiency calculation model presented in Table 1, ensuring methodological rigor, expert validation, and alignment with Outcome-Based Education principles.
For PO attainment calculation, we used direct assessment tools i.e. students' academic transcripts. All the courses in the syllabus of a program were mapped to POs as per the expected outcome of the courses. The PO attainment was calculated as average of scores obtained in end semester evaluation for all courses mapped to the PO.
POs are categorized as academic skills, technical skills, soft skills and life skills as per the definitions of each proficiency. The calculation of each proficiency is shown in "Table 1". This proficiency calculation was designed as per the level of alignment of academic skills, technical skills, soft skills and life skills to POs. The weightage for each PO in calculation of proficiency was validated using nominal group technique (NGT), the process of decision making. The board of studies chairman of all departments shared their inputs for finalization of proficiency calculation during NGT.
The weightage assigned to each PO reflects its direct contribution to Academic skills. PO1 carries weightage of 30% because strong subject knowledge forms the base for all academic work. PO2 and PO3 have 20% each, as critical analysis and solution design support problem-solving and project performance. PO4 and PO5 carry 15% each, recognizing the role of research and modern tools in strengthening academic depth and practical understanding. Together, these weightages create a balanced model reinforcing knowledge, analysis, design, investigation, and tool usage.
The weightage for technical skills focuses on outcomes that build a student's ability to design, investigate, and apply engineering solutions. PO3 has the highest weight 30% because design and development are central to technical skills. PO4 and PO5 receive 25% each for their roles in experimentation, prototyping, and tool usage. PO6 contributes 20% by linking technical solutions with societal and environmental needs. Overall, these weightages strengthen practical ability, tool competence, and responsible engineering practice.
The weightage for soft skills highlight the importance of communication, teamwork, and professional behaviour. PO8 and PO9 each receive 30% because teamwork and communication are essential in engineering practice. PO10 receives 25% for developing planning, leadership, and project management skills. PO7 carries 15% as ethics guides responsible and value-based decision-making. Together, these weightages ensure students build strong interpersonal, managerial, and ethical skills.
The weightage for Life skills prioritizes adaptability, ethical maturity, and social awareness. PO11 receives the highest weight 35% to emphasize continuous learning and personal growth. PO7 contributes 25% by reinforcing ethical responsibility. PO6 carries 20% to develop societal and environmental awareness. PO8 has 20% to support teamwork and empathy. Combined, these weightages promote holistic personal development and responsible citizenship.
"Table 2" presents the description of proficiency levels. The cut-off scores of 60% and 70% used to classify proficiency levels are based on institutional academic performance benchmarks commonly adopted in outcome-based education frameworks. A score below 60% represents minimum acceptable attainment (Basic), 60-70% indicates satisfactory attainment aligned with program expectations (Intermediate), and scores above 70% reflect strong mastery and advanced competency (Advanced). These thresholds are consistent with grading and attainment practices followed in Indian engineering institutions implementing OBE and NBA accreditation norms.
3. Results
For this study, end-semester results of all eight semesters for one class are used. One class from the 2021-2025 engineering batch, comprising 124 students was studied. Program Outcome (PO) attainment was computed individually for each student using the prescribed calculation method, and a sample of these results is illustrated in "Figure 1".
Based on the PO scores, four proficiency domains, academic skills, technical skills, soft skills, and life skills, are calculated using the defined weighting model. A sample proficiency calculation is presented in "Table 3".
Using this framework, average skill attainment was calculated, along with reports highlighting specific areas requiring improvement as shown in "Table 4".
Average attainment of all skills is calculated and accordingly students are finally categorized to have achieved level 3 (high), level 2 (medium) or level 1 (low) of competency and reports are generated as in "Table 5".
The summary report of the class performance is shown in "Table 6". This helps to analyze the class performance and device strategies for curriculum and delivery improvement to enhance students' proficiency. From proficiency academic skills, technical skills, soft skills, and life skills are calculated. Correlation analysis is done on the results obtained to quantify the relation between academic skills, technical skills, soft skills, and life skill. "Table 6" shows Correlation matrix for proficiency skills with p < 0.01 for all correlations. All correlation coefficients were statistically significant (p < 0.01), confirming that the observed relationships among academic skills, technical skills, soft skills, and life skills are not due to random variation.
The correlation matrix that indicates the curriculum is effectively promoting holistic student development. This is because near-perfect correlation between academic and technical skills (r = 0.98) shows that the academic components of the curriculum are strongly reinforcing technical competence. Similarly, the high correlations of academic skills with soft skills (r = 0.87) and life skills (r = 0.89) demonstrate that classroom learning is translating into improved communication, teamwork, and personal effectiveness.
The strong association between technical skills and both soft skills (r = 0.90) and life skills (r = 0.92) reflects that practical, hands-on learning is also shaping students' interpersonal abilities and adaptability. The highest correlation, between soft skills and life skills (r = 0.97), further confirms that the curriculum's emphasis on teamwork, ethics, communication, and experiential learning is contributing to broader personal and professional maturity.
The findings highlight that academic, technical, soft, and life skills are strongly interconnected, indicating that improving one domain naturally strengthens others. Based on this, students are encouraged to adopt a balanced approach to learning by strengthening academic fundamentals, engaging in hands-on technical activities, and actively developing communication, teamwork, and personal management abilities. The proficiency calculation is important because today employers expect graduates to think critically, work collaboratively, adapt quickly, and apply knowledge effectively. By focusing on all four domains, students can enhance their overall proficiency. From correlation matrix we calculated covariance matrix. We used covariance matrix for geometric interpretation of results.
The academic skills, technical skills, soft skills and life skills can be represented by geometric representation. The relationship and interdependence among parameters like proficiency and skills are represented with covariance matrix. The graphical representation shows the interdependence of variables in the shape of an ellipse. The spread of the ellipse (minor axis) is represented with the standard deviation of the data set. The spread of ellipse (minor axis) is measured as the square root of the variance; However, variance represents the minor axis of ellipse in the directions parallel to the axes of skills and proficiency. In case of 2-D spread of the data, the variance in both horizontal and vertical direction needs to be calculated. The horizontal spread and the vertical spread of the data represent the positive and negative correlation among the measured parameters.
A 4*4 covariance matrix ([M.sub.cov]) related to academic skills, technical skills, soft skills and life skills is shown as,
[Please download the PDF to view the formula] (1)
Here, relation among academic skills (a), technical skills (t), life skills (ls) and soft skills (s) in each row of the matrix. For ease of analyzing only two variables at one time, a 2*2 subset of 4*4 covariance is considered in this approach. A 2*2 subset represents the shape of an ellipse. The shape of ellipse helps to understand the overall correlation between two parameters and their deviation from ideal value. In ideal case, when two variables are perfectly correlated with each other (correlation close to 1), the spread of the ellipse in negligible. In this condition, the shape of matrix closely resembles to a straight line. However, the deviation from the mean value always exists in the data set which results in the spread of data. The principal diagonal elements of the covariance matrix are represented by variances, and the off-diagonal elements are represented as co-variances. Equation (2) represents the values of co-variances among measured parameters as the off-diagonal elements, while the values of variances are represented in the principal diagonal element.
[Please download the PDF to view the formula] (2)
The "Figure 2" to "Figure 7" shows the two-dimensional (2-D) scattered data for academic skills, technical skills, soft skills and life skills respectively. These plots are graphical representations of corresponding covariance matrix. The covariance matrix defines the shape of the data. The diagonal spread is defined by the covariance, while the axis-aligned spread is defined by the variance. The geometrical shape of the scattered data closely represents the ellipse. The elliptical shape obtained for any of the two parameters (among s, a and t) is scaled by eigen values and rotated by eigen vectors.
"Figure 8" shows the impact of white Gaussian noise on natural data fluctuations in terms of intensity graph. The white Gaussian noise affects the stability of the covariance matrix. The presence of noise introduces variations in the covariance values, which appear as small intensity shifts across the plot. Overall, "Figure 8" highlights which proficiency domains show strong co-movement, which ones vary independently, and how the underlying variance structure influences the geometric interpretation of the proficiency ellipse plots.
The presence of Gaussian noise causes variation in the values of covariance elements in covariance matrix. The elements of covariance matrix are plotted on the intensity graph to show change in the value in terms of color intensity. Each time new data values are observed on an intensity graph; the new data values replace old data values. The colors displayed on an intensity graph correspond to the numeric values associated with the specified element of covariance matrix. The Gaussian Noise (WGN) is considered in this study. That data used in this research study is derived from academic transcripts, performance metrics, and human-centric evaluation. Due to this reason, random fluctuations occur due to factors such as variability in assessment, evaluator subjectivity, academic environment, and student-specific fluctuations. The WGN is widely used in statistical modelling because it has zero mean, constant variance, and no correlation between samples.
These properties closely resemble the random, unpredictable nature of performance deviations observed in educational datasets.
For analysis of covariance matrix, the presence of Gaussian white noise causes natural spread in variance and covariance values, which is reflected in the elliptical shapes in the 2-D scatter plots. WGN analysis therefore ensures ellipse orientation, spread, and eigen value-based scaling. The covariance in presence of noise is defined as
[M'.sub.cov] = [M.sub.cov] + [[sigma].sup.2][I.sub.4] (3)
Here, [[sigma].sup.2] is noise variance and [I.sub.4] is identity matrix.
4. Discussion
Proficiency assessment plays an important role in assessing students' readiness for industry. To achieve this, academic institutions should adopt holistic curriculum that includes courses in syllabus of program. This will help to develop knowledge about core domain, soft skills and life skills. This will help the students to perform in highly dynamic industry environment.
Industry wants graduates to adapt to dynamic local and global changes. They also want graduates to learn new tools, work effectively in diverse teams, and respond to evolving industry demands.
Proficiency assessment is a multidimensional evaluation framework that helps measure competencies. The required competencies as per industry expectations are already mapped to the POs. POs are the core metrics for proficiency calculation. This is in line with Kolb's Experiential Learning Theory and Biggs' Constructive Alignment Model [42,45-46].
Unlike conventional outcome assessment methods that report Program Outcome attainment in isolation, the proposed framework integrates PO attainment into a unified proficiency model across four skill domains. The correlation and covariance analyses demonstrate that proficiency domains do not evolve independently but reinforce one another. This integrated statistical and geometric interpretation provides a novel way for institutions to visualize skill interdependence at both individual and cohort levels, thereby enabling data-driven academic interventions and curriculum redesign.
5. Conclusion
As per the "India Skills Report 2025," every year, nearly 1.5 million engineering graduates complete their studies, but only about 25% meet industry expectations and get employed [47-48]. This gap highlights the need to re-examine the curriculum so that it not only imparts domain knowledge but also equips students with skills aligned with industry requirements. Today, employers expect graduates to demonstrate strong academic understanding along with technical competency, effective communication skills, and the life skills necessary to adapt to evolving industry demands.
Therefore, in this paper a structured and quantifiable framework for assessing the proficiency of engineering students across four key domains, academic skills, technical skills, soft skills, and life skills is presented. Kolb's Experiential Learning Theory and Biggs' Constructive Alignment Model also support the suggested framework.
Universities are encouraged to integrate structured proficiency assessment frameworks into their academic processes to continuously monitor student development beyond academic performance alone. Curriculum designers should ensure balanced inclusion of technical, soft, and life skill-oriented courses, supported by experiential learning and interdisciplinary projects.
The framework uses Program Outcome (PO) attainment as the core metric for engineering graduate proficiency calculation. This model is applied to one class of 124 students to calculate individual proficiency profiles of all students. The results show that the framework is effective in identifying strengths and areas of improvement. The geometric interpretation allows institutions to identify distribution patterns within a batch. The statistical analysis shows strong positive correlations among all four proficiency domains, with correlation values ranging from 0.87 to 0.97. This shows that student competencies are interconnected. At an institutional level, the framework supports evidence-based curriculum refinement, faculty development, and accreditation processes.
Feedback reports based on proficiency levels enable students to clearly understand competencies they need to strengthen. The case study clearly demonstrates the way framework can be applied in academic institutes.
At present the model has limitations that it uses only direct assessment tool i.e. academic transcripts for proficiency calculation but in future the model can be strengthened by including indirect assessment tools such as resumes, certifications, internships, and project portfolios. The use of AI-based tools for automatic report generation and deeper learning analytics can further strengthen accuracy, efficiency, and scalability of the model.
Author contributions
Lochan Jolly: Conceptualization, Reviewing, Methodology, Analysis, Arpit Rawankar: Data Analysis, Software Modelling Megha Gupta: Writing-Original draft preparation, Writing and Editing, Anvita Birje: Writing-Original draft preparation, Writing and Editing.
Conflicts of interest
The authors declare no conflicts of interest.
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Lochan Jolly (1) (iD), Megha Gupta (*2) (iD), Arpit Rawankar (3) (iD), Anvita Birje (4) (iD)
(1) Thakur College of Engineering & Technology, Dept. of Electronics and Telecommunications Engineering, India, lochan.jolly@thakureducation.org
(2) Thakur College of Engineering & Technology, Dept. of Electronics and Telecommunications Engineering, India, megha.gupta@thakureducation.org
(3) Thakur College of Engineering & Technology, Dept. of Electronics and Telecommunications Engineering, India, arpit.rawankar@thakureducation.org
(4) Thakur College of Engineering & Technology, Dept. of Electronics and Telecommunications Engineering, India, anvita.birje@thakureducation.org
Cite this study: Jolly, L., Gupta, M., Rawankar, A. & Birje, A. (2026). A Structured Framework for Proficiency Calculation of Engineering Graduates. Turkish Journal of Engineering, 10(3), 948-957.
Received: 09.12. 2025
Revised: 04.03. 2026
Accepted: 07.03. 2026
Published: 30.07.2026
https://doi.org/10.31127/tuje.1838833
Table 1. Proficiency Calculation Proficiency Formula skill Academic 0.3PO1+0.2PO2+0.2PO3+0.15PO4+0.15PO5 Technical 0.3PO3+0.25PO4+0.25PO5+0.2PO6 Soft 0.3PO8+0.3PO9+0.25PO10+0.15PO7 Life 0.35PO11+0.25PO7+0.2PO6+0.2PO8 "Source: Author".
Table 2. Proficiency level description Proficiency Level Description Level 1 (Basic): Foundational understanding with limited application. Level 2 Moderate understanding applies knowledge effectively (Intermediate): in routine tasks. Level 3 Mastery of concepts, demonstrating leadership and (Advanced): innovation in solving complex problems. "Source: Author".
Table 3. Proficiency calculation for sample Sr. Academic Technical Soft skills Life skills No skills skills 1 2 2 2 2 2 3 3 3 3 3 3 3 3 3 4 3 2 3 3 5 2 2 3 3 "Source: Author".
Table 4. Average skill attainment and report for the sample
Sr Average Report
No Skill
attainment
Meets Expectations. Academic Skills need improvement.
Strengthen PO1, PO2, PO3, PO4, PO5. Technical Skills
need improvement. Focus on PO3, PO4, PO5. Soft Skills
1 2.28 need enhancement. Address PO8, PO10. Life Skills need
improvement. Improve PO8, PO11.
Exceeds Expectations. Can work to attain beyond
2 3 Curriculum Skills to get Competitive edge and best of
Global Career. Life Skills need improvement.
Exceeds Expectations. Strives to develop skills beyond
3 3 the curriculum for a competitive advantage and a
successful global career.
Beyond Expectations. Demonstrates the potential to
develop skills beyond the curriculum for a competitive
4 2.78 edge and a successful global career. However,
technical skills require improvement. Focus on
enhancing them.
Beyond Expectations. Has the potential to develop
skills beyond the curriculum for a competitive edge
5 2.7 and a successful global career. However, academic and
technical skills need improvement, that is focus on
strengthening PO1, PO3, and PO5.
"Source: Author".
Table 5. Summary PO attainment report of Class Level Students Students with Students with Total [right arrow] with Level 1 Level 2 Level 3 Students PO[down arrow] Count % Count % Count % PO1 2 1.61 51 41.13 71 57.26 124 PO2 2 1.61 48 38.71 74 59.68 124 PO3 2 1.61 64 51.61 58 46.77 124 PO4 1 0.81 51 41.13 72 58.06 124 PO5 1 0.81 47 37.90 76 61.29 124 PO6 0 0.00 19 15.32 105 84.68 124 PO7 0 0.00 24 19.35 100 80.65 124 PO8 1 0.81 43 34.68 80 64.52 124 PO9 0 0.00 29 23.39 95 76.61 124 PO10 1 0.81 40 32.26 83 66.94 124 PO11 1 0.81 41 33.06 82 66.13 124 "Source: Author".
Table 6. Correlation matrix for Proficiency Skills Proficiency Academic Technical Soft Life Skills Skills Skills Skills Skills Academic 1 0.98 0.87 0.89 Technical 0.98 1 0.90 0.92 Soft Skills 0.87 0.90 1 0.97 Life Skills 0.89 0.92 0.97 1 "Source: Author".
ORCID: https://orcid.org/0000-0003-3038-7958
ORCID: https://orcid.org/0000-0002-8271-0780
ORCID: https://orcid.org/0000-0002-6459-2135
ORCID: https://orcid.org/0009-0000-0995-6867
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| Title Annotation: | Research Article |
|---|---|
| Author: | Jolly, Lochan; Gupta, Megha; Rawankar, Arpit; Birje, Anvita |
| Publication: | Turkish Journal of Engineering (TUJE) |
| Article Type: | Report |
| Geographic Code: | 9INDI |
| Date: | Jul 1, 2026 |
| Words: | 5541 |
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