The research, conducted by Richa Indu and Atul Kulshrestha of the Department of Computer Science and Information Technology at the Himalayan Institute of Technology in Dehradun, Uttarakhand, focused on a cohort of 365 undergraduate students enrolled in Bachelor of Computer Applications and Bachelor of Science in Information Technology programs. Rather than relying on self-reported surveys or qualitative impressions, the authors constructed a numerical skill quotient for each student, computed from weighted combinations of attendance, assignment scores, and sessional test performance. The approach transforms an abstract policy goal into something that can be plotted, correlated, and regressed—a methodological choice that reflects a broader push in education research to ground reform debates in hard data.

The courses chosen for scrutiny were deliberately varied. Three Skill Enhancement Courses, a category introduced under the policy to embed practical competencies within degree programs, formed the analytical backbone: Mathematical Foundations of Computer Science, System Analysis and Design, and Graph Theory. Alongside these, the researchers examined a markup language, Hypertext Markup Language, and two programming languages, C and C#. This mix allowed the team to compare how skill attainment manifests across different kinds of intellectual demands—abstract mathematical reasoning on one end, applied systems thinking in the middle, and hands-on coding and markup fluency on the other.

The results reveal a striking heterogeneity in how skills develop across subjects. Among the Skill Enhancement Courses, System Analysis and Design recorded the highest mean skill score at 3.815, followed closely by Mathematical Foundations of Computer Science at 3.736. Graph Theory, however, lagged noticeably behind with a mean of 3.246. Among the programming and markup languages, HTML and C# posted nearly identical means of 3.620 and 3.619 respectively, while C trailed at 3.321. These gaps are not trivial. They suggest that even within a single department teaching a single cohort, the policy’s skill-development machinery produces uneven returns depending on the subject matter, with abstract theoretical content proving harder to translate into measurable skill gains than applied or structured content.

To probe these patterns, the researchers deployed a full statistical arsenal: descriptive statistics to characterize distributions, correlation analysis to detect associations, non-parametric tests to handle data that resisted normal-distribution assumptions, and regression models to disentangle the relative contributions of different academic indicators. The central question was deceptively simple—does showing up to class, completing assignments, and performing on sessional tests actually predict skill attainment as measured by the computed quotient? The answer, in short, is a qualified yes, with important caveats that carry real weight for how the policy should be evaluated going forward.

Perhaps the most consequential finding concerns the relative predictive power of different academic indicators. Regression analysis suggested that sessional test performance may be a stronger indicator of computed skill than attendance alone. In other words, what a student demonstrates on an in-course assessment tells you more about their skill development than the simple fact of their physical presence in a lecture hall. This finding cuts against a persistent institutional habit in Indian higher education, where attendance mandates are often treated as a proxy for learning. The data here imply that presence is a weak signal, while demonstrated performance carries more informational weight about whether skills are actually being absorbed.

Yet the study is equally clear about the limits of its own indicators. The analysis indicates that the relationship between attendance, academic performance, and skill attainment is subject-specific, meaning that no single statistical relationship holds uniformly across the curriculum. A correlation that appears in System Analysis and Design may not replicate in Graph Theory, and the strength of association between test scores and skill scores varies from course to course. This subject-specificity complicates any attempt to build a universal metric of skill development, and it suggests that administrators hoping to monitor the policy’s effectiveness with a single dashboard number may be oversimplifying a genuinely multidimensional phenomenon.

The authors are candid that attendance and academic performance, while useful, are insufficient to explain skill development independently. They point to a constellation of other factors that likely shape how competencies actually form: practical application of knowledge outside the classroom, active participation in learning activities, prior knowledge that students bring with them, individual learning strategies, and broader engagement with the material. This framing aligns with a substantial body of education research arguing that skill formation is a process of active construction rather than passive accumulation, and it implies that the policy’s emphasis on experiential learning is aimed at exactly the variables that traditional academic metrics cannot capture.

The study arrives at a moment when scrutiny of the National Education Policy 2020 is intensifying. A growing literature has examined the policy’s alignment with the United Nations Sustainable Development Goals, its implications for digital education, its relevance to lifelong learning, and its systematic review of higher education reforms. What distinguishes the Dehradun study is its granular, quantitative focus: rather than evaluating the policy at the level of national rhetoric or institutional design, it drills down to the level of individual students and individual courses, asking whether the day-to-day instruments of undergraduate assessment register the skill gains the policy envisions. The answer—that they register something, but far from everything—offers both reassurance and warning to policymakers.

For educators and administrators, the practical implications are concrete. If sessional tests outperform attendance as skill indicators, then assessment design deserves more investment than attendance enforcement. If skill attainment varies systematically across subjects, then Skill Enhancement Courses may need subject-specific pedagogical strategies rather than a one-size-fits-all template. And if the unmeasured factors—engagement, prior knowledge, practical application—do much of the heavy lifting, then institutions should consider supplementing numerical skill quotients with richer forms of evaluation, from project portfolios to peer assessment. The study does not declare the National Education Policy 2020 a success or a failure. Instead, it does something arguably more valuable: it demonstrates that the policy’s central promise can be measured, identifies where current measurement falls short, and hands researchers a replicable template for tracking, course by course and cohort by cohort, whether India’s great educational experiment is actually delivering the skills it promised.

Subject of Research: Quantitative assessment of skill development among Indian undergraduate students under the National Education Policy 2020

Article Title: Efficiency of the Indian national education policy 2020 in developing skills in undergraduate students

Article References: Efficiency of the Indian national education policy 2020 in developing skills in undergraduate students. (n.d.). https://doi.org/10.1007/s43545-026-01843-5

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01843-5

Keywords: National Education Policy 2020, skill development, undergraduate students, attendance, academic performance, skill score, experiential learning, computer applications, programming languages, regression analysis, India, higher education