Machine Learning Model Predicts Student Grades With Near-Perfect Accuracy

Every semester, universities around the world lose thousands of students who slip through the cracks — learners whose struggles go unnoticed until it is too late to intervene. A new study from Banasthali Vidyapith in Jaipur, India, suggests that this failure of early detection may be largely solvable with the right algorithm. Researchers Nisha Madaan and Sanjay Kumar Sharma have built a machine learning system that predicts student academic performance with startling precision, and they have paired it with explainable artificial intelligence tools that reveal exactly why each prediction is made. The work, published in SN Social Sciences, arrives at a moment when higher education institutions are under growing pressure to improve retention, raise teaching quality, and demonstrate measurable progress toward the United Nations Sustainable Development Goals.

The study sits at the intersection of two research traditions that have been converging for years: Educational Data Mining, a field devoted to extracting useful patterns from the digital traces students leave behind, and the broader movement to apply artificial intelligence to sustainable development. The authors frame their work explicitly in terms of institutional sustainability, arguing that a university’s ability to identify and support struggling students is not merely a pastoral concern but a structural one. Student success rates and the quality of education feed directly into institutional rankings, funding decisions, and reputations, which means that a reliable early-warning system has consequences far beyond any individual classroom.

To build their model, the researchers collected primary data from 200 university students using a structured questionnaire with Likert-scale items designed to measure learning behavior, social media usage, and psychological traits, alongside academic records used strictly for research purposes in accordance with institutional ethical guidelines. What distinguishes this dataset from many earlier efforts in the field is its breadth. Rather than focusing narrowly on grades and attendance, the team captured seven categories of potential influence on academic outcomes: demographic background, socioeconomic conditions, academic history, social and family environment, health and wellness, infrastructure and services available to the student, and time management and extracurricular activity. This holistic framing reflects a growing recognition in education research that performance is shaped by a dense web of factors extending well beyond the lecture hall.

With the dataset assembled, the researchers trained and compared several regression algorithms, each representing a different mathematical philosophy for mapping input variables to a predicted outcome. The contenders included a Decision Tree Regressor, which partitions the data through a series of branching questions; a Random Forest Regressor, which averages the outputs of many decision trees to reduce overfitting; and a Support Vector Regressor, which fits a function within a tolerance margin around the training points. They also deployed Elastic Net Regression, a hybrid technique that blends two regularization penalties — the L1 penalty of lasso regression and the L2 penalty of ridge regression — into a single objective function. Regularization is the mathematical equivalent of a discipline mechanism: it constrains the model’s coefficients so that the algorithm cannot chase noise in the training data, and the elastic net’s dual penalty allows it both to shrink irrelevant features toward zero and to handle situations where predictor variables are correlated with one another, a common condition in survey data where questions about family support, finances, and wellness inevitably overlap.

The verdict was unambiguous. The Elastic Net Regressor outperformed its competitors, achieving an R-squared value of 0.9961 and a root mean square error of 0.511. In plain terms, an R-squared approaching 1.0 means the model explains almost all of the variance in the observed academic outcomes, and an RMSE of roughly half a point indicates that its predictions deviate only modestly from actual performance values. Such near-ceiling accuracy on a dataset of this size will invite scrutiny — models that fit training data almost perfectly sometimes reflect overfitting rather than genuine predictive power — but the authors position the result as evidence that the relationships between academic performance and its determinants can be represented effectively by machine learning when the right features are measured and the right constraints are applied. The elastic net’s built-in feature selection, which pushes the coefficients of uninformative variables toward zero, is likely part of the reason it generalized better than the tree-based ensembles on this data.

Perhaps the most consequential aspect of the study is not the raw accuracy but the transparency layer the researchers built on top of it. Black-box predictions have long been a barrier to adopting machine learning in education, because administrators and educators are understandably reluctant to act on a number they cannot interrogate. Madaan and Sharma addressed this by applying two of the most widely used explainable AI frameworks: SHAP, or SHapley Additive exPlanations, which borrows concepts from cooperative game theory to distribute credit for each prediction among the input features, and LIME, or Local Interpretable Model-agnostic Explanations, which builds simple, locally faithful surrogate models around individual predictions. Together, these methods allow the system to show not just that a student is predicted to underperform, but which specific factors — a health issue, a family circumstance, a time-management pattern — are driving that forecast for that particular individual.

This interpretability matters because it converts prediction into action. A model that merely flags a student as at risk tells an advisor where to look; a model that says the risk is driven primarily by, say, inadequate access to campus services or excessive social media consumption tells the advisor what to do. The authors visualize the factors influencing academic performance graphically, and they describe the development of dashboards intended to support decision-making processes within higher education institutions. In operational terms, the envisioned workflow is straightforward: institutions feed student data into the model, the system computes predicted outcomes and their explanations, and staff use those outputs to detect struggling students early, offer timely interventions, and ultimately reduce dropout. The researchers argue that such a system can be beneficial across the sector, from resource-constrained colleges to large research universities.

The study also connects to a lively international research conversation. Recent work has applied machine learning to academic performance prediction across a wide range of contexts — from graph neural networks that model peer influence in online question pools, to exercise-aware knowledge tracing systems that track what a student knows in real time, to comparative studies of classification and regression models among postgraduate public health students. Other teams have explored elastic net approaches in adjacent domains, including using elastic net regression to classify developmental language disorders, underscoring the method’s versatility when datasets contain many correlated predictors. What the Banasthali Vidyapith study adds to this landscape is the combination of a multi-dimensional survey-based feature set, a head-to-head algorithm comparison, and an explicit explainability pipeline, all framed within the sustainability agenda that increasingly shapes higher education policy.

There are, of course, important caveats. A model trained on 200 students at a single institution cannot simply be transplanted to a different university with a different student population, and the authors note that no datasets were generated or analyzed beyond the study itself, with code available on request. Survey-based measures of behavior and psychological traits are self-reported and therefore subject to well-known biases, and any system that predicts student outcomes raises ethical questions about labeling, privacy, and the risk that a low prediction becomes a self-fulfilling prophecy. The researchers are careful to state that academic records were used strictly for research purposes under institutional ethical guidelines, and the growing literature on explainable educational AI reflects a shared awareness that transparency is not just a technical nicety but an ethical requirement when algorithms touch people’s educational trajectories.

Even with those qualifications, the direction of travel is clear. As artificial intelligence matures from novelty to infrastructure in higher education, the institutions that thrive will be those that can turn data into timely, comprehensible, and humane action. A regression model that achieves an R-squared of 0.9961 while explaining its own reasoning through SHAP and LIME offers a glimpse of what that future could look like: not chatbots replacing teachers, but quiet analytical systems working in the background, flagging trouble before it becomes failure, and giving educators the evidence they need to intervene while intervention still matters. For the students who would otherwise have slipped through the cracks, that difference may be the most important number of all.

Subject of Research: Machine learning prediction of university student academic performance using regression models and explainable AI

Article Title: Predicting student academic performance using a regression model

Article References: Predicting student academic performance using a regression model. (n.d.). https://doi.org/10.1007/s43545-026-01838-2

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01838-2

Keywords: machine learning, educational data mining, academic performance prediction, elastic net regression, explainable AI, SHAP, LIME, higher education, student dropout, predictive analytics, sustainability, regression models