ArticleMethodsX2025
A multi-factor machine learning framework for predicting and profiling student academic performance using behavioral, financial, and wearable data.
Article in MethodsX, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study introduces a multi-factor machine learning framework designed to predict and profile student academic performance using behavioral, financial, and wearable data. The dataset, collected from higher education students in India, integrates lifestyle behaviors (e.g., activity score, screen time), financial variables (income, scholarship, loans, and tuition), and physiological data such as average heart rate, high BPM, and smartwatch usage. The method begins with structured preprocessing and feature engineering, including the construction of a Financial Stress metric and a composite stress index combining financial and physiological inputs. Multiple regression models were benchmarked to predict CGPA, with Random Forest yielding the highest accuracy (R² ≈ 0.30). Further, wearable-related features were analyzed using correlation and
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.