ArticleScientific reports2025
A student academic performance prediction model based on the interval belief rule base.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- A Correlation-Decoupled Interval Belief Rule Base for Interpretable Cross-Condition Bearing Fault Diagnosis.Entropy (Basel, Switzerland) · 2026Article
- A Photovoltaic Power Prediction Method Based on Data-Driven Interval Construction Belief Rule Base.Sensors (Basel, Switzerland) · 2026Article
- A spam detection model based on the discriminative TF-IDF belief rule base.Scientific reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Student performance prediction (SPP) constitutes one of the pivotal tasks in educational data analysis. Outcomes from the prediction enables educators to implement targeted interventions for students. Therefore, developing an effective SPP model is of critical importance. The belief rule base (BRB) is a rule-based modeling approach that integrates expert knowledge and effectively manages uncertain information. Nevertheless, when employing traditional BRB to construct a prediction model, excessive input attributes and reference points may result in a combination explosion. Furthermore, in practical scenarios, the configuration of the model's parameters may be restricted by the limitations of expert knowledge. To overcome these challenges, an SPP model using an interval BRB structure based on the random forest (RF) attribute selection method (IBRB-C) is proposed. The parameters of the IBRB-C model are determined by combining the expert knowledge and the Kmeans++ algorithm. Subsequently, the P-CMA-ES algorithm is applied to optimize the initial model. Ablation experiment is conducted to validate the rationality of the IBRB-C. Finally, case studies on graduate applications and GPA of students demonstrate that the mean squared error (MSE) of the IBRB-C is 0.0024 and 0.1014, respectively. The results of comparative experiments confirm the superiority of the IBRB-C model in predicting student performance.
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.