Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Multiple Regression01:25

Multiple Regression

2.9K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
2.9K
Aggregates Classification01:29

Aggregates Classification

299
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
299
Reliability and Validity01:29

Reliability and Validity

12.7K
Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
12.7K
Classification of Systems-I01:26

Classification of Systems-I

168
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
168
Classification of Systems-II01:31

Classification of Systems-II

133
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
133
Classification of Signals01:30

Classification of Signals

381
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
381

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

HYDRA-XAI dual-backbone disaster scene recognition using ResNet50-Swin transformer feature fusion, explainable evidence, and an operational recommender.

Scientific reports·2026
Same author

ColoXAI-RecomNet: Explainable Recommender Framework for Colorectal Cancer Classification Using Integrated CNN Ensemble and LIME Interpretability.

Journal of imaging informatics in medicine·2026
Same author

Vitamin C in fruits and vegetables: stability, processing effects, analytical detection, nutrient interactions, bioavailability, and health implications.

Food chemistry·2026
Same author

Enhancing ionic conductivity in biodegradable cellulose acetate polymer electrolytes through formamide-induced plasticization.

International journal of biological macromolecules·2025
Same author

CADxPolydetect: a clinically explainable hybrid deep learning system for multi-class colorectal lesion detection using augmented colonoscopy images.

BMC medical informatics and decision making·2025
Same author

Colorectal cancer unmasked: A synergistic AI framework for Hyper-granular image dissection, precision segmentation, and automated diagnosis.

BMC medical imaging·2025

Related Experiment Video

Updated: May 30, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K

A multi-dimensional student performance prediction model (MSPP): An advanced framework for accurate academic

V Balachandar1, K Venkatesh1

  • 1Department of Networking & Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, India.

Methodsx
|January 27, 2025
PubMed
Summary

This study introduces a new Multi-dimensional Student Performance Prediction Model (MSPP) to accurately forecast student outcomes. The MSPP model enhances educational data analysis for better learning interventions and reliable classification.

Keywords:
Artificial neural networkClassificationDeep learningExplainable AIFeature engineeringMulti-dimensional Student Performance Prediction Model (MSPP)Personalized learning

More Related Videos

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

628

Related Experiment Videos

Last Updated: May 30, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

628

Area of Science:

  • Educational Data Mining
  • Artificial Intelligence in Education
  • Machine Learning

Background:

  • Traditional student performance prediction models struggle with multi-dimensional, imbalanced, and temporal educational data, leading to suboptimal classification.
  • Existing methods often provide generalized insights rather than tailored predictions, limiting the effectiveness of interventions.

Purpose of the Study:

  • To propose a novel Multi-dimensional Student Performance Prediction Model (MSPP) for precise forecasting of student academic performance.
  • To address limitations of existing models in handling complex educational datasets and improve the accuracy of student classification.

Main Methods:

  • Developed the MSPP model integrating advanced data preprocessing, feature engineering, and deep learning techniques, including graph neural network layers.
  • Employed adaptive hyper-parameter tuning and explainable AI (XAI) features to handle imbalanced and temporal data.
  • Utilized domain-specific preprocessing to structure sparse, heterogeneous academic data for multi-class classification.

Main Results:

  • The MSPP model achieved high accuracy (76%), precision (0.79), and macro F1-score (0.73), outperforming existing models.
  • Significantly reduced the False Positive Rate (FPR) to 0.15, enhancing prediction reliability.
  • Demonstrated improved precision-recall across multiple student performance categories (distinction, pass, fail, withdrawn).

Conclusions:

  • The MSPP model offers a robust framework for accurate student performance prediction by incorporating contextual information and multi-layered analysis.
  • The model's ability to handle complex data and provide reliable classifications supports the development of effective, personalized educational interventions.
  • This approach provides a foundation for generalizing insights from sparse, heterogeneous academic data, improving educational outcomes.