ACADEMIC PERFORMANCE PREDICTION BASED ON MULTI SOURCE, MULTI-FEATURE BEHAVIORAL DATA

Abstract

Academic performance prediction has become an
important research area in modern educational
systems due to the rapid growth of educational data
and digital learning platforms. Traditional
evaluation methods mainly depend on examination
scores and attendance records, which provide only
limited insights into student learning behavior.
However, student success is influenced by multiple
factors such as study habits, engagement levels,
participation in academic activities, and interaction
with online learning systems. This project proposes
an intelligent academic performance prediction
system based on multisource, multi-feature
behavioral data to improve prediction accuracy and
support proactive educational decision-making. The
proposed system integrates academic records,
behavioral characteristics, and digital learning
activities collected from multiple data sources.
Data preprocessing techniques such as cleaning,
normalization, handling missing values, and feature
selection are applied to improve data quality and
consistency. Feature fusion methods are then used
to combine multiple attributes into a unified dataset
for effective analysis. Machine learning algorithms
including Linear Regression, Random Forest, and
Neural Networks are employed to analyze learning
patterns and predict academic performance. The
system classifies students into different
performance categories such as high-performing,
medium-performing, and at-risk students. It also
provides personalized recommendations, learning
guidance, and early intervention support to improve
student outcomes. Continuous monitoring of
student activities enables institutions to track
performance trends and identify academic risks at
an early stage. The proposed framework enhances
prediction reliability, supports personalized
learning strategies, improves student retention, and
promotes data-driven educational management.
Overall, the system contributes to the development
of intelligent and adaptive learning environments
capable of improving academic success and
institutional effectiveness.