CLUSTERING ANALYSIS ON FACTORS RESPONSIBLE FOR STUDENTS’ ACADEMIC LOW PERFORMANCE IN PLATEAU STATE POLYTECHNIC BARKING LADI, PLATEAU STATE

Low academic performance among students in tertiary institutions has become a significant concern, particularly in developing countries like Nigeria. This challenge has manifested in high failure rates, poor examination outcomes, and declining graduation statistics. Despite various efforts by institutions and governments to curb this trend, there remains a lack of comprehensive, data-driven analysis to pinpoint the underlying causes. This gap has limited the effectiveness of interventions, necessitating a more systematic approach. This study investigates the factors contributing to low academic performance among students at Plateau State Polytechnic Barkin Ladi, using a clustering analysis approach. The research identifies individual, institutional, and environmental factors influencing academic outcomes. Data were collected from 300 students through structured questionnaires and analyzed using hierarchical cluster analysis and principal component analysis. The findings reveal that motivation, study habits, quality of teaching, curriculum relevance, access to resources, parental involvement, peer influence, and socioeconomic status are critical factors. The study concludes that a holistic approach is necessary to address low academic performance, recommending targeted interventions to enhance student motivation, improve teaching quality, increase access to resources, strengthen parental and community involvement, and address socioeconomic barriers. This research provides insights for stakeholders in the educational sector to develop effective strategies for improving academic performance.

Authors

Publication Details

Journal
FUDMA Journal of Sciences
Published
2025-05-31
DOI
https://doi.org/10.33003/fjs-2025-0905-3519
Primary Topic
Online and Blended Learning
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

CLUSTERING ANALYSIS ON FACTORS RESPONSIBLE FOR STUDENTS’ ACADEMIC LOW PERFORMANCE IN PLATEAU STATE POLYTECHNIC BARKING LADI, PLATEAU STATE

Simon Dachung Jugul
FUDMA Journal of Sciences
Online and Blended Learning
article

CLUSTERING ANALYSIS ON FACTORS RESPONSIBLE FOR STUDENTS’ ACADEMIC LOW PERFORMANCE IN PLATEAU STATE POLYTECHNIC BARKING LADI, PLATEAU STATE

Simon Dachung Jugul
article en

Abstract

Low academic performance among students in tertiary institutions has become a significant concern, particularly in developing countries like Nigeria. This challenge has manifested in high failure rates, poor examination outcomes, and declining graduation statistics. Despite various efforts by institutions and governments to curb this trend, there remains a lack of comprehensive, data-driven analysis to pinpoint the underlying causes. This gap has limited the effectiveness of interventions, necessitating a more systematic approach. This study investigates the factors contributing to low academic performance among students at Plateau State Polytechnic Barkin Ladi, using a clustering analysis approach. The research identifies individual, institutional, and environmental factors influencing academic outcomes. Data were collected from 300 students through structured questionnaires and analyzed using hierarchical cluster analysis and principal component analysis. The findings reveal that motivation, study habits, quality of teaching, curriculum relevance, access to resources, parental involvement, peer influence, and socioeconomic status are critical factors. The study concludes that a holistic approach is necessary to address low academic performance, recommending targeted interventions to enhance student motivation, improve teaching quality, increase access to resources, strengthen parental and community involvement, and address socioeconomic barriers. This research provides insights for stakeholders in the educational sector to develop effective strategies for improving academic performance.

FUDMA Journal of SciencesVol. 9(5)
Openalex Percentile: Top 87%
Online and Blended Learning
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.