Development of a Predictive Model for Early Identification of At-Risk Students in Nasarawa State University, Keffi, Nasarawa State
- Project Research
- 1-5 Chapters
- Abstract : Available
- Table of Content: Available
- Reference Style:
- Recommended for :
- NGN 5000
Background of the Study
Early identification of at-risk students is crucial for implementing timely interventions that improve academic outcomes and reduce dropout rates. At Nasarawa State University, Keffi, Nasarawa State, the application of predictive modeling through data science presents a transformative opportunity to identify students who are likely to struggle academically. By analyzing historical data such as grades, attendance records, socio-economic background, and behavioral indicators, predictive models can detect early warning signs of academic difficulties (Ibrahim, 2023). Machine learning algorithms, including logistic regression, decision trees, and neural networks, have been widely adopted in educational research to forecast student performance with high accuracy. These models can continuously learn from new data, thereby improving their predictive power over time. The integration of predictive analytics into the university’s administrative processes enables educators to develop targeted support strategies and allocate resources effectively. Additionally, predictive models provide actionable insights that allow for personalized intervention plans tailored to individual student needs (Chinwe, 2024). Real-time monitoring of student data can further enhance the model’s ability to alert faculty and counselors to emerging issues, ensuring that interventions are both timely and effective. Despite these advantages, challenges such as data quality, privacy concerns, and the need for robust computational infrastructure remain significant obstacles to the full implementation of predictive systems. This study aims to develop and validate a predictive model that accurately identifies at-risk students, thereby enabling proactive academic support and ultimately improving student retention and success (Olufemi, 2025).
Statement of the Problem
Nasarawa State University currently relies on traditional methods for identifying struggling students, which are often reactive and based on periodic assessments. This approach delays intervention and contributes to high dropout rates and poor academic performance. The absence of a predictive system limits the university’s ability to proactively identify at-risk students and provide timely support (Adebola, 2023). Additionally, the existing manual processes are inefficient, labor-intensive, and prone to human error, resulting in missed opportunities for early intervention. Although predictive modeling using data science offers a promising alternative, its implementation is challenged by issues such as incomplete or inconsistent data, concerns over data privacy, and limited technical expertise in deploying advanced machine learning models. These challenges hinder the creation of an accurate and reliable early warning system that can effectively forecast student performance. Consequently, many students who need assistance do not receive it until they are significantly behind, which exacerbates academic difficulties. This study seeks to address these challenges by developing a robust predictive model that leverages diverse student data to forecast academic risks. The model aims to enable timely interventions, thereby reducing dropout rates and enhancing overall student performance. By comparing various machine learning algorithms and validating the model with historical data, the research intends to provide a comprehensive solution for early identification of at-risk students and offer actionable recommendations for its integration into the university’s support framework.
Objectives of the Study:
-
To develop a predictive model for early identification of at-risk students using machine learning techniques.
-
To evaluate the model’s accuracy and reliability in forecasting academic performance.
-
To propose strategies for integrating the predictive model into student support services.
Research Questions:
-
How accurately can machine learning algorithms predict at-risk students?
-
What are the key factors that influence student performance at Nasarawa State University?
-
How can the predictive model be integrated into existing support systems to improve early intervention?
Significance of the Study
This study is significant as it utilizes predictive modeling to enhance early identification of at-risk students, enabling timely and targeted interventions that improve academic outcomes. The findings will provide actionable insights for educators and administrators at Nasarawa State University, contributing to reduced dropout rates and enhanced student success. The research supports data-driven decision-making in higher education and offers a model that can be replicated in similar institutions (Ibrahim, 2023).
Scope and Limitations of the Study:
The study is limited to the development and evaluation of a predictive model for early identification of at-risk students at Nasarawa State University, Keffi, Nasarawa State, and does not extend to other institutions or academic programs.
Definitions of Terms:
-
Predictive Model: A mathematical model that uses historical data to forecast future outcomes.
-
At-Risk Students: Students who are likely to experience academic difficulties or drop out.
-
Machine Learning: A subset of artificial intelligence that involves algorithms learning from data to make predictions.
Related Project Materials
An Evaluation of the Effectiveness of the Nigerian Tax Appeal Tribunal in Resolving Disputes: A Study of Cases from 2018–2023
Background of the Study
The Nigerian Tax Appeal Tribunal (TAT) serves as a specialized body for resolving...
An Evaluation of Ethical Investment Practices within Islamic Finance
Background of the Study
Ethical investment practices are a defining feature of Islamic finance, where financial decisions...
An Evaluation of Forensic Auditing Techniques in Nigerian Public Institutions: A Study of the Nigerian Ports Authority
Background of the Study
Public institutions in Nigeria are often plagued by inefficiencies, corruption, and financial mi...
EXTRACTION AND FORMULATION OF PERFUME FROM LEMONGRASS LEAVES
ABSTRACT
Perfume extraction refers to the extraction of aromatic compounds from raw materials, using methods such as distillation, solven...
AN APPRAISAL OF LEGAL AND INSTITUTIONAL FRAMEWORK ON MARITIME LAW IN NIGERIA
ABSTRACT
Nigeria has a land area of 923,768.64 square kilometers, with a coastline of over 823 kilometres and navigable inland waterways...
An assessment of digital campaign integration on brand consistency: A case study of a media conglomerate in Kano, Nigeria
Background of the study
Digital campaign integration involves the coordination of multiple digital marketing channels to d...
The effect of political socialization on women’s leadership emergence in Gusau Local Government Area, Zamfara State
Background of the Study
Political socialization is the process by which individuals acquire political beliefs, values, and...
An appraisal of government policy’s influence on small business credit availability in Nigeria: A study of microfinance institutions in Kano State
Background of the Study :
Government policies are pivotal in shaping financial landscapes, especially concerning credit availability for...
An examination of the cultural implications of name-changing practices in Abeokuta South Local Government Area, Ogun State
Background of the Study
Name-changing practices in Abeokuta South have long been embedded in cultural, religious, and social contexts, se...
An assessment of digital marketing tool effectiveness on customer acquisition in banking: a case study of Access Bank Nigeria
Background of the Study
Digital marketing has transformed customer acquisition strategies in the banking sector by providin...
