Evaluation of Machine Learning Algorithms for Detecting Student Learning Difficulties in Kebbi State Polytechnic, Birnin Kebbi, Kebbi State
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Background of the study
The identification of learning difficulties in students is essential for providing timely intervention and improving academic outcomes. Traditionally, educators rely on assessments and observations to identify students struggling with learning, but these methods can be subjective and may not detect early warning signs. With advancements in machine learning (ML), there is a growing opportunity to automate the detection of learning difficulties by analyzing students’ behavior, engagement, and academic performance data. Kebbi State Polytechnic, Birnin Kebbi, provides an ideal setting for exploring how machine learning algorithms can be applied to identify students facing learning challenges. By analyzing patterns in students’ academic records, attendance, and interaction with learning materials, machine learning models can offer valuable insights into which students may need additional support. This study aims to evaluate different machine learning algorithms for detecting learning difficulties and their potential to enhance academic support at Kebbi State Polytechnic.
Statement of the problem
In Kebbi State Polytechnic, there is a lack of early detection systems to identify students facing learning difficulties. Current methods for identifying struggling students primarily rely on manual assessments and educator observations, which are time-consuming and often miss students who may require early intervention. Additionally, the absence of a systematic, data-driven approach leads to inconsistencies and delayed academic support. This study seeks to evaluate the effectiveness of machine learning algorithms in detecting student learning difficulties, which could help instructors intervene at an earlier stage and offer targeted support to those who need it the most.
Objectives of the study
1. To evaluate the effectiveness of different machine learning algorithms in detecting student learning difficulties at Kebbi State Polytechnic.
2. To analyze the correlation between students' academic performance and their likelihood of facing learning difficulties.
3. To provide recommendations for implementing machine learning-driven detection systems in educational institutions.
Research questions
1. Which machine learning algorithms are most effective in detecting student learning difficulties at Kebbi State Polytechnic?
2. How do academic performance and engagement correlate with learning difficulties identified by the machine learning models?
3. How can machine learning-based detection systems improve the identification and support of students with learning difficulties?
Research hypotheses
1. Machine learning algorithms will be effective in detecting students with learning difficulties at Kebbi State Polytechnic.
2. There will be a significant correlation between students' academic performance and the likelihood of facing learning difficulties.
3. The use of machine learning-based systems will improve the efficiency and accuracy of detecting learning difficulties in students.
Significance of the study
This study will provide insights into the applicability and effectiveness of machine learning in detecting learning difficulties, offering educational institutions a tool to enhance academic support. The findings may pave the way for Kebbi State Polytechnic and other similar institutions to adopt data-driven approaches for early intervention, ultimately improving student outcomes.
Scope and limitations of the study
The study will focus on the evaluation of machine learning algorithms applied to detect student learning difficulties in Kebbi State Polytechnic, Birnin Kebbi, Kebbi State. Limitations include the availability and quality of student data, as well as challenges in training machine learning models to accurately detect various types of learning difficulties.
Definitions of terms
• Machine Learning Algorithms: Algorithms that allow computers to identify patterns in data and make predictions or decisions without being explicitly programmed.
• Learning Difficulties: Challenges that hinder a student's ability to understand, learn, or apply academic concepts effectively.
• Early Intervention: The timely support provided to students to address learning difficulties before they impact academic performance significantly.
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