Development of AI-Based Exam Question Difficulty Level Prediction Systems in Sokoto State University, Sokoto State
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Background of the Study
One of the critical challenges in academic assessments is ensuring that exam questions are appropriately aligned with students' capabilities and learning levels. Traditional methods of designing exam questions may not always reflect the diversity in students’ knowledge and abilities, leading to either excessively difficult or easy questions. AI-based exam question difficulty level prediction systems can utilize machine learning algorithms to predict the difficulty of exam questions based on historical performance data, student feedback, and question characteristics. These systems can support educators in designing balanced exams that match students' knowledge levels, promoting fairer and more effective assessments.
Sokoto State University, located in Sokoto State, faces challenges in exam design, particularly in accurately determining the difficulty levels of questions. Some students report that exams are too difficult, while others feel that the questions are too easy, which can lead to dissatisfaction and inaccurate assessments of student performance. By adopting AI-based systems, Sokoto State University can predict exam question difficulty levels based on data-driven insights, ensuring that exams are more aligned with students’ actual learning progress. This study aims to develop and evaluate an AI-based exam question difficulty level prediction system tailored to the needs of Sokoto State University.
Statement of the Problem
Designing exams that accurately reflect students' capabilities remains a significant challenge in Nigerian universities. At Sokoto State University, exam questions often fail to provide a balanced level of difficulty, leading to inaccurate assessments of student performance. Traditional methods of question design do not fully account for variations in student performance or question difficulty. AI-based exam question difficulty prediction systems have the potential to address this gap by providing real-time analysis and predictions about the difficulty level of questions based on student data. However, there is limited research on the implementation of these systems in Nigerian universities, particularly in Sokoto State University. This study seeks to assess the effectiveness and feasibility of such AI-based systems in improving exam question design.
Objectives of the Study
- To develop an AI-based exam question difficulty level prediction system for Sokoto State University.
- To evaluate the effectiveness of the AI-based system in predicting the appropriate difficulty levels of exam questions.
- To explore the challenges and opportunities in implementing AI-based exam question difficulty level prediction systems in Nigerian universities.
Research Questions
- How effective is the AI-based exam question difficulty level prediction system in accurately predicting the difficulty of exam questions at Sokoto State University?
- What impact does the AI-based prediction system have on the fairness and accuracy of student assessments at Sokoto State University?
- What challenges and opportunities exist in implementing AI-based exam question difficulty level prediction systems in Nigerian universities?
Research Hypotheses
- AI-based exam question difficulty level prediction systems can accurately predict the difficulty of exam questions at Sokoto State University.
- The implementation of AI-based exam question prediction systems results in more balanced and fair assessments of student knowledge.
- The adoption of AI-based exam question difficulty level prediction systems faces challenges related to data quality, algorithm accuracy, and faculty readiness at Sokoto State University.
Significance of the Study
This study will offer insights into how AI-based systems can improve the design and fairness of exams at Sokoto State University. The findings may help educational institutions design more balanced assessments, enhancing student satisfaction and the accuracy of performance evaluation.
Scope and Limitations of the Study
The study will focus on the development and evaluation of an AI-based exam question difficulty level prediction system at Sokoto State University, Sokoto State. It will be limited to a sample of faculty members involved in exam design and students from selected departments. The findings may not be applicable to other universities outside Sokoto State, and the study will focus on AI technologies that can be integrated into the university's existing infrastructure.
Definitions of Terms
AI-Based Exam Question Difficulty Level Prediction System: A system that uses machine learning algorithms and historical data to predict the difficulty of exam questions before they are administered to students.
Exam Question Difficulty Level: The perceived level of challenge a question presents to students, determined by factors such as content complexity, question format, and students’ prior knowledge.
Performance Assessment: The process of evaluating a student’s academic achievement, typically through exams, assignments, or other forms of evaluation.
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