An Investigation of Natural Language Processing for Automated Essay Scoring: A Case Study of Federal College of Education, Kano (Kumbotso LGA, Kano State)
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Background of the Study
Natural Language Processing (NLP) has made significant strides in the field of artificial intelligence, particularly in the analysis and understanding of human language. One of its promising applications is in automated essay scoring, which aims to evaluate written text in a manner similar to human graders. The Federal College of Education, Kano, located in Kumbotso LGA, Kano State, uses traditional grading methods for essay-based assessments, which are often time-consuming and subjective. With the increasing number of students and the growing importance of assessments, automated essay scoring systems powered by NLP can provide a solution to improve grading efficiency, consistency, and scalability. This study will investigate the potential of NLP techniques in automating the essay scoring process at the Federal College of Education, Kano, by developing a model that can accurately grade essays based on content, coherence, grammar, and structure.
Statement of the Problem
The current manual grading system at the Federal College of Education, Kano, poses several challenges, including inconsistencies in grading, delays in feedback, and the burden on lecturers. The increasing volume of students and assignments exacerbates these problems. Traditional essay grading relies on human evaluators, which can introduce subjectivity, leading to inconsistent scores for similar answers. Moreover, the time required to grade essays manually delays the return of feedback to students, which impacts their learning process. NLP-based automated essay scoring can address these challenges, but its effectiveness and accuracy in an educational context such as Federal College of Education, Kano, have not been thoroughly explored.
Objectives of the Study
- To develop an NLP-based model for automating the essay scoring process at Federal College of Education, Kano.
- To evaluate the performance of the automated essay scoring system in comparison with human grading.
- To analyze the strengths and limitations of using NLP in automated essay scoring in a Nigerian educational context.
Research Questions
- How effective is the NLP-based automated essay scoring model in evaluating student essays at Federal College of Education, Kano?
- What is the level of agreement between automated essay scores and human scores?
- What are the challenges and limitations of using NLP for essay scoring in Nigerian educational institutions?
Research Hypotheses
- The NLP-based automated essay scoring system will provide consistent and accurate scores comparable to those of human graders.
- There will be a high degree of agreement between the automated essay scores and human scores.
- The implementation of NLP-based automated scoring will significantly reduce the time required to grade essays.
Significance of the Study
This study will contribute to the exploration of NLP's potential in automating essay scoring in Nigerian higher education, specifically at the Federal College of Education, Kano. The findings will assist in improving grading efficiency, enhancing consistency in assessment, and providing students with quicker feedback. The research may also help institutions in adopting advanced technologies for academic assessment.
Scope and Limitations of the Study
The study will focus on the development and evaluation of an NLP-based essay scoring model for the Federal College of Education, Kano, within Kumbotso LGA, Kano State. It will not extend to other institutions or address broader challenges in NLP application outside of essay grading.
Definitions of Terms
Natural Language Processing (NLP): A branch of artificial intelligence that focuses on the interaction between computers and human language.
Automated Essay Scoring: The use of computer-based systems to grade essays based on predefined criteria such as content, structure, and grammar.
Grading Consistency: The degree to which different graders provide similar scores for the same answer.
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