Auto Evaluation for Essay Assessment Using a 1D Convolutional Neural Network

Abstract

Traditional assessment methods often face a trade-off between accessibility and in-depth evaluation. While multiple-choice exams offer easy grading, they may limit the ability to assess critical thinking and analytical skills. On the other hand, essay exams provide a valuable tool to gauge these skills, but their manual evaluation has several drawbacks. First, grading essays is a time-consuming process. Each student’s response requires individual attention, leading to a significant workload for educators. Second, subjectivity is a major concern. Factors like the evaluator’s mental state, fatigue, or even background can influence their judgment, leading to inconsistencies and potential biases in grading. This paper proposes solving these challenges using artificial intelligence (AI) for essay assessment. We present a novel approach utilizing a One-dimensional Convolutional Neural Network (1D CNN) deep learning model. This model is specifically designed to analyze image-based student answer sheets, automatically classifying them according to the scores allocated for each question. The dataset used consists of answer sheets from 30 students in a coding class, each provided with a pre-annotated template. Our development process divided the available data into a 60/40 split, with 60% dedicated to testing the model’s performance and 40% used for training. The model achieved an average validation accuracy of 81.18% through this training. These results suggest that the proposed 1D CNN model offers a promising avenue for mitigating the limitations of manual essay grading. By automating the process and reducing subjectivity, this approach has the potential to streamline the assessment workload for educators while promoting consistency and fairness in evaluating student learning outcomes.

Keywords

• Deep learning
• automatic essay scoring
• handwritten image
• 1D CNN