A Structured Dataset for Automated Grading: From Raw Data to Processed Dataset

Ibidapo Dare Dada (), Adio T. Akinwale and Ti-Jesu Tunde-Adeleke
Additional contact information
Ibidapo Dare Dada: Department of Computer and Information Science, Covenant University, P.M.B. 1023, Ota 112104, Ogun State, Nigeria
Adio T. Akinwale: Department of Computer Science, Federal University of Agriculture, P.M.B. 2240, Abeokuta 111101, Ogun State, Nigeria
Ti-Jesu Tunde-Adeleke: Department of Computer and Information Science, Covenant University, P.M.B. 1023, Ota 112104, Ogun State, Nigeria

Data, 2025, vol. 10, issue 6, 1-17

Abstract: The increasing volume of student assessments, particularly open-ended responses, presents a significant challenge for educators in ensuring grading accuracy, consistency, and efficiency. This paper presents a structured dataset designed for the development and evaluation of automated grading systems in higher education. The primary objective is to create a high-quality dataset that facilitates the development and evaluation of natural language processing (NLP) models for automated grading. The dataset comprises student responses to open-ended questions from the Management Information Systems (MIS221) and Project Management (MIS415) courses at Covenant University, collected during the 2022/2023 academic session. The responses were originally handwritten, scanned, and transcribed into Word documents. Each response is paired with corresponding scores assigned by human graders, following a detailed marking guide. To assess the dataset’s potential for automated grading applications, several machine learning and transformer-based models were tested, including TF-IDF with Linear Regression, TF-IDF with Cosine Similarity, BERT, SBERT, RoBERTa, and Longformer. The experimental results demonstrate that transformer-based models outperform traditional methods, with Longformer achieving the highest Spearman’s Correlation of 0.77 and the lowest Mean Squared Error (MSE) of 0.04, indicating a strong alignment between model predictions and human grading. The findings highlight the effectiveness of deep learning models in capturing the semantic and contextual meaning of both student responses and marking guides, making it possible to develop more scalable and reliable automated grading solutions. This dataset offers valuable insights into student performance and serves as a foundational resource for integrating educational technology into automated assessment systems. Future work will focus on enhancing grading consistency and expanding the dataset for broader academic applications.

Keywords: automated grading dataset; natural language processing; automated essay grading (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2306-5729/10/6/87/pdf (application/pdf)
https://www.mdpi.com/2306-5729/10/6/87/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jdataj:v:10:y:2025:i:6:p:87-:d:1673005

Access Statistics for this article

Data is currently edited by Ms. Cecilia Yang

More articles in Data from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().