International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12
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 Volume 12, Issue 10 (October 2025), Pages: 1-10

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 Original Research Paper

A deep learning model for automated marking of students’ assessments in a learning management system (LMS)

 Author(s): 

 Mohammed Altamimi 1, *, Yaser Altameemi 2, Adel Alkhalil 3, Romany F. Mansour 4, Magdy Abdelrhman 5, Ikhlaq Ahmed 6, Aakash Ahmad 7, Azizah Alogali 8, 9

 Affiliation(s):

  1Department of Information and Computer Science, College of Computer Science and Engineering, University of Ha’il, Ha’il 81481, Saudi Arabia
  2Department of English, College of Arts and Literature, University of Ha’il, Ha’il 81481, Saudi Arabia
  3Department of Software Engineering, College of Computer Science and Engineering, University of Ha’il, Ha’il 81481, Saudi Arabia
  4Department of Mathematics, Faculty of Science, New Valley University, El-Kharga 72511, Egypt
  5Applied College, University of Ha’il, Ha’il, Saudi Arabia
  6Department of Computer Software Engineering, National University of Sciences and Technology, Islamabad, Pakistan
  7School of Computing and Communications, Lancaster University Leipzig, Leipzig 04109, Germany
  8Department of Educational Leadership, University of Rochester, Rochester, NY 14627, USA
  9Department of Educational Leadership, University of Akron, Akron, OH 44325, USA

 Full text

    Full Text - PDF

 * Corresponding Author. 

   Corresponding author's ORCID profile:  https://orcid.org/0000-0002-4170-6910

 Digital Object Identifier (DOI)

  https://doi.org/10.21833/ijaas.2025.10.001

 Abstract

Learning Management Systems (LMSs) are widely used to support teaching and learning, with platforms such as Blackboard managing lectures, activities, assessments, and reports. Although LMSs provide useful tools and some automated feedback, the accuracy of evaluating students’ typed responses has received little attention in prior research. A particular issue arises in fill-in-the-gap questions, where answers are marked only if they exactly match the instructor’s input, often leading to unfair grading for minor spelling errors. To address this problem, we propose a model that integrates the Levenshtein edit distance with deep learning methods to identify and correct spelling errors, enabling fairer and more accurate automatic grading. The model demonstrated strong performance, achieving an average F1-measure of 0.938 on a dataset of misspelled words.

 © 2025 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Learning management systems, Automatic grading, Fill-in-the-gap questions, Spelling error correction, Deep learning

 Article history

 Received 12 April 2025, Received in revised form 20 August 2025, Accepted 30 August 2025

 Acknowledgment

This research was funded by the Scientific Research Deanship at the University of Ha’il, Saudi Arabia, through project number RG-21 149. 

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Altamimi M, Altameemi Y, Alkhalil A, Mansour RF, Abdelrhman M, Ahmed I, Ahmad A, and Alogali A (2025). A deep learning model for automated marking of students’ assessments in a learning management system (LMS). International Journal of Advanced and Applied Sciences, 12(10): 1-10

  Permanent Link to this page

 Figures

  Fig. 1  Fig. 2  Fig. 3 

 Tables

  Table 1  Table 2  Table 3  Table 4  Table 5

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