Authors : Winarsih; Adang Suhendra; Ana Kurniawati

Volume/Issue : Volume 9 - 2024, Issue 5 - May

Google Scholar : https://tinyurl.com/3bp9mka8

Scribd : https://tinyurl.com/3bmjvbbp

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAY200

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Automatic Essay Scoring (AES) with context- based analysis with cohesion and coherence aims to develop a model that can assess essays automatically or by translating language diversity and student understanding. AES with context-based analysis using methods and applications based on Natural Language Processing (NLP) and the Machine Learning Framework (MLF), apart from being able to provide essay answers automatically, can also assess student understanding. Student or student understanding is the value obtained from answering questions according to the level of understanding that comes from the answer. By using the concepts of cohesion and coherence in the essay assessment system, the teacher can assess the quality of the answers obtained. The context-based essay assessment system was built to facilitate and speed up the process of assessing essay exam answers, to obtain standards and consistency in essay assessment according to the diversity of answers and the diversity of assessors if they have more than one subject. An essay exam is a learning evaluation given in the form of essay questions which have more varied answers than multiple choice questions. These variations in answers create difficulties for lecturers or teaching staff in assessing answers.

Keywords : AES; Cohesion ; Coheresion; NLP; Machine Learning.

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