Harnessing Machine Learning for Educational Assessments: A Data-Driven Approach
Year: 2025
Type: conference-paper
Abstract: The poster explores the integration of machine learning with educational assessments. It covers various assessment types, such as multiple-choice questions (MCQs) and true/false questions, and explains how machine learning processes these data types. The poster delves into data acquisition, preprocessing, model training, evaluation, and testing. By using practical examples, the poster will provide the audiences with a comprehensive understanding of how machine learning models can be trained on binary and categorical data for MCQs and true/false questions. This knowledge will empower them to appreciate the underlying processes. By understanding the intricacies of data types and machine learning, participants will be better equipped to leverage AI effectively in their respective fields.
Author: Adeel, Muhammad
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7112322372
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