Harnessing Machine Learning for Educational Assessments: A Data-Driven Approach

Contributor

University of Central Florida. Faculty Center for Teaching and Learning; University of Central Florida. Division of Digital Learning; Teaching and Learning with AI Conference (2025 : Orlando, Fla.)

Location

Universal Center

Start Date

29-5-2025 4:00 PM

End Date

29-5-2025 5:00 PM

Publisher

University of Central Florida Libraries

Keywords:

Educational assessments; Data preprocessing; Model training; Machine learning applications; Evaluation techniques

Subjects

Machine learning--Study and teaching; Machine learning--Evaluation; Artificial intelligence--Educational applications; Learning--Evaluation; Educational tests and measurements--Data processing

Description

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.

Language

eng

Type

Poster

Rights Statement

All Rights Reserved

Audience

Faculty; Students

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May 29th, 4:00 PM May 29th, 5:00 PM

Harnessing Machine Learning for Educational Assessments: A Data-Driven Approach

Universal Center

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.