Understanding Machine Learning with Different Types of Educational Assessments
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
Gold Coast III/IV
Start Date
28-5-2025 3:30 PM
End Date
28-5-2025 3:55 PM
Publisher
University of Central Florida Libraries
Keywords:
Machine learning; Educational assessments; Data preprocessing; Algorithm selection; AI applications
Subjects
Machine learning--Study and teaching; Machine learning--Evaluation; Machine learning--Experiments; Artificial intelligence--Educational applications; Multiple-choice examinations--Data processing
Description
This presentation explores how machine learning models can be trained on binary and categorical data, using examples of multiplechoice questions (MCQs) and true/false questions. It covers the process of acquiring and preprocessing data, followed by training, evaluating, and testing the models. For MCQs, the data type is categorical, while for true/false questions, it is binary. The discussion highlights how these data types influence algorithm selection and the overall modeling process. The goal is to provide participants with a comprehensive understanding of these processes, empowering them to leverage AI effectively in their fields.
Language
eng
Type
Presentation
Rights Statement
All Rights Reserved
Audience
Faculty, Students, Instructional designers
Recommended Citation
Adeel, Muhammad, "Understanding Machine Learning with Different Types of Educational Assessments" (2025). Teaching and Learning with AI Conference Presentations. 72.
https://stars.library.ucf.edu/teachwithai/2025/wednesday/72
Understanding Machine Learning with Different Types of Educational Assessments
Gold Coast III/IV
This presentation explores how machine learning models can be trained on binary and categorical data, using examples of multiplechoice questions (MCQs) and true/false questions. It covers the process of acquiring and preprocessing data, followed by training, evaluating, and testing the models. For MCQs, the data type is categorical, while for true/false questions, it is binary. The discussion highlights how these data types influence algorithm selection and the overall modeling process. The goal is to provide participants with a comprehensive understanding of these processes, empowering them to leverage AI effectively in their fields.
Accessibility Statement
This item was created or digitized prior to April 24, 2027, or is a reproduction of legacy media created before that date. It is preserved in its original, unmodified state specifically for research, reference, or historical recordkeeping. In accordance with the ADA Title II Final Rule, the University Libraries provides accessible versions of archival materials upon request. To request an accommodation for this item, please submit an accessibility request form.