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Anthony F. Botelho - University of Florida. Gainesville, FL, US

Anthony F. Botelho

Assistant Professor | University of Florida

Gainesville, FL, UNITED STATES

Anthony Botelho's researches the development and application of educational technology.


Anthony Botelho is an assistant professor of educational technology in the School of Teaching and Learning at the University of Florida's College of Education. He is an expert in developing and deploying technology to study student learning across educational contexts. His research combines theory, methodologies and application across fields including education, cognitive psychology, artificial intelligence and computer science. Anthony has leveraged quantitative methodologies, including learning analytics, machine learning and natural language processing to study cognitive, behavioral, and affective constructs of learning in online and classroom settings. He also engages in interdisciplinary collaboration with educators and researchers across disciplines to design, develop, and refine human-in-the-loop systems and methodologies to advance instructional practices and our understanding of how students learn.

Areas of Expertise (7)

Behavioral Psychology

Educational Technology

Machine Leearning

Qualitative Methods

Artificial Intelligence

Natural Language Processing

Cognitive Psychology


Articles (3)

The automated grading of student open responses in mathematics

Association for Computing Machinery

John A. Erickson , et al.


The use of computer-based systems in classrooms has provided teachers with new opportunities in delivering content to students, supplementing instruction, and assessing student knowledge and comprehension. Among the largest benefits of these systems is their ability to provide students with feedback on their work and also report student performance and progress to their teacher. While computer-based systems can automatically assess student answers to a range of question types, a limitation faced by many systems is in regard to open-ended problems.

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Developing Early Detectors of Student Attrition and Wheel Spinning Using Deep Learning

Journal of IEEE Transactions on Learning Technologies

Anthony F. Botelho, et al.


The increased usage of computer-based learning platforms and online tools in classrooms presents new opportunities to not only study the underlying constructs involved in the learning process, but also use this information to identify and aid struggling students. Many learning platforms, particularly those driving or supplementing instruction, are only able to provide aid to students who interact with the system. With this in mind, student persistence emerges as a prominent learning construct contributing to students success when learning new material.

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Refusing to Try: Characterizing Early Stopout on Student Assignments

Association for Computing Machinery

Anthony F. Botelho, et al.


A prominent issue faced by the education research community is that of student attrition. While large research efforts have been devoted to studying course-level attrition, widely referred to as dropout, less research has been focused on finer-grained assignment-level attrition commonly observed in K-12 classrooms. This later instantiation of attrition, referred to in this paper as "stopout," is characterized by students failing to complete their assigned work, but the cause of such behavior are not often known.

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