Year: 2023
Type: conference-paper
Abstract: To provide an education experience tailored to students’ needs, instructors’ awareness of the students’ challenges is essential. Course evaluations or intermittent feedback prompts can help facilitate this awareness and provide students an avenue of self-expression. My work, however, focuses on the analysis of student feedback to extract students’ emotional states as related to learning from periodic written student reflections. Emotion detection of students is a hot topic in education and has been explored through various perspectives, whether to predict student performance or attrition, to identify students for academic intervention. My work explores the use of emotionally-charged reflections to supplement traditional means of analysis and provide instructors with context for students’ behavior to help them develop such interventions. To address this vision, I present a framework to analyze reflections over time (ROTF) and investigate ways to convey and analyze reflections using text-processing and machine learning.
Author: Sandra Wiktor
Institution: University of North Carolina at Charlotte
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: bronze
OpenAlex ID: W4386737652