An Open-Source Automated Feedback System for STEM Education
ORAL
Abstract
In education, timely, personalized feedback is critical to improving student learning outcomes. This project introduces an open-source automated feedback system designed to support undergraduate students in STEM fields. By providing real-time, adaptive feedback our aim is to guide students through problem-solving processes and reinforcing correct approaches, ultimately fostering more effective, self-regulated learning throughout the course. The system aligns with curriculum goals, offering formative feedback immediately after problem-solving, along with integrated help materials to guide students through challenges. It features interactive dashboards for both students and instructors, providing real-time insights into performance and learning progress. Its flexible design allows instructors to customize content across a wide range of STEM topics, ensuring adaptability to diverse educational needs. Using the Technology Acceptance Model 2 (TAM2), we assessed the system's perceived usefulness and ease of use. This pilot study showed high user acceptance, highlighting the value of formative feedback in enhancing student engagement. In this presentation, we will discuss the system's design, TAM2 findings, and its potential to scale, offering an accessible solution for enhancing STEM education.
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Publication: Steinert, S., Krupp, L., Avila, K.E. et al. Lessons learned from designing an open-source automated feedback system for STEM education. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-13025-y
Presenters
Karina E Avila
RPTU Kaiserslautern-Landau
Authors
Karina E Avila
RPTU Kaiserslautern-Landau
Steffen Steinert
Ludwig-Maximilians-University Munich
Lars Krupp
German Research Center for Artificial Intelligence
Anke S Janssen
RPTU Kaiserslautern-Landau
Verena Ruf
Ludwig-Maximilians-University Munich
David Dzsotjan
Ludwig-Maximilians-University Munich
Christian De Schryver
RPTU Kaiserslautern-Landau
Jakob Karolus
German Research Center for Artificial Intelligence
Stefan Ruzika
RPTU Kaiserslautern-Landau
Karen Joisten
RPTU Kaiserslautern-Landau
Paul Lukowicz
German Research Center for Artificial Intelligence