TY - GEN
T1 - A Dashboard to Support Teachers During Students’ Self-paced AI-Supported Problem-Solving Practice
AU - Aleven, Vincent
AU - Blankestijn, Jori
AU - Lawrence, Lu Etta Mae
AU - Nagashima, Tomohiro
AU - Taatgen, Niels
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022/9/5
Y1 - 2022/9/5
N2 - Past research has yielded ample knowledge regarding the design of analytics-based tools for teachers and has found beneficial effects of several tools on teaching and learning. Yet there is relatively little knowledge regarding the design of tools that support teachers when a class of students uses AI-based tutoring software for self-paced learning. To address this challenge, we conducted design-based research with 20 middle school teachers to create a novel real-time dashboard, Tutti, that helps a teacher monitor a class and decide which individual students to help, based on analytics from students’ tutoring software. Tutti is fully implemented and has been honed through prototyping and log replay sessions. A partial implementation was piloted in remote classrooms. Key design features are a two-screen design with (1) a class overview screen showing the status of each student as well as notifications of recent events, and (2) a deep dive screen to explore an individual student's work in detail, with both dynamic replay and an interactive annotated solution view. The project yields new insight into effective designs for a real-time analytics-based tool that may guide the design of other tools for K-12 teachers to support students in self-paced learning activities.
AB - Past research has yielded ample knowledge regarding the design of analytics-based tools for teachers and has found beneficial effects of several tools on teaching and learning. Yet there is relatively little knowledge regarding the design of tools that support teachers when a class of students uses AI-based tutoring software for self-paced learning. To address this challenge, we conducted design-based research with 20 middle school teachers to create a novel real-time dashboard, Tutti, that helps a teacher monitor a class and decide which individual students to help, based on analytics from students’ tutoring software. Tutti is fully implemented and has been honed through prototyping and log replay sessions. A partial implementation was piloted in remote classrooms. Key design features are a two-screen design with (1) a class overview screen showing the status of each student as well as notifications of recent events, and (2) a deep dive screen to explore an individual student's work in detail, with both dynamic replay and an interactive annotated solution view. The project yields new insight into effective designs for a real-time analytics-based tool that may guide the design of other tools for K-12 teachers to support students in self-paced learning activities.
KW - AI-based tutoring software
KW - Problem-solving practice
KW - Teacher dashboards
UR - http://www.scopus.com/inward/record.url?scp=85137991235&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-16290-9_2
DO - 10.1007/978-3-031-16290-9_2
M3 - Conference contribution
AN - SCOPUS:85137991235
SN - 978-3-031-16289-3
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 16
EP - 30
BT - Educating for a New Future
A2 - Hilliger, Isabel
A2 - Muñoz-Merino, Pedro J.
A2 - De Laet, Tinne
A2 - Ortega-Arranz, Alejandro
A2 - Farrell, Tracie
PB - Springer
T2 - 17th European Conference on Technology Enhanced Learning, EC-TEL 2022
Y2 - 12 September 2022 through 16 September 2022
ER -