TY - GEN
T1 - Automatic Formative Assessment in Computer Science
T2 - 44th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2020
AU - Marchisio, Marina
AU - Margaria, Tiziana
AU - Sacchet, Matteo
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - Adaptive online learning can facilitate students' support by responding immediately to the user's interactions. Good feedback to students helps closing the gap between actual and desired performance. In this paper we analyze how to introduce online adaptive formative learning in Computer Science, a discipline with well documented challenges that are hard to tackle with traditional classroom methods. Specifically, we developed illustrative learning items teaching Model-Driven Design and implemented them in an online system that implements a model for automatic formative assessment developed by University of Torino. The model takes advantage of an automatic assessment system initially designed for STEM disciplines, then adopted for teaching languages and other disciplines too. The key features of the adaptive model supported by the online system are algorithmic questions, availability, contextualization, immediate feedback, interactive feedback, and open answers. These features are portable across subject domains, so the system can be adapted to include new subjects. We chose MDD because it is a topic of Computer Science education connected with Computational Thinking, software design, and formal methods, which are three of the core areas in need of enhanced support.
AB - Adaptive online learning can facilitate students' support by responding immediately to the user's interactions. Good feedback to students helps closing the gap between actual and desired performance. In this paper we analyze how to introduce online adaptive formative learning in Computer Science, a discipline with well documented challenges that are hard to tackle with traditional classroom methods. Specifically, we developed illustrative learning items teaching Model-Driven Design and implemented them in an online system that implements a model for automatic formative assessment developed by University of Torino. The model takes advantage of an automatic assessment system initially designed for STEM disciplines, then adopted for teaching languages and other disciplines too. The key features of the adaptive model supported by the online system are algorithmic questions, availability, contextualization, immediate feedback, interactive feedback, and open answers. These features are portable across subject domains, so the system can be adapted to include new subjects. We chose MDD because it is a topic of Computer Science education connected with Computational Thinking, software design, and formal methods, which are three of the core areas in need of enhanced support.
KW - adaptive assessment
KW - automatic assessment
KW - computational thinking
KW - Computer Science education
KW - DIME
KW - formative assessment
KW - interactive feedback
KW - model checking
KW - model driven design
UR - http://www.scopus.com/inward/record.url?scp=85094096602&partnerID=8YFLogxK
U2 - 10.1109/COMPSAC48688.2020.00035
DO - 10.1109/COMPSAC48688.2020.00035
M3 - Conference contribution
AN - SCOPUS:85094096602
T3 - Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020
SP - 201
EP - 206
BT - Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020
A2 - Chan, W. K.
A2 - Claycomb, Bill
A2 - Takakura, Hiroki
A2 - Yang, Ji-Jiang
A2 - Teranishi, Yuuichi
A2 - Towey, Dave
A2 - Segura, Sergio
A2 - Shahriar, Hossain
A2 - Reisman, Sorel
A2 - Ahamed, Sheikh Iqbal
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 13 July 2020 through 17 July 2020
ER -