How Can Automatic Feedback Help Students Construct Automata?
- Loris D’Antoni ,
- Dileep Kini ,
- Rajeev Alur ,
- Sumit Gulwani ,
- Mahesh Viswanathan ,
- Björn Hartmann
ACM Transactions on Computer-Human Interaction (TOCHI) - Special Issue on Online Learning at Scale | , Vol 22(2)
In computer-aided education, the goal of automatic feedback is to provide a meaningful explanation of students’ mistakes. We focus on providing feedback for constructing a deterministic finite automaton (DFA) that accepts strings that match a described pattern. Natural choices for feedback are binary feedback (correct/wrong) and a counterexample of a string that is processed incorrectly. Such feedback is easy to compute but might not provide the student enough help. Our first contribution is a novel way to automatically compute alternative conceptual hints. Our second contribution is a rigorous evaluation of feedback with 377 students. We find that providing either counterexamples or hints is judged as helpful, increases student perseverance, and can improve problem completion time. However, both strategies have particular strengths and weaknesses. Since our feedback is completely automatic it can be deployed at scale and integrated into existing MOOCs.
This research was supported by the NSF Expeditions in Computing award CCF 1138996. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 (212) 869-0481, or permissions@acm.org. © 2015 ACM