TY - GEN
T1 - Efficient Feedback and Partial Credit Grading for Proof Blocks Problems
AU - Poulsen, Seth
AU - Kulkarni, Shubhang
AU - Herman, Geoffrey
AU - West, Matthew
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Proof Blocks is a software tool that allows students to practice writing mathematical proofs by dragging and dropping lines instead of writing proofs from scratch. Proof Blocks offers the capability of assigning partial credit and providing solution quality feedback to students. This is done by computing the edit distance from a student’s submission to some predefined set of solutions. In this work, we propose an algorithm for the edit distance problem that significantly outperforms the baseline procedure of exhaustively enumerating over the entire search space. Our algorithm relies on a reduction to the minimum vertex cover problem. We benchmark our algorithm on thousands of student submissions from multiple courses, showing that the baseline algorithm is intractable, and that our proposed algorithm is critical to enable classroom deployment. Our new algorithm has also been used for problems in many other domains where the solution space can be modeled as a DAG, including but not limited to Parsons Problems for writing code, helping students understand packet ordering in networking protocols, and helping students sketch solution steps for physics problems. Integrated into multiple learning management systems, the algorithm serves thousands of students each year.
AB - Proof Blocks is a software tool that allows students to practice writing mathematical proofs by dragging and dropping lines instead of writing proofs from scratch. Proof Blocks offers the capability of assigning partial credit and providing solution quality feedback to students. This is done by computing the edit distance from a student’s submission to some predefined set of solutions. In this work, we propose an algorithm for the edit distance problem that significantly outperforms the baseline procedure of exhaustively enumerating over the entire search space. Our algorithm relies on a reduction to the minimum vertex cover problem. We benchmark our algorithm on thousands of student submissions from multiple courses, showing that the baseline algorithm is intractable, and that our proposed algorithm is critical to enable classroom deployment. Our new algorithm has also been used for problems in many other domains where the solution space can be modeled as a DAG, including but not limited to Parsons Problems for writing code, helping students understand packet ordering in networking protocols, and helping students sketch solution steps for physics problems. Integrated into multiple learning management systems, the algorithm serves thousands of students each year.
KW - Automated feedback
KW - Mathematical proofs
KW - Scaffolding
UR - http://www.scopus.com/inward/record.url?scp=85164915007&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85164915007&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-36272-9_41
DO - 10.1007/978-3-031-36272-9_41
M3 - Conference contribution
AN - SCOPUS:85164915007
SN - 9783031362712
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 502
EP - 514
BT - Artificial Intelligence in Education - 24th International Conference, AIED 2023, Proceedings
A2 - Wang, Ning
A2 - Rebolledo-Mendez, Genaro
A2 - Matsuda, Noboru
A2 - Santos, Olga C.
A2 - Dimitrova, Vania
PB - Springer
T2 - 24th International Conference on Artificial Intelligence in Education, AIED 2023
Y2 - 3 July 2023 through 7 July 2023
ER -