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 - Acknowledgements. Babette Bühler is a doctoral candidate and supported by the LEAD Graduate School and Research Network, which is funded by the Ministry of Science, Research and the Arts of the state of Baden-Württemberg within the frame-work of the sustainability funding for the projects of the Excellence Initiative II. Efe Bozkir and Enkelejda Kasneci acknowledge the funding by the DFG with EXC number 2064/1 and project number 390727645. This work is also supported by Leibniz-WissenschaftsCampus Tübingen “Cognitive Interfaces” by a grant to Ulrich Trautwein, Peter Gerjets, and Enkelejda Kasneci. We thank Katrin Kunz and Jan Thiele for their excellent assistance.
Acknowledgements. The research reported here was supported by National Science Foundation Grant No. 2016966 and No.1623702 to North Carolina State University.
This work was funded by NSF grant #1824257.
Acknowledgements. This work is also partially supported by the European
Acknowledgments. This work was financially supported by Base Funding - UIDB/00027/2020 of the Artificial Intelligence and Computer Science Laboratory - LIACC - funded by national funds through the FCT/MCTES (PIDDAC). Bernardo Leite is supported by a PhD studentship (with reference 2021.05432.BD), funded by Funda¸cão para a Ciência e a Tecnologia (FCT).
Acknowledgements. The authors thank the NSF (grants 1917713, 2118706, 2202506,
Acknowledgment. This research was supported by Basic Science Research Program through NRF of Korea funded by the Ministry of Education (2022R1A6A1A0305295412). This research is part of the master’s thesis of the first author at Ewha Womans University.
Acknowledgements. This work has been supported by the Natural Science Foundation of China (No. 62277045) and the Natural Science Foundation of Shandong Province (No. ZR 2021MF011).
Acknowledgement. This work was supported by UME Academy Ltd. academy) and Mitacs (Grant #IT13336).
Acknowledgments. This material is based upon work supported by the National Defense Education Program (NDEP) for Science, Technology, Engineering, and Mathematics (STEM) Education, Outreach, and Workforce Initiative Programs under Grant
Acknowledgements. Hannah Deininger and Leona Colling are doctoral students at the LEAD Graduate School & Research Network, which is funded by the Ministry of Science, Research and the Arts of the state of Baden-Württemberg. Furthermore, this research was funded by the German Federal Ministry of Research and Education (BMBF; 01JD1905A and 01JD1905B). It was additionally supported by Dr. Kou Murayama’s Humboldt Professorship (Alexander von Humboldt Foundation) for the employment of Rosa Lavelle-Hill. Ines Pieronczyk is now affiliated with the DLR Pro-jektträger Bonn.
This work was supported by JSPS KAKENHI Grant Numbers 19H05663, 20K20817, and 21H00898.
Acknowledgement. The study was funded by National Science Foundation (NSF) (Award # 2101104, 2138854, PI: Zhai). Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF.
This research was supported by the NSF Grant 2013502.
Acknowledgements. This material is based upon work supported by an NSF Graduate Research Fellowship (DGE-1842213; Amy Adair) and the U.S. Department of Education Institute of Education Sciences (R305A210432; Janice Gobert & Michael Sao Pedro). Any opinions, findings, and conclusions or recommendations expressed are those of the author(s) and do not necessarily reflect the views of either organization.
Acknowledgements. This work was supported in part by grants from Amii; a Canada CIFAR AI Chair, Amii; Compute Canada; Huawei; Mitacs; and NSERC.
Acknowledgments. This research was supported by the NSF National AI Institute for Student-AI Teaming (iSAT) under grant DRL 2019805. The opinions expressed are those of the authors and do not represent views of the NSF.
Acknowledgements. This work was supported by Hong Kong Research Grants Council, University Grants Committee (Grant No.: 17605221), and by the Innovation and Technology Commission of the Government of the HKSAR (Grant No.: ITB/FBL/7026/20/P).
Acknowledgements. The authors thank the NSF (under grants 1917713, 2118706,
Acknowledgements. This work was supported by NSF Award #DRL-2201796. The opinions expressed are those of the authors and do not represent the views of NSF.
Commission-funded project “Humane AI: Toward AI Systems That Augment and Empower Humans by Understanding Us, our Society and the World Around Us” (grant 820437), EU Erasmus+ project 621586-EPP-1-2020-1-NO-EPPKA2-KA and the EPSRC Fellowship “Task Based Information Retrieval” (grant EP/P024289/1). This research is conducted as part of the X5GON project (www.x5gon.org) funded by the EU’s Horizon 2020 grant No 761758.
This research was funded by an NSERC discovery grant.
Acknowledgements. The authors thank the NSF (under grants 1917713, 2118706, 2202506, 2215193) for partially supporting this work.
Acknowledgements. The research described herein has been sponsored by the U.S. Army DEV-COM, Soldier Center under cooperative agreement W912CG-19-2-0001. The statements and opinions expressed in this article do not necessarily reflect the position or the policy of the United States Government, and no official endorsement should be inferred.
Acknowledgements. This work was supported by the Ministry of Research, Innovation, and Digitalization, project CloudPrecis, Contract Number 344/390020/ 06.09.2021, MySMIS code: 124812, within POC, the Institute of Education Sciences (NSF R305A130124, R305A190063), the U.S. Department of Education, and the National Science Foundation (NSF REC0241144; IIS-0735682).
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 -