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The process of synthesizing solutions for mathematical problems is cognitively complex. Students formulate and implement strate- gies to solve mathematical problems, develop solutions, and make connections between their learned concepts as they apply their reasoning skills to solve such problems. The gaps in student knowl- edge or shallowly-learned concepts may cause students to guess at answers or otherwise apply the wrong approach, resulting in errors in their solutions. Despite the complexity of the synthesis process in mathematics learning, teachers’ knowledge and ability to anticipate areas of potential difficulty is essential and correlated with student learning outcomes. Preemptively identifying the common miscon- ceptions in students that result in subsequent incorrect attempts can be arduous and unreliable, even for experienced teachers. This pa- per aims to help teachers identify the subsequent incorrect attempts that commonly occur when students are working on math problems such that they can address the underlying gaps in knowledge and common misconceptions through feedback. We report on a longi- tudinal analysis of historical data, from a computer-based learning platform, exploring the incorrect answers in the prior school years (’15-’20) that establish the commonality of wrong answers on two Open Educational Resources (OER) curricula–Illustrative Math (IM) and EngageNY (ENY) for grades 6, 7, and 8. We observe that incor- rect answers are pervasive across 5 academic years despite changes in underlying student and teacher population. Building on our find- ings regarding the Common Wrong Answers (CWAs), we report on goals and task analysis that we leveraged in designing and develop- ing a crowdsourcing platform for teachers to write Common Wrong Answer Feedback (CWAF) aimed are remediating the underlying cause of the CWAs. Finally, we report on an in vivo study by analyz- ing the effectiveness of CWAFs using two approaches; first, we use next-problem-correctness as a dependent measure after receiving CWAF in an intent-to-treat second, using next-attempt correctness as a dependent measure after receiving CWAF in a treated analysis. With the rise in popularity and usage of computer-based learning platforms, this paper explores the potential benefits of scalability in identifying CWAs and the subsequent usage of crowd-sourced CWAFs in enhancing the student learning experience through re- mediation.
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Enhancing the Automatic Identification of Common Math Misconceptions Using Natural Language Processing
In order to facilitate student learning, it is important to identify and remediate misconceptions and incomplete knowledge pertaining to the assigned material. In the domain of mathematics, prior research with computer-based learning systems has utilized the commonality of incorrect answers to problems as a way of identifying potential misconceptions among students. Much of this research, however, has been limited to the use of close-ended questions, such as multiple-choice and fill-in-the-blank problems. In this study, we explore the potential usage of natural language processing and clustering methods to examine potential misconceptions across student answers to both close- and openended problems. We find that our proposed methods show promise for distinguishing misconception from non-conception, but may need further development to improve the interpretability of specific misunderstandings exhibited through student explanations.
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- Award ID(s):
- 1903304
- PAR ID:
- 10470436
- Publisher / Repository:
- In International Conference on Artificial Intelligence in Education
- Date Published:
- Page Range / eLocation ID:
- 302-307
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-031-36336-8_47
