Automatic Diagnosis of Students' Misconceptions in K-8 Mathematics
Authors
K-8 mathematics students must learn many procedures, such as addition and subtraction. Students frequently learn "buggy'' variations of these procedures, which we ideally could identify automatically. This is challenging because there are many possible variations that reflect deep compositions of procedural thought. Existing approaches for K-8 math use manually specified variations which do not scale to new math algorithms or previously unseen misconceptions. Our system examines students' answers and infers how they incorrectly combine basic skills into complex procedures. We evaluate this approach on data from approximately 300 students. Our system replicates 86% of the answers that contain clear systematic mistakes (13%). Investigating further, we found 77% at least partially replicate a known misconception, with 53% matching exactly. We also present data from 29 participants showing that our system can demonstrate inferred incorrect procedures to an educator as successfully as a human expert.
Research Questions / Practical Problems
Question signals indexed for this paper.
- 83%
Starting From Scratch Again and Again: Tracing the Origins of High Schoolers’ Negative Perceptions of Block-Based Programming
CHI '26· Programming Education & Computational Thinking +2
- 80%
Improving Instruction of Programming Patterns with Faded Parsons Problems
CHI '21· Programming Education & Computational Thinking +1
- 80%
The Elephant in the Syntax: A Comparative Study of Semantics‑First, Block‑Based, and Textual Programming
CHI '26· Programming Education & Computational Thinking +1
- 67%
Investigating the Impact of a Real-time, Multimodal Student Engagement Analytics Technology in Authentic Classrooms
CHI '19· Programming Education & Computational Thinking +2
- 67%
Exploring the Potential of an Intelligent Tutoring System for Sketching Fundamentals
CHI '20· Programming Education & Computational Thinking +1
- 67%
Engaging Teachers to Co-Design Integrated AI Curriculum for K-12 Classrooms
CHI '21· Programming Education & Computational Thinking +2
- 67%
“Even Though I Went Through Everything, I Didn’t Feel Like I Learned a Lot”: Insights From Experiences of Non-Computer Science Students Learning to Code
CHI '25· Programming Education & Computational Thinking +1
- 67%
"Let’s talk about data": Co-Designing Critical Data Literacy Tools for K-12 Education through Dialogic Learning
CHI '26· Programming Education & Computational Thinking +2
- 60%
AlgoSolve: Supporting Subgoal Learning in Algorithmic Problem-Solving with Learnersourced Microtasks
CHI '22· Programming Education & Computational Thinking +1
Based on Jaccard similarity of research subtopics & professions (≥60%)