A Diagnostic Question is a multiple-choice question with four options: one correct answer and three distractors (incorrect answers). Each distractor is carefully crafted to capture a specific misconception. For example:
If a student selects the distractor "13," they may have the misconception "Carries out operations from left to right regardless of priority order."
Tagging distractors with appropriate misconceptions is essential but time-consuming, and it is difficult to maintain consistency across multiple human labellers. Misconceptions vary significantly in terms of description granularity, and new misconceptions are often discovered as human labellers tag distractors in new topic areas.
Initial efforts to use pre-trained language models have not been successful, likely due to the complexity of the mathematical content in the questions. Therefore, a more efficient and consistent approach is needed to streamline the tagging process and enhance the overall quality.
This competition challenges you to develop a Natural Language Processing (NLP) model driven by Machine Learning (ML) that predicts the affinity between misconceptions and distractors. The goal is to create a model that not only aligns with known misconceptions but also generalizes to new, emerging misconceptions. Such a model would assist human labelers in accurately selecting suitable misconceptions from both existing and newly identified options.
Your work could help improve the understanding and management of misconceptions, enhancing the educational experience for both students and teachers.
Eedi, alongside Vanderbilt University, and together with The Learning Agency Lab, an independent nonprofit based in Arizona, have collaborated with Kaggle on this competition.
### Acknowledgments
Eedi, Vanderbilt University and the Learning Agency Lab would like to thank the Bill & Melinda Gates Foundation, Schmidt Futures, and Chan Zuckerberg Initiative for their support in making this work possible.
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