ravikumarmn/Feedback-Prize---Predicting-Effective-Arguments

Goal of the Competition The goal of this competition is to classify argumentative elements in student writing as "effective," "adequate," or "ineffective." You will create a model trained on data that is representative of the 6th-12th grade population in the United States in order to minimize bias. Models derived from this competition will help pave the way for students to receive enhanced feedback on their argumentative writing. With automated guidance, students can complete more assignments and ultimately become more confident, proficient writers. This competition will comprise two tracks. The first track will be a traditional track in which accuracy of classification will be the only metric used for success. Success on this track will be updated on the Kaggle leaderboard. Prize money for the accuracy-only, “Leaderboard Prize” track will be $25,000. The second track will measure computational efficiency in which efficiency is determined using a combination of accuracy and the speed at which models are able to generate these predictions. We are hosting this track because highly accurate models are often computationally heavy. Such models have a stronger carbon footprint and frequently prove difficult to utilize in real-world educational contexts, since most educational organizations have limited computational capabilities. Weekly updates on models based on computational efficiency will be posted in the discussion forum. Prize money for the computational, “Efficiency Prize” track will be $30,000.

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