{"id":18096599,"url":"https://github.com/andreaschandra/feedback-prize-effectiveness","last_synced_at":"2025-04-06T03:26:44.228Z","repository":{"id":115162925,"uuid":"502240052","full_name":"andreaschandra/feedback-prize-effectiveness","owner":"andreaschandra","description":null,"archived":false,"fork":false,"pushed_at":"2022-07-22T16:21:00.000Z","size":88,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-02-12T09:49:31.091Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/andreaschandra.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-06-11T03:29:42.000Z","updated_at":"2022-10-07T15:40:53.000Z","dependencies_parsed_at":"2023-05-17T01:30:23.614Z","dependency_job_id":null,"html_url":"https://github.com/andreaschandra/feedback-prize-effectiveness","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andreaschandra%2Ffeedback-prize-effectiveness","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andreaschandra%2Ffeedback-prize-effectiveness/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andreaschandra%2Ffeedback-prize-effectiveness/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andreaschandra%2Ffeedback-prize-effectiveness/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/andreaschandra","download_url":"https://codeload.github.com/andreaschandra/feedback-prize-effectiveness/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247429959,"owners_count":20937779,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-10-31T19:14:49.492Z","updated_at":"2025-04-06T03:26:44.222Z","avatar_url":"https://github.com/andreaschandra.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# feedback-prize-effectiveness\n\n## Goal of the competition\nThe 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.\n\nThis 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.\n\nThe 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.\n\nYou can find more details about the Efficiency Prize Evaluation via the side tab.\n\n## Context\nWriting is crucial for success. In particular, argumentative writing fosters critical thinking and civic engagement skills, and can be strengthened by practice. However, only 13 percent of eighth-grade teachers ask their students to write persuasively each week. Additionally, resource constraints disproportionately impact Black and Hispanic students, so they are more likely to write at the “below basic” level as compared to their white peers. An automated feedback tool is one way to make it easier for teachers to grade writing tasks assigned to their students that will also improve their writing skills.\n\nThere are numerous automated writing feedback tools currently available, but they all have limitations, especially with argumentative writing. Existing tools often fail to evaluate the quality of argumentative elements, such as organization, evidence, and idea development. Most importantly, many of these writing tools are inaccessible to educators due to their cost, which most impacts already underserved schools.\n\nGeorgia State University (GSU) is an undergraduate and graduate urban public research institution in Atlanta. U.S. News \u0026 World Report ranked GSU as one of the most innovative universities in the nation. GSU awards more bachelor’s degrees to African-Americans than any other non-profit college or university in the country. GSU and The Learning Agency Lab, an independent nonprofit based in Arizona, are focused on developing science of learning-based tools and programs for social good.\n\nTo best prepare all students, GSU and The Learning Agency Lab have joined forces to encourage data scientists to improve automated writing assessments. This public effort could also encourage higher quality and more accessible automated writing tools. If successful, students will receive more feedback on the argumentative elements of their writing and will apply the skill across many disciplines.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fandreaschandra%2Ffeedback-prize-effectiveness","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fandreaschandra%2Ffeedback-prize-effectiveness","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fandreaschandra%2Ffeedback-prize-effectiveness/lists"}