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Persuade 2.0 Student Argumentation Data
Data Science and Analytics
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About
This collection of student-written texts originates from the Persuade 2.0 corpus. The data provides diverse argumentative essays, often focused on contemporary social issues. A significant portion of the material addresses the debate surrounding the capacity in which drivers should or should not use cell phones while operating a vehicle, examining the hazards of distracted driving, including texting and talking. The texts allow for the analysis of arguments related to safety, legislation, and consequences such as accidents, injuries, or fatalities.
Columns
- essay_id_comp: A unique identification code assigned to each essay entry.
- full_text: The actual written content of the student’s essay response.
- holistic_essay_score: A score indicating the overall evaluation of the essay quality.
- word_count: The quantifiable length of the essay text.
- prompt_name: Identifies the specific argumentative topic assigned to the student (e.g., “Phones and driving”).
- task assignment: Specifies the nature of the writing activity (e.g., "Independent").
- source_text: Contextual or reference material provided to the student prior to writing the essay.
- gender: The reported gender of the student author.
- grade_level: The academic grade level of the student.
- ell_status: Indicates the student's English Language Learner status.
- race_ethnicity: Demographic data detailing the student's race or ethnicity (e.g., Black/African American, White).
- economically_disadvantaged: Reports the economic status of the student.
- student_disability_status: Indicates whether the student has a reported disability.
Distribution
The dataset is available in CSV format. Each entry in the file represents a single student essay accompanied by its associated metadata fields. Specific record counts or file sizes are not documented in the available information.
Usage
This data is suitable for Natural Language Processing (NLP) tasks, such as training models to identify persuasive language, argument structure, and sentiment regarding safety regulations. It can be utilised by researchers in education to study the relationship between demographic factors (like race, gender, and economic status) and writing proficiency or assigned scores. Policy analysts can leverage the essays to gauge student attitudes toward traffic laws and technology usage in vehicles. Additionally, the data can support studies on reaction time, the legal ramifications of distracted driving, and the effectiveness of current legislative efforts, such as 'Hands Free' laws.
Coverage
The dataset encompasses essays covering a range of topics including cell phone use while driving, student activities, voting, and driverless cars. Demographic scope includes student characteristics such as grade level, gender, race/ethnicity, and disability status. Geographic context is broadly focused on American students and related state laws (e.g., Georgia). A defined time range for when the essays were authored is not detailed.
License
CC BY-NC-SA 4.0
Who Can Use It
- Linguists and Rhetoric Scholars: For analysing argumentative patterns and persuasive techniques used by students.
- Machine Learning Engineers: To create models that automatically score or classify persuasive essays.
- Traffic Safety Researchers: To understand human behaviour and perceived risks associated with operating electronic devices while driving.
- Education Technologists: To benchmark writing performance across different student groups and prompt styles.
Dataset Name Suggestions
- Persuade 2.0 Student Argumentation Data
- Essays on Distracted Driving and Safety
- Student Persuasive Writing Corpus
- Cell Phone Policy Debate Essays
Attributes
Original Data Source: Persuade 2.0 Student Argumentation Data
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