This is an independent prediction project using data from this database: https://www.kaggle.com/competitions/linking-writing-processes-to-writing-quality/overview
Source: https://www.kaggle.com/code/alexia/kerasnlp-starter-notebook-writing-quality
Let's dicover the data which are composed of :
- events
- activities
- scores
Events
| Column | Definition |
|---|---|
| essay_id_comp | The unique ID of the essay |
| event_id | The index of the event, ordered chronologically |
| down_time | The time of the down event in milliseconds |
| up_time | The time of the up event in milliseconds |
| action_time | The duration of the event (the difference between down_time and up_time) |
| activity | The category of activity which the event belongs to |
| down_event | The name of the event when the key/mouse is pressed |
| up_event | The name of the event when the key/mouse is released |
| text_change | The text that changed as a result of the event (if any) |
| cursor_position | The character index of the text cursor after the event |
| word_count | The word count of the essay after the event |
Activities
| Activity Name | Definition |
|---|---|
| Nonproduction | The event does not alter the text in any way |
| Input | The event adds text to the essay |
| Remove/Cut | The event removes text from the essay |
| Paste | The event changes the text through a paste input |
| Replace | The event replaces a section of text with another string |
| Move From [x1, y1] To [x2, y2] | The event moves a section of text spanning character index x1, y1 to a new location x2, y2 |
Scores
| Column | Definition |
|---|---|
| essay_id_comp | The unique ID of the essay |
| score | The score the essay received out of 6 (the prediction target for the competition) |
The goal is to analyze key and mouse stroke patterns and predict an essay score assigned by graders, indicative of the writing's quality. I chose this project because:
- I'm interested in keystrokes as biometric verification & for verification of academic integrity, and this is a good way to familizarize myself with methods to analyze this kind of data
- I would like to work on prediction projects where I must generate features myself from raw data
The jupyter notebook contains sections of code organized into exploratory data analysis, rudimentary prediction, and a final pipeline that uses various machine learning models, followed by a concluding analysis of prediction accuracy from each of the methods. A pdf report will be uploaded from overleaf at the project's conclusion.