Online Course Dropout Reason Tracking
This dataset provides detailed records of student dropout events from online courses, including coded reasons, timestamps, and anonymized demographic information. It enables comprehensive analysis of dropout patterns, supports targeted retention strategies, and facilitates research into factors affecting digital education completion rates.
Sample rows
preview · 8 of 200 rows · all 16 columns| dropout_event_idstring | dropout_reason_codestring | student_ageinteger | student_countrystring | student_idstring | course_idstring | dropout_datetimedatetime | student_genderstring | student_ethnicitystring | student_education_levelstring | course_titlestring | course_categorystring | course_start_datedate | course_end_datedate | course_delivery_modestring | dropout_reason_textstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| DE0001 | academic | 19 | India | S1001 | C2001 | 2024-03-12T15:42:00 | female | Asian | bachelor | Intro to Data Science | Technology | 2024-01-15 | 2024-04-15 | self-paced | Struggled to keep up with course material and assignments. |
| DE0002 | technical | 22 | USA | S1002 | C2002 | 2024-02-22T11:15:00 | male | White | associate | Cloud Computing Essentials | Technology | 2024-01-10 | 2024-03-30 | self-paced | Repeated platform outages disrupted my studies. |
| DE0003 | personal | 37 | Mexico | S1003 | C2003 | 2024-04-02T18:40:00 | nonbinary | Latinx | bachelor | Business Communication Skills | Business | 2024-02-01 | 2024-05-01 | hybrid | Family emergency required me to pause my studies. |
| DE0004 | workload | 29 | Nigeria | S1004 | C2004 | 2024-05-10T10:30:00 | male | Black | master | Financial Analysis for Managers | Finance | 2024-03-10 | 2024-06-10 | hybrid | Unable to balance workload with job responsibilities. |
| DE0005 | financial | 42 | Germany | S1005 | C2005 | 2024-02-14T14:10:00 | female | White | master | Advanced Marketing Strategies | Business | 2024-01-10 | 2024-04-10 | hybrid | Could not afford the course fees after recent expenses. |
| DE0006 | lack_of_interest | 17 | Brazil | S1006 | C2006 | 2024-03-28T09:05:00 | female | Hispanic | high_school | Creative Writing for Teens | Art | 2024-02-01 | 2024-04-30 | instructor-led | Lost motivation as the course content felt irrelevant. |
| DE0007 | health | 53 | UAE | S1007 | C2007 | 2024-05-03T13:20:00 | other | Middle Eastern | doctorate | Modern Art Appreciation | Art | 2024-03-01 | 2024-06-01 | instructor-led | blank |
| DE0008 | academic | 16 | China | S1008 | C2008 | 2024-04-18T16:45:00 | male | Asian | high_school | Mathematics Olympiad Prep | Mathematics | 2024-03-01 | 2024-05-15 | self-paced | Difficulty understanding advanced mathematics concepts. |
| DE0009 | technical | 35 | South Africa | S1009 | C2009 | 2024-03-23T10:15:00 | female | Black | bachelor | Introduction to Programming | Technology | 2024-02-05 | 2024-05-05 | self-paced | Course videos frequently failed to load. |
| DE0010 | academic | 14 | Russia | S1010 | C2010 | 2024-04-29T19:10:00 | male | White | high_school | Robotics for Kids | Technology | 2024-02-25 | 2024-05-30 | other | Course content was too advanced for my current level. |
| DE0011 | personal | 25 | Japan | S1011 | C2011 | 2024-05-22T09:35:00 | prefer_not_to_say | East Asian | bachelor | Japanese Language for Beginners | Language | 2024-03-20 | 2024-06-20 | instructor-led | Moved to a new city and unable to continue. |
| DE0012 | workload | 31 | USA | S1012 | C2012 | 2024-03-11T12:30:00 | female | White | master | Machine Learning Fundamentals | Technology | 2024-01-20 | 2024-04-20 | self-paced | Too many assignments to manage alongside work. |
| DE0013 | lack_of_interest | 18 | UK | S1013 | C2013 | 2024-03-21T17:10:00 | female | White | high_school | Introduction to Visual Arts | Art | 2024-02-10 | 2024-04-10 | instructor-led | Lost interest in the subject matter. |
| DE0014 | financial | 24 | India | S1014 | C2014 | 2024-04-15T11:50:00 | male | Asian | bachelor | Business Analytics in Practice | Business | 2024-02-15 | 2024-05-15 | hybrid | Unexpected expenses forced me to drop the course. |
| DE0015 | technical | 40 | Nigeria | S1015 | C2015 | 2024-03-30T07:25:00 | male | Black | master | Advanced Excel for Business | Business | 2024-02-01 | 2024-05-01 | hybrid | Access issues with the learning portal. |
| DE0016 | other | 27 | Brazil | S1016 | C2016 | 2024-05-08T16:40:00 | female | Latinx | associate | Basics of Accounting | Finance | 2024-03-01 | 2024-06-01 | hybrid | Decided to pursue a different learning opportunity. |
| DE0017 | academic | 21 | China | S1017 | C2017 | 2024-04-25T08:55:00 | male | Asian | bachelor | Physics for Engineers | Science | 2024-02-20 | 2024-05-20 | self-paced | Content was significantly more challenging than expected. |
| DE0018 | personal | 36 | Germany | S1018 | C2018 | 2024-03-08T20:10:00 | prefer_not_to_say | White | bachelor | Intermediate German Language | Language | 2024-01-20 | 2024-04-20 | instructor-led | Job relocation interrupted my studies. |
| DE0019 | health | 62 | France | S1019 | C2019 | 2024-05-05T13:50:00 | female | White | doctorate | Advanced Literature Studies | Literature | 2024-03-10 | 2024-06-10 | instructor-led | blank |
| DE0020 | workload | 39 | India | S1020 | C2020 | 2024-03-19T17:55:00 | male | Asian | master | Strategic Management Essentials | Business | 2024-01-10 | 2024-04-10 | hybrid | Course requirements conflicted with my business travel. |
What the 200 rows show
from the 200-row sampleHealth (dropout reason code) stands out: mean student_
- 29median student_
age - 4course delivery modes
- 5student genders
- 6student education levels
- 10student ethnicities
- 10course categories
Median 29, from 12 to 93.
- string 12
- integer 1
- date 2
- datetime 1
Columns
16 columns in three groups| column | type | description | example |
|---|---|---|---|
| Text 12 columns | |||
dropout_event_id | string | Unique identifier for each dropout eventunique | DE0001 |
student_id | string | Anonymized unique identifier for each student | S1001 |
course_id | string | Unique identifier for the digital course | C2001 |
dropout_reason_code | string | Coded reason for the dropout (e.g., 'financial', 'academic', 'technical', etc.)8 values | academic |
dropout_reason_text | string | Free-text description or elaboration of the dropout reasonoptional | Struggled to keep up with… |
student_gender | string | Anonymized gender of the studentmale · female · nonbinary · prefer_not_to_say · other · optional | female |
student_ethnicity | string | Anonymized ethnicity of the student10 ethnicities · optional | Asian |
student_country | string | Country of residence of the studentoptional | India |
student_education_level | string | Highest education level attained by the student6 values · optional | bachelor |
course_title | string | Title of the course from which the student dropped outoptional | Intro to Data Science |
course_category | string | Category or subject area of the course10 categories · optional | Technology |
course_delivery_mode | string | Mode of course delivery (e.g., 'self-paced', 'instructor-led', 'hybrid')self-paced · instructor-led · hybrid · other · optional | self-paced |
| Numbers 1 column | |||
student_age | integer | Age of the student at the time of dropout10 to 100 · optional | 19 |
| Dates and times 3 columns | |||
dropout_datetime | datetime | Timestamp when the dropout event occurred | 2024-03-12T15:42:00 |
course_start_date | date | Date when the course startedoptional | 2024-01-15 |
course_end_date | date | Date when the course ended (or is scheduled to end)optional | 2024-04-15 |
Use it for
An education dashboard
Student_
age by dropout_ reason_ code and a breakdown of student_ country. Excel, Power BI or Tableau. Why do the 18 health rows have a mean student_
age of 69.4? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Dropout events200DE000119academicDE000222technicalDE000337personal
A software demo
Believable dropout events with student_
id, course_ id and dropout_ datetime to fill a screen in front of a buyer.
This dataset200 rows16 columns
Yours1,000 rows16 columnsstudent_country: UK only
blueprint · online-course-dropout-reason-tracking
Behind this dataset
Same schema. As many rows as you need.
These 200 rows came out of a blueprint — 16 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.
Rules it was built with
- Dropout reason codes standardized
- Course subject and difficulty mapped
- Timestamp for dropout event
- Anonymize student profiles
Open the blueprint in Data Factory
1 credit per row. Preview 20 rows free. Buy rows for the full run.
- Exports
- CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
- Licence
- yours to use, including commercially
- API slug
- online-course-dropout-reason-tracking