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Student Digital Achievement Metrics
Education & Learning Analytics
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About
The data quantifies how educational technology influences performance and results, focusing on student outcomes, engagement logs, and achievement tracking. It contains information related to users developing innovative services, winning awards, participating in hackathons, and founding startups, illustrating high achievement among EdTech students. The data is instrumental for understanding usage patterns of computers, software, and other digital learning forms used to enhance student learning effectively.
Columns
The dataset contains 19 fields, including:
- client_id: Identifier associated with a website visit.
- user_id: The deduplicated identifier for the user from the database.
- first_trial_appointment_date: The date a user had their initial trial appointment. This field has a high percentage of missing values.
- first_payment_date: The date of the first financial transaction made by the user, noting that a vast majority of records do not contain this date.
- os: The user's Operating System (e.g., iOS, Android).
- tutor: Indicates whether the student participated in additional individual lessons.
- job: The user's reported employment sphere, with 'Finance' being a notable entry among valid records.
- task_class: Reflects the number of completed tasks within the lessons, typically averaging around 6.76.
- average_score: The student's Grade Point Average (GPA), with scores ranging from 0 to 100, averaging around 77.2.
- homework_done: The recorded number of completed student homework assignments.
- paywall_paid: A boolean field indicating if the user has paid the paywall fee.
- school_name: The name of the school associated with the user, though the majority are listed as 'Unknown'.
- desktop_enter: A boolean flag noting if the user accessed the platform via a desktop device.
- nps_score: The Net Promoter Score given by the user, though this metric is highly sparse across the dataset.
- add_homework_done: The count of additional homework completed by the student.
- call_date: The date of contact or call engagement.
- first_visit_date: The initial date the user visited the platform.
- region: Geographic location of the user; this field is mostly unrecorded.
- is_big_city: A boolean field indicating if the location is considered a major urban centre.
Distribution
The data is presented in a tabular format and is available as a single file,
edtech_data.csv, approximately 20.5 MB in size. It contains about 125,000 records spread across 19 distinct columns. The data is considered static as the expected update frequency is noted as 'Never'. The data usability score is high.Usage
This data is suitable for projects involving predictive analytics, particularly regression modeling to forecast student success (based on
average_score). It can be applied in segmenting user populations by device (OS) and engagement level (task completion, homework), informing targeted marketing or personalised instruction strategies. Furthermore, the dataset supports analyses seeking to identify factors that lead to increased engagement, higher achievement, and successful conversion paths (e.g., from trial appointment to payment).Coverage
The data spans chronologically from early April 2020 through mid-September 2020, charting various user interactions and academic events within this timeframe. Geographically, most regional information is absent; however, Moscow is the most frequently recorded city among the small subset of valid geographic data. Demographic details are limited primarily to employment sphere and device type used for accessing the platform.
License
CC0: Public Domain
Who Can Use It
- EdTech Product Managers: For evaluating the effectiveness of interactive whiteboards, digital learning content, and online learning platforms.
- Data Scientists and Analysts: To build models predicting student performance, success rates, and the efficacy of instructional methods.
- Academic Researchers: Studying the impact of educational technology on student outcomes, learning styles, and efficiency.
- Marketing Teams: To analyse conversion metrics and user journey patterns, particularly concerning trial appointments and first payments.
Dataset Name Suggestions
- Student Digital Achievement Metrics
- EdTech Performance and Engagement Log
- Digital Learning Outcome Data
- Academic Success Tracing
Attributes
Original Data Source: Student Digital Achievement Metrics
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