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Student E-Learning Adaptability Factors

Education & Learning Analytics

Tags and Keywords

Education

Adaptability

Students

Online

Learning

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Student E-Learning Adaptability Factors Dataset on Opendatabay data marketplace

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Free

About

This dataset aims to predict students' adaptability levels in online education using machine learning approaches. It serves as a valuable resource for machine learning beginners seeking to apply predictive techniques and for researchers interested in understanding the effectiveness of online education. The data provides insight into various factors influencing a student's ability to adapt to online learning environments.

Columns

  • Gender: The gender type of the student, either Boy or Girl. Boy accounts for 55% of the data, and Girl accounts for 45%.
  • Age: The age range of the student. Common ranges include 21-25 (31%) and 11-15 (29%).
  • Education Level: The level of the educational institution the student attends, such as School (44%) or University (38%).
  • Institution Type: The type of educational institution, predominantly Non Government (68%) or Government (32%).
  • IT Student: A boolean indicating whether the student is studying as an IT student (true: 25%, false: 75%).
  • Location: A boolean indicating if the student's location is in a town (true: 78%, false: 22%).
  • Load-shedding: The level of load shedding experienced, categorised as Low (83%) or High (17%).
  • Financial Condition: The financial condition of the student's family, mainly Mid (73%) or Poor (20%).
  • Internet Type: The type of internet most frequently used on the student's device, primarily Mobile Data (58%) or Wifi (42%).
  • Network Type: The type of network connectivity used, with 4G (64%) and 3G (34%) being the most common.
  • Class Duration: The daily duration of classes, typically 1-3 hours (70%) or 3-6 hours (18%).
  • Self Lms: A boolean indicating the availability of the institution's own Learning Management System (LMS) (true: 17%, false: 83%).
  • Device: The device most often used for classes, predominantly Mobile (84%) or Computer (13%).
  • Adaptivity Level: The target feature, representing the student's adaptability level, categorised as Moderate (52%), Low (40%), or Other (8%).

Distribution

This dataset is provided as a CSV file, named students_adaptability_level_online_education.csv, and is approximately 98.97 kB in size. It contains 14 columns and 1205 records, with no missing or mismatched values across all features.

Usage

This dataset is ideal for applying machine learning techniques to predict students' adaptability levels in online education. It can be used for various analytical and research purposes aimed at understanding and improving the effectiveness of online learning.

Coverage

The dataset's geographic scope is specified as Asia. It covers a demographic of students, providing insights into various attributes such as gender, age ranges, education levels, and financial conditions. Specific time ranges for data collection are not detailed, however, the dataset is expected to be updated annually.

License

CC BY-SA 4.0

Who Can Use It

This dataset is suitable for machine learning enthusiasts and beginners looking to practice predictive modelling, as well as researchers and academics focusing on the effectiveness and adaptability of students in online education environments. It can be used for any general analysis or research purpose.

Dataset Name Suggestions

  • Online Education Adaptability Prediction
  • Student E-Learning Adaptability Factors
  • Digital Education Readiness Dataset
  • Student Online Learning Success
  • Adaptability in Remote Learning

Attributes

Listing Stats

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LISTED

08/07/2025

REGION

GLOBAL

Universal Data Quality Score Logo UDQSQUALITY

5 / 5

VERSION

1.0

Free

Download Dataset in CSV Format