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Impact of Gamification on Testing Education
Data Science and Analytics
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About
This dataset explores the impact of gamification on the effectiveness of software testing education. It contains experimental data gathered from students, monitoring their engagement with game activities and their performance within a gamified learning experience. The dataset aims to provide insights into how gamification can influence learning outcomes and student participation in educational settings, specifically within software testing courses. Student demographic information was collected, and their engagement was closely monitored, with full transparency and voluntary participation throughout the experiment.
Columns
- Academic Year: The academic year in which the data was collected. (string)
- ID Student: A unique identifier for each student. (string)
- Race Effectiveness: The effectiveness score for the entire course. (number)
- Exercise1 Effectiveness: The effectiveness score for Exercise 1. (number)
- Exercise2 Effectiveness: The effectiveness score for Exercise 2. (number)
- Exercise3 Effectiveness: The effectiveness score for Exercise 3. (number)
- Exercise4 Effectiveness: The effectiveness score for Exercise 4. (number)
- Race Effectiveness Increase: The increase in effectiveness for the entire course. (number)
- Exercise1 Effectiveness Increase: The increase in effectiveness for Exercise 1. (number)
- Exercise2 Effectiveness Increase: The increase in effectiveness for Exercise 2. (number)
- Exercise3 Effectiveness Increase: The increase in effectiveness for Exercise 3. (number)
- Exercise4 Effectiveness Increase: The increase in effectiveness for Exercise 4. (number)
- Race Participation: Indicates participation in the course for the entire course. (integer)
- Exercise1 Participation: Indicates participation in Exercise 1. (number)
- Exercise2 Participation: Indicates participation in Exercise 2. (number)
- Exercise3 Participation: Indicates participation in Exercise 3. (number)
- Exercise4 Participation: Indicates participation in Exercise 4. (number)
- Race Dropout: Indicates dropouts for the entire course. (integer)
- Exercise1 Dropout: Indicates dropouts for Exercise 1. (integer)
- Exercise2 Dropout: Indicates dropouts for Exercise 2. (integer)
- Exercise3 Dropout: Indicates dropouts for Exercise 3. (integer)
- Exercise4 Dropout: Indicates dropouts for Exercise 4. (number)
- Race Active Time: Total active time for the entire course. (number)
- Exercise1 Active Time: Active time for Exercise 1. (integer)
- Exercise2 Active Time: Active time for Exercise 2. (integer)
- Exercise3 Active Time: Active time for Exercise 3. (integer)
- Exercise4 Active Time: Active time for Exercise 4. (integer)
- Race Number Executions: The number of executions for the entire course. (integer)
- Exercise1 Number Executions: The number of executions for Exercise 1. (integer)
- Exercise2 Number Executions: The number of executions for Exercise 2. (integer)
- Exercise3 Number Executions: The number of executions for Exercise 3. (integer)
- Exercise4 Number Executions: The number of executions for Exercise 4. (integer)
Distribution
The dataset is provided in CSV format. The file size is 28.54 kB and it contains 32 columns. There are 235 records within the dataset.
Usage
This dataset is ideal for academic research, statistical analysis, and pedagogical studies focused on the effectiveness of gamification in education. It can be used to analyse student engagement patterns, assess learning outcomes in gamified environments, and inform the design of software testing curricula or other educational programmes incorporating game-based elements.
Coverage
The data was collected during the academic year 2019-2020. The scope is primarily focused on students participating in software testing education, with demographic information gathered as part of the experimental procedure. Student participation in data collection was entirely voluntary and undertaken with informed consent.
License
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Who Can Use It
This dataset is suitable for academic researchers, educational psychologists, data scientists, and software engineering educators. It is particularly valuable for those interested in evaluating the impact of educational interventions, understanding student behaviour in digital learning environments, or developing more engaging teaching methodologies in computer science fields.
Dataset Name Suggestions
- Gamified Software Testing Education Effectiveness
- Student Performance in Gamified Software Testing
- Software Testing Education Gamification Study
- Impact of Gamification on Testing Education
Attributes
Original Data Source: Impact of Gamification on Testing Education
QUALITY