Dataset of Online Education Technologies and Resources for Learning After COVID19 Pandemic
Description
The dataset provided contains information on 120 students from three different universities, focusing on their use of online educational tools. The dataset includes various attributes related to each student's demographics, university details, preferences, and their experiences with online learning environments. Below is a detailed overview of the attributes: Attributes in the Dataset: Student_ID: Unique identifier for each student. University: The university that each student is affiliated with, including "Mother Teresa University", "South East European University", and "Saint Cyril and Methodious University". Year_of_Study: Current academic year of each student, ranging from 1st to 4th year. Major: The field of study for each student, spanning 15 unique majors such as Economics, Engineering, Law, etc. Gender: Gender of the student (Male or Female). Age: Age of each student, ranging from 18 to 25 years. Online_Tool_Used: The online learning platform used by each student, including tools like Blackboard Collaborate, Moodle, MS Teams, and Google Classroom. Usage_Frequency: How often students use the online learning tools, with values such as "Daily", "Weekly", and "Monthly". Satisfaction_Score: Students' satisfaction with the online tools, measured on a scale from 1 to 5. Accessibility_Score: Rating given by students on the accessibility of the tools, also measured on a scale from 1 to 5. Impact_on_Learning: The perceived impact of the online learning tools on students' academic performance, rated from 1 to 5. Challenges_Faced: Challenges faced by students while using the tools, including issues such as "Security concerns", "Bandwidth issues", or "Learning curve". Preferred_Features: Features that students value most in the online learning tools, such as "Simplicity", "User interface", "Resource access". Suggestions_for_Improvement: Feedback provided by students on how the tools could be improved, for instance, suggestions like "Add more interactive tools" or "Enhance login security". Dataset Insights: The dataset provides insights into how different groups of students interact with online learning platforms. Satisfaction and accessibility scores offer a quantitative look at the user experience. The dataset includes diverse majors and universities, offering a broad view of online education usage. Qualitative fields like "Challenges_Faced" and "Suggestions_for_Improvement" provide valuable data for understanding the problems and recommendations from the user perspective. This dataset is suitable for examining patterns in online education tool usage, identifying potential areas for improvement, and understanding the general sentiment and challenges faced by students in online learning environments.
Files
Steps to reproduce
For the provided dataset, suppose the steps to reproduce involve creating a summary analysis of student satisfaction across different universities. The steps may look like: Environment Setup: Install Python 3.9. Install required libraries using pip install pandas. Data Preparation: Download the dataset student_dataset.csv. Save the file in the working directory. Steps to Reproduce: Load the Data: Run the command: import pandas as pd dataset = pd.read_csv('student_dataset.csv') Group by University: Execute the following command to obtain mean satisfaction scores by university: satisfaction_by_university = dataset.groupby('University')['Satisfaction_Score'].mean() print(satisfaction_by_university) Expected Outcome: The console should display the average satisfaction score for each university. Handling Issues: If the command fails, check for missing values in the Satisfaction_Score column using: dataset['Satisfaction_Score'].isna().sum() Describing steps to reproduce is integral to troubleshooting, ensuring consistency in data analysis, and allowing others to verify findings or replicate processes accurately. The goal is to be as clear and precise as possible so that any individual can follow along and achieve the same results without ambiguity.