Re-alignment of Study Schedules Through Exploratory Data Analysis

In my venture SPARK , I focus on "Build Your Own Schedule." My purpose is to help students manage time well. Managing time efficiently eventually improves your study, sets a particular pattern to it to which your brain responds and works accordingly.

"Creating a timetable does not ensure following of it !"

The Missing Link : Many a times I see young students creating time schedule but failing to achieve what they have decided. This happens because they do not track the process and the progress. Continuous assessment is necessary to check whether the time table made is being followed or not. I sensed a need for creating a tool to Track Daily Self Study schedule made by students to ensure the systematic progress. Checking the feasibility of schedule is also necessary. Gradual changes are necessary in the schedule.

To tackle this problem, I decided to use the winter vacation ( Dec 2024 - Jan 2025).

I planned a mini-project under our Sunday Side Projects, one of the initiatives of ValidusEduTech . These projects are designed for enchaining student Profiles wherein they practice programming languages and gain practical experience on mini projects like building basic HTML/CSS/JS based websites or using GEOGEBRA to understand physics.

Methodology

1. The Data Collection Process

I designed a Google form that collected:

• Name of the student
• Date and time stamp when the form was filled ( This is collected automatically by the google form )
• Study start time and end time
• Rate of Perceived Exhaustion (RPE) measured on a scale of 1 to 5
• Subject information

The RPE scale helped me calculate the efforts put in by VET students into their studies, where 1 indicates they haven't exerted much and 5 indicates maximum exertion.

2. Expected Outcomes :

• Comparative Analysis : Comparing RPE, number of sessions completed, average duration given to each sessions amongst the subjects studied by the students including Graphical representations.
• Prime Time Analysis : Dividing day into 6 hour time slots like dawn, morning, afternoon and evening and getting analysis of what was the RPE and average duration during that particular time.

The process starts by the students recording their daily study times in the GOOGLE FORM. This google form was circulated at the time when VET students created their own self study schedule.

3. Technical Implementation

To get the analysis from the collected data, I used Python libraries namely pandas for reading and exploring the data, date-time module for manipulating dates and time format and finally matplotlib/plotly for visualizations. These tools helped me track patterns effectively. The process started with:

1. Data Cleaning

• Checking for null entries
• Deleting unwanted columns
• Converting time stamps into proper formats
• Making copies of original data sets

2. Individual Analysis

We created separate spreadsheets for individual students to track their progress without disturbing the original data.

Analysis Components

Our analysis focused on three main areas:

1. Comparative Analysis

We looked at how students performed across different subjects, comparing:

• RPE values
• Number of sessions completed
• Duration of study periods

This helped us tell students which subject needs proper push and how much time they should study.

2. Prime Time Analysis

We divided each day into working hours and analyzed:

• When students studied most effectively
• Average RPE during different time slots
• Duration of productive study sessions

We considered maximum study duration to be 3 hours, as RPE was formally decided as 5.

3. Subject-wise Patterns

For each subject, we tracked:

• Number of sessions
• Time slots chosen
• Performance levels

Results

This short winter project helped me check on a daily basis whether students follow their time table and providing them with feedback to increase their efficiency in managing work.

After displaying comparative analysis about their subjects, I approached our students, informing them which subject needed more attention. The analysis helped them track where do they actually stand and how much push should they need to give.

Impact on Student Schedules

Post analysis, I helped students:

• Realign their timetables based on the data
• Understand their optimal study periods
• Adjust their study durations per subject.

The visualizations we made for each time slot showed when they studied best and what RPE they achieved. This helped get a perspective about how much more time these students should put in.

Through this project at ValidusEduTech, we demonstrated how data analysis can improve study patterns and time management. The system continues to help students build better schedules and achieve improved results through systematic tracking and analysis.

Technical Points of Contact

For implementation details and technical specifications, reach out to us on validus.edutech@gmail.com

GitHub repo : https://github.com/SuhaniKolhatkar/Exploratory_Data_Analysis_2025