Schools and cram schools often worry about how well a student will perform. If we could predict their grades in advance, we could provide more accurate instruction and support.
In this article, we will use Python and supervised learning toHow to build an AI model to predict student gradesWe will explain everything from the necessary libraries to setting up the environment, preprocessing data, learning models, and executing predictions.Expressions and specific codes that even elementary school students can understandWe will introduce it here.
We will proceed carefully so that even those who are new to AI will feel like, "I can do this."
What is the prediction of student performance?
conclusion
What is student grade prediction?Technology to predict future performance from past learning datais.
reason
A student's grades are influenced by the degree of completion of assignments, class attitude, past test results, etc. By learning from this data, it becomes possible to predict future scores.
Examples
• Predict final exam scores based on quiz scores and assignment submission status
• Predicting end-of-year overall grades from report card fluctuation patterns
• Use academic progress as a reference for choosing schools to apply to
reassertion
The prediction of the results isContributing to the realization of individually optimized instruction in the educational fieldI will.
Preparing the Python environment and installing libraries
conclusion
Create a Python virtual environment, install the necessary libraries, and you're ready to go.
procedure
1. Creating a virtual environment (Windows/Mac)
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2. Activate the virtual environment
• Windows
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• Mac/Linux
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3. Install required libraries
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point
• pandas: Useful for handling table data
• scikit-learn: A library for building machine learning models.
reassertion
Once the environment is ready, you can immediately implement AI.
Loading and preprocessing data
conclusion
Supervised learning requires data that pairs "features (input)" with "answers (output)."
Example of data used (student information)
| hours_study | attendance | test_score |
| 3 | 90 | 70 |
| 5 | 100 | 88 |
| 1 | 60 | 45 |
Code example
1 | = : [3, 5, 1, 2, 4], : [90, 100, 60, 70, 95], : [70, 88, 45, 50, 85= , ]] |
reassertion
In supervised learning, a "set of inputs and outputs" is essential.
Building a supervised learning model
conclusion
In supervised learning, a predictive model is trained based on data.
Algorithm used: Linear regression
1 | = =0.2= |
point
• train_test_splitSplit the data into 8:2
• fit() functionLearn on
reassertion
Linear regression isA powerful technique when the predicted values are numericalis.
Let's actually predict the results
conclusion
By feeding data into a trained model, we can predict performance.
1 | = [[4, 92]] |
Example results
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reassertion
With just a few lines, future scores become predictable.
Accuracy evaluation and future use
conclusion
Let's check the numbers to see how accurately the AI made predictions.
Evaluation method: Mean Squared Error (MSE)
1 | = = ) |
What does the rating mean?
• The smaller the MSE, the closer the prediction is to reality.
• Generally, a value below 10 is considered to be fairly accurate.
reassertion
Visualizing accuracy is key to increasing trust in AI.
FAQ
Q1. What should I do when there is little training data?
A. If the amount of data is small, accuracy will decrease, so increase the amount of training data or use data augmentation (duplication or synthesis).
Q2. Can you predict things other than test scores?
A. Of course, that is possible. We can handle anything that can be expressed in numbers, such as the number of days attended, the number of submissions, and overall grades.
Q3. Can I use Random Forest or XGBoost?
A. Yes, if you want to achieve higher accuracy, these algorithms are also effective.
summary
Main points
• Using supervised learning, we can predict student performance in advance.
• Feature and ground truth pairs are required
• Easy to implement with Python + scikit-learn
Applications
• Teaching plans for educational settings
• Alerts for underperforming students
• Use as materials for parent-teacher interviews
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