Predicting Student Exam Outcomes with Machine Learning in Ruby

Introduction

Predicting student performance can be a valuable tool for educators and institutions aiming to identify students who might need additional support. With the rise of machine learning, educational institutions can leverage these technologies to enhance their decision-making processes. In this article, we’ll explore how to use Rumale, a machine learning library for Ruby, to predict whether students will pass or fail based on features such as study hours, attendance rates, and previous exam scores.

Why Rumale?

Rumale (Ruby Machine Learning) is a comprehensive machine learning library in Ruby that offers various algorithms for classification, regression, and clustering. It is designed to be compatible with Numo, Ruby’s numerical computation library, which provides fast array operations, making it an excellent choice for handling large datasets typical in machine learning tasks.

Setting Up

Before diving into the code, ensure you have Ruby installed on your machine. You will also need to install the Rumale gem. Open your terminal and run:

gem install rumale

This command installs Rumale and its dependencies, including Numo, which we will use for numerical operations.

Preparing the Data

For our example, we’ll use a simple dataset representing various students. Each student has three features: study hours, attendance rate, and previous exam scores. These features are common predictors of academic success. Here’s how we set up our data:

require 'rumale'

# Sample data
data = [
  {features: Numo::DFloat[5, 0.9, 80], label: 1},   # pass
  {features: Numo::DFloat[2, 0.8, 65], label: 0},   # fail
  {features: Numo::DFloat[3, 0.8, 70], label: 0},   # fail
  {features: Numo::DFloat[4, 0.95, 75], label: 1},  # pass
  {features: Numo::DFloat[6, 1.0, 90], label: 1}    # pass
]

Training the Model

We use a Random Forest Classifier, a popular ensemble learning method used for classification tasks. The classifier operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes of the individual trees.

# Prepare features and labels for training
features = Numo::DFloat.vstack(data.map { |d| d[:features] })
labels = Numo::Int32[*data.map { |d| d[:label] }]

# Initialize and train the classifier
rf = Rumale::Ensemble::RandomForestClassifier.new(n_estimators: 10, max_depth: 5, random_seed: 1)
rf.fit(features, labels)

Making Predictions

Finally, let’s use our trained model to predict the outcome for a new student:

# New student features
new_student_features = Numo::DFloat[4, 0.85, 72]
prediction = rf.predict(Numo::DFloat[new_student_features.reshape(1,3)])

puts "The predicted outcome for the new student is: #{prediction[0] == 1 ? 'pass' : 'fail'}"

Conclusion

In this tutorial, we’ve demonstrated how to use Rumale to predict student outcomes based on their study habits and previous performance. Machine learning offers powerful tools for educational data analysis, helping to identify trends and make informed decisions that can positively impact student success.

By integrating machine learning into educational processes, we can provide more personalized support to students, enhancing learning experiences and outcomes.