diff --git a/lib/irt_ruby/three_parameter_model.rb b/lib/irt_ruby/three_parameter_model.rb
index 309dc31..73eb299 100644
--- a/lib/irt_ruby/three_parameter_model.rb
+++ b/lib/irt_ruby/three_parameter_model.rb
@@ -5,7 +5,7 @@
 module IrtRuby
   # A class representing the Three-Parameter model for Item Response Theory.
   class ThreeParameterModel
-    def initialize(data, max_iter: 1000, tolerance: 1e-6)
+    def initialize(data, max_iter: 1000, tolerance: 1e-6, learning_rate: 0.01)
       @data = data
       @abilities = Array.new(data.row_count) { rand }
       @difficulties = Array.new(data.column_count) { rand }
@@ -13,31 +13,32 @@ def initialize(data, max_iter: 1000, tolerance: 1e-6)
       @guessings = Array.new(data.column_count) { rand * 0.3 }
       @max_iter = max_iter
       @tolerance = tolerance
+      @learning_rate = learning_rate
     end
 
+    # Sigmoid function to calculate probability
     def sigmoid(x)
       1.0 / (1.0 + Math.exp(-x))
     end
 
+    # Probability function for the 3PL model
     def probability(theta, a, b, c)
       c + (1 - c) * sigmoid(a * (theta - b))
     end
 
+    # Calculate the log-likelihood of the data given the current parameters
     def likelihood
       likelihood = 0
       @data.row_vectors.each_with_index do |row, i|
         row.to_a.each_with_index do |response, j|
           prob = probability(@abilities[i], @discriminations[j], @difficulties[j], @guessings[j])
-          if response == 1
-            likelihood += Math.log(prob)
-          elsif response.zero?
-            likelihood += Math.log(1 - prob)
-          end
+          likelihood += response == 1 ? Math.log(prob) : Math.log(1 - prob)
         end
       end
       likelihood
     end
 
+    # Update parameters using gradient ascent
     def update_parameters
       last_likelihood = likelihood
       @max_iter.times do |_iter|
@@ -45,10 +46,11 @@ def update_parameters
           row.to_a.each_with_index do |response, j|
             prob = probability(@abilities[i], @discriminations[j], @difficulties[j], @guessings[j])
             error = response - prob
-            @abilities[i] += 0.01 * error * @discriminations[j]
-            @difficulties[j] -= 0.01 * error * @discriminations[j]
-            @discriminations[j] += 0.01 * error * (@abilities[i] - @difficulties[j])
-            @guessings[j] += 0.01 * error * (1 - prob)
+            @abilities[i] += @learning_rate * error * @discriminations[j]
+            @difficulties[j] -= @learning_rate * error * @discriminations[j]
+            @discriminations[j] += @learning_rate * error * (@abilities[i] - @difficulties[j])
+            @guessings[j] += @learning_rate * error * (1 - prob)
+            @guessings[j] = [[@guessings[j], 0].max, 1].min # Keep guessings within [0, 1]
           end
         end
         current_likelihood = likelihood
@@ -58,6 +60,7 @@ def update_parameters
       end
     end
 
+    # Fit the model to the data
     def fit
       update_parameters
       {