AI与人类注意力流：打造人机融合的教育

    def is atenção_concentrada(eyetracking_data, threshold=50):
    last_position = None
       注意力分散时间 = 0
    for timestamp, position in eyetracking_data:
        if last_position is not None:
            distance = calculate_distance(last_position, position)
            if distance > threshold:
                注意力分散时间 += timestamp - last_timestamp
        last_position = position
        last_timestamp = timestamp
    return 注意力分散时间 < threshold
    
    def calculate_distance(p1, p2):
    return ((p1[0] - p2[0])**2 + (p1[1] - p2[1])**2)**0.5
    
    eyetracking_data = [
    (1, (100, 100)),
    (2, (110, 110)),
    (3, (90, 90)),
    (4, (120, 120)),
    ]
    
    print(is_atsentido_concentrada(eyetracking_data))  # 输出 False
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    class IntelligentTutorSystem:
    def __init__(self):
        self.student_data = {}
        self.learning_resources = []
    
    def collect_data(self, student_id, data):
        self.student_data[student_id] = data
    
    def analyze_behavior(self, student_id):
        data = self.student_data[student_id]
        # 分析数据，提供学习建议
        recommendation = "根据你的表现，建议你加强数学基础知识的学习。"
        return recommendation
    
    def recommend_resources(self, student_id):
        recommendation = self.analyze_behavior(student_id)
        if "数学" in recommendation:
            resources = ["数学基础教程", "数学习题集"]
        else:
            resources = ["语文基础教程", "语文习题集"]
        return resources
    
    def provide_feedback(self, student_id, feedback):
        # 存储反馈，用于优化系统
        pass
    
    # 使用系统
    system = IntelligentTutorSystem()
    system.collect_data("S123", {"math_score": 70, "english_score": 80})
    print(system.recommend_resources("S123"))  # 输出 ['数学基础教程', '数学习题集']
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    import tensorflow as tf
    from tensorflow.keras.layers import Embedding, LSTM, Dense
    from tensorflow.keras.models import Model
    
    # 假设我们已经有预训练的词向量 embedding_matrix
    # 嵌入层
    embedding = Embedding(input_dim=vocab_size, output_dim=embedding_size, weights=[embedding_matrix], trainable=False)
    
    # 编码器
    encoder_inputs = tf.keras.Input(shape=(None,))
    encoded = embedding(encoder_inputs)
    encoded = LSTM(units, return_state=True)(encoded)
    
    # 解码器
    decoder_inputs = tf.keras.Input(shape=(None,))
    decoded = embedding(decoder_inputs)
    decoded = LSTM(units, return_sequences=True, return_state=True)(decoded)
    
    # 注意力层
    attention = Dense(units, activation='softmax')(encoded)
    attention = tf.keras.layers.Attention()([decoded, attention])
    
    # 模型组装
    outputs = LSTM(units, return_sequences=True)(attention)
    outputs = Dense(vocab_size, activation='softmax')(outputs)
    
    # 模型编译
    model = Model([encoder_inputs, decoder_inputs], outputs)
    model.compile(optimizer='adam', loss='categorical_crossentropy')
    
    # 模型训练
    # model.fit([encoder_inputs_train, decoder_inputs_train], decoder_targets_train, batch_size=batch_size, epochs=epochs)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读