【机器学习】和【人工智能】在未来教育行业的发展——案例分析

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# 加载学习记录数据
data = pd.read_csv('student_learning_data.csv')
X = data.drop('recommended_content', axis=1)
y = data['recommended_content']

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 构建并训练随机森林分类模型
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# 预测推荐内容
y_pred = model.predict(X_test)

# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy: {accuracy}')

import openai

# 设置API密钥
openai.api_key = 'your-api-key'

# 构建智能辅导系统函数
def intelligent_tutor(question):
    response = openai.Completion.create(
        engine="davinci",
        prompt=question,
        max_tokens=150
    )
    return response.choices[0].text.strip()

# 示例问题
question = "What is the Pythagorean theorem?"
answer = intelligent_tutor(question)
print(f'Answer: {answer}')

import cv2
import pytesseract

# 加载并处理图像
image = cv2.imread('exam_paper.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY_INV)[1]

# 识别文本
text = pytesseract.image_to_string(thresh, lang='eng')

# 自动评分函数
def auto_grade(answer_key, student_answer):
    score = 0
    total_questions = len(answer_key)
    for i in range(total_questions):
        if answer_key[i] == student_answer[i]:
            score += 1
    return (score / total_questions) * 100

# 示例答案和评分
answer_key = "ABCD"
student_answer = "ABCD"
score = auto_grade(answer_key, student_answer)
print(f'Score: {score}%')