How to implement the question difficulty adaptive function in online answering questions
With the rise of online education, more and more learners choose to study on the Internet. Online question answering is a very important part, and its question difficulty adaptive function plays a crucial role in improving learning results. This article will introduce how to implement the question difficulty adaptive function in online question answering and provide some specific code examples.
The key to realizing the test difficulty adaptive function is to dynamically adjust the difficulty of the test questions according to the learner's ability level. An adaptive algorithm for test question difficulty based on learners' responses will be introduced below.
The following is a sample code of the test question difficulty adaptive function to briefly illustrate the above implementation idea:
def get_difficulty(level, ability): # 定义试题难度与得分范围的关系 difficulty_range = { "easy": (0, 3), "medium": (4, 7), "hard": (8, 10) } # 根据能力水平和试题难度等级计算试题分数范围 min_score = difficulty_range[level][0] max_score = difficulty_range[level][1] difficulty_score = min_score + (max_score - min_score) * ability return difficulty_score def select_question(questions, ability): # 根据学习者能力水平选择试题 selected_question = None max_score = 0 for question in questions: difficulty = question["difficulty"] difficulty_score = get_difficulty(difficulty, ability) if difficulty_score > max_score: max_score = difficulty_score selected_question = question return selected_question # 测试代码 questions = [ {"id": 1, "difficulty": "easy", "content": "问题1"}, {"id": 2, "difficulty": "medium", "content": "问题2"}, {"id": 3, "difficulty": "hard", "content": "问题3"} ] ability = 0.8 selected_question = select_question(questions, ability) print(selected_question)
In the above code, the get_difficulty
function is based on The test question difficulty level and learner ability level are used to calculate the score range for the test question. select_question
The function selects appropriate test questions based on the learner’s ability level.
In practical applications, the above code needs to be embedded into the online question answering system, and appropriately adjusted and expanded according to actual needs. In addition, machine learning and other technologies can also be combined to optimize and improve the question difficulty adaptive algorithm.
To sum up, implementing the test difficulty adaptive function in online answering mainly requires determining the difficulty level of the test questions, assessing the learner's ability level, and selecting test questions based on the ability level. By dynamically adjusting the difficulty of test questions, it can better meet the needs of learners and improve learning effects.
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