Adaptive learning systems collect data on student performance and use them to personalize system behavior. Most current personalization techniques focus on the correctness of answers. Although the correctness of answers is the most straightforward source of information about student state, research suggests that additional data are also useful, e.g., response times, hints usage, or specific values of incorrect answers. However, these sources of data are not easy to utilize and are often used in an ad hoc fashion. We propose to use answer classification as an interface between raw data about student performance and algorithms for adaptive behavior. Specifically, we propose a classification of student answers into six categories: three classes of correct answers and three classes of incorrect answers. The proposed classification is broadly applicable and makes the use of additional interaction data much more feasible. We support the proposal by analysis of extensive data from adaptive learning systems.

中文翻译：

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超越二元正确性：学习系统中学生答案的分类

自适应学习系统收集有关学生表现的数据，并使用它们来个性化系统行为。大多数当前的个性化技术都侧重于答案的正确性。虽然答案的正确性是关于学生状态的最直接的信息来源，但研究表明附加数据也很有用，例如响应时间、提示使用或错误答案的特定值。然而，这些数据源并不容易使用，并且经常以临时方式使用。我们建议使用答案分类作为有关学生表现的原始数据和自适应行为算法之间的接口。具体来说，我们提出将学生答案分为六类：三类正确答案和三类错误答案。提议的分类具有广泛的适用性，并且使得使用额外的交互数据更加可行。我们通过分析来自自适应学习系统的大量数据来支持该提议。