【作 者】：
殷宝媛， 武法提
【关 键 词 】：
学习习惯； 作业习惯； 学业拖延； 学业勤奋； 智能学习系统； 建模
【栏 目】：
学习环境与资源
【中文摘要】：
作业习惯与学习者的学业成就紧密相关，作业习惯的建模是智能学习系统设计中亟待解决的问题。本研究应用混合式研究方法，依据多层次作业模型，确定“学业拖延”和“学业勤奋”作为两个重要且可以测量的作业习惯的维度，分别表征作业时间和作业努力这两类核心的作业行为。构建了包括做题拖延和提交拖延的学业拖延习惯子模型，应用聚类实现了对学业拖延习惯的诊断，定义了“无拖延习惯者”“严重拖延习惯者”“提交作业拖延者”“做作业拖延者”四类学习者。以时间投入—专注度模型为理论框架，构建了包括时间投入和专注度的学业勤奋习惯子模型，应用人工神经网络实现对学业勤奋习惯进行诊断，诊断出五种勤奋度的级别，并验证了模型的有效性。
【英文摘要】：
Homework habits are closely related to learners' academic achievement and the modeling of homework habits is an urgent problem in the design of intelligent learning system. Based on a multi-level homework model, this study adopts a hybrid research approach to identify "academic procrastination" and "academic diligence" as two important and measurable dimensions of homework habits, which represent the two core types of homework behaviors: homework time and homework effort. A sub-model of academic procrastination habits is constructed, including procrastination in doing and submitting. Clustering is applied to diagnose the habit of academic procrastination, and four types of learners are defined as "non-procrastinators", "severe procrastinators", "procrastinators in submitting" and " procrastinators in doing". Based on the theoretical framework of time investment and focus model, a sub-model of academic diligence habits including time investment and focus is constructed. The artificial neural network is used to diagnose the academic diligence habits, as a result, five levels of diligence are diagnosed, and the validity of the model is verified.