In higher education, the initial studying period of each course plays a crucial role for students, and seriously influences the subsequent learning activities. However, given the large size of a course’s students at universities, it has become impossible for teachers to keep track of the performance of individual students. In this circumstance, an academic early warning system is desirable, which automatically detects students with difficulties in learning (i.e., at-risk students) prior to a course starting. However, previous studies are not well suited to this purpose for two reasons: 1) they have mainly concentrated on e-learning platforms, e.g., massive open online courses (MOOCs), and relied on the data about students’ online activities, which is hardly accessed in traditional teaching scenarios; and 2) they have only made performance prediction when a course is in progress or even close to the end. In this paper, for traditional classroom-teaching scenarios, we investigate the task of pre-course student performance prediction, which refers to detecting at-risk students for each course before its commencement. To better represent a student sample and utilize the correlations among courses, we cast the problem as a multi-instance multi-label (MIML) problem. Besides, given the problem of data scarcity, we propose a novel multi-task learning method, i.e., MIML-Circle, to predict the performance of students from different specialties in a unified framework. Extensive experiments are conducted on five real-world datasets, and the results demonstrate the superiority of our approach over the state-of-the-art methods.

中文翻译：

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多任务MIML学习，用于课前学生成绩预测

在高等教育中，每门课程的初始学习期对学生至关重要，并严重影响随后的学习活动。但是，鉴于大学课程的学生人数众多，因此教师无法跟踪每个学生的表现。在这种情况下，需要一个学术预警系统，该系统可以在课程开始之前自动检测学习困难的学生（即处于危险中的学生）。但是，先前的研究由于以下两个原因而不太适合该目的：1）他们主要集中在电子学习平台上，例如大规模开放在线课程（MOOC），并依赖于学生在线活动的数据，这是在传统教学场景中几乎无法访问；2）他们仅在课程进行中甚至接近结束时才进行绩效预测。在本文中，对于传统的课堂教学方案，我们研究了课前学生表现预测的任务，这是指在课程开始之前检测每门课程的高风险学生。为了更好地表示学生样本并利用课程之间的相关性，我们将该问题称为多实例多标签（MIML）问题。此外，针对数据匮乏的问题，我们提出了一种新颖的多任务学习方法，即MIML-Circle，可以在一个统一的框架中预测来自不同专业的学生的表现。在五个真实世界的数据集上进行了广泛的实验，结果证明了我们的方法优于最新方法的优越性。