arXiv 1606.072332016-06-23cs.AI

在线学习环境中的自适应任务分配

Adaptive Task Assignment in Online Learning Environments

University of Agder（阿格德大学）；Oslo and Akershus University College（奥斯陆与阿克什胡斯大学学院）

Per-Arne Andersen, Christian Kråkevik, Morten Goodwin, Anis Yazidi

备注：: 6th International Conference on Web Intelligence

摘要：

随着在线学习的日益普及，智能辅导系统正重新获得越来越多的关注。在本文中，我们引入自适应算法的个性化分配学习任务的学生，以提高他的表现在在线学习环境。作为本文的主要贡献，我们提出了一种新的基于技能的任务分配（SBTS）算法，它能够近似一个学生的技能水平的基础上，他的表现，从而建议适当的任务。SBTS的灵感来自于多臂强盗算法。然而，与标准的多臂强盗方法相比，SBTS旨在获得与学生学习相关的两个标准，即：学生应该研究哪些主题，以及任务的难度应该是什么。SBTS以基于学生行为的任务和技能矩阵中的创新奖励和惩罚计划为中心。为了验证该算法，复杂的学生行为建模使用邻居节点选择方法的基础上经验估计的学生学习曲线。该算法是从一个基本的Java编程课程的实际情况进行评估。SBTS能够快速准确地适应复合学生能力-即使有多种学生模型。

英文摘要：

With the increasing popularity of online learning, intelligent tutoring systems are regaining increased attention. In this paper, we introduce adaptive algorithms for personalized assignment of learning tasks to student so that to improve his performance in online learning environments. As main contribution of this paper, we propose a a novel Skill-Based Task Selector (SBTS) algorithm which is able to approximate a student's skill level based on his performance and consequently suggest adequate assignments. The SBTS is inspired by the class of multi-armed bandit algorithms. However, in contrast to standard multi-armed bandit approaches, the SBTS aims at acquiring two criteria related to student learning, namely: which topics should the student work on, and what level of difficulty should the task be. The SBTS centers on innovative reward and punishment schemes in a task and skill matrix based on the student behaviour. To verify the algorithm, the complex student behaviour is modelled using a neighbour node selection approach based on empirical estimations of a students learning curve. The algorithm is evaluated with a practical scenario from a basic java programming course. The SBTS is able to quickly and accurately adapt to the composite student competency --- even with a multitude of student models.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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