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Using HCI Task Modeling Techniques to Measure How Deeply Students Model

机译：使用HCI任务建模技术来衡量学生建模的深度

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摘要

User modeling in AIED has been extended in the past decades to include affective and motivational aspects of learner's interaction in intelligent tutoring systems. In order to study those factors, various detectors have been created that classify episodes in log data as gaming, high/low effort on task, robust learning, etc. In this article, we present our method for creating a detector of shallow modeling practices within a meta-tutor instructional system. The detector was defined using HCI (human-computer interaction) task modeling as well as a coding scheme defined by human coders from past users' screen recordings of software use. The detector produced classifications of student behavior that were highly similar to classifications produced by human coders with a kappa of .925.
机译：在过去的几十年中，AIED中的用户建模得到了扩展，涵盖了智能辅导系统中学习者互动的情感和动机方面。为了研究这些因素，已经创建了各种检测器，这些记录器将日志数据中的情节分类为游戏，工作的高/低努力，健壮的学习等。在本文中，我们介绍了用于在内部创建浅层建模实践的检测器的方法元教师教学系统。使用HCI（人机交互）任务模型以及人类编码人员根据过去用户对软件使用的屏幕记录定义的编码方案来定义检测器。检测器产生的学生行为分类与人类编码者产生的分类非常相似，kappa为.925。

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