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首页> 外文期刊>Philippine Information Technology >Is the Part Greater than the Whole : A Comparison of Affective and Behavioral Models Derived from Feature Sub-sets
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Is the Part Greater than the Whole : A Comparison of Affective and Behavioral Models Derived from Feature Sub-sets

机译：该部分是否大于整体：从特征子集中获得的情感和行为模型的比较

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Affective computing is computing that relates to user emotion, feelings and motivation. One core problem that it tries to address is the automatic detection of user affect. In this paper, attempts were made to develop models of affective and behavioral states that users exhibit while using Aplusix, an intelligent tutoring system for Algebra. We gathered both user interaction log data and biometrics data from first year Information Technology students at the Mapua Institute of Technology. Both logs were synchronized, cut into time frames, and labeled following rules that we formulated for identifying the specific states of interest. We then used logistic regression and decision tree algorithms to model student affect and behavior based on three feature sets – data from the log files, data from the biometrics logs and a combination of the two. We attempted to determine which feature set was able to produce a more accurate model of affective states of boredom, flow and confusion, and on-task and off-task behavior. We found that logistic regression produces a more accurate model using the feature set from the log files. However, there are no changes in its accuracy when compared to the logistic regression model produced using the combined feature set.
机译：情感计算是与用户情感，感觉和动机相关的计算。它试图解决的一个核心问题是自动检测用户影响。在本文中，我们尝试开发用户在使用代数智能补习系统Aplusix时表现出的情感和行为状态模型。我们从Mapua理工学院一年级信息技术专业的学生那里收集了用户交互日志数据和生物统计数据。两种日志均已同步，切入时间范围并按照我们制定的用于标识特定关注状态的规则进行标记。然后，我们使用逻辑回归和决策树算法基于三个功能集对学生的情感和行为进行建模-日志文件中的数据，生物特征学日志中的数据以及两者的组合。我们试图确定哪个功能集能够产生一个更准确的模型，以模拟无聊，流动和困惑以及任务时和任务外行为的情感状态。我们发现逻辑回归使用日志文件中的功能集生成了更准确的模型。但是，与使用组合特征集生成的逻辑回归模型相比，其准确性没有变化。

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