Centro de Ciências, Tecnologias e Saúde, Universidade Federal de Santa Catarina, Araranguá 88906072, Brazil.
Escuela de Ingeniería Informática, Universidad de Valparaíso, Valparaíso 2362735, Chile.
Sensors (Basel). 2021 Feb 22;21(4):1525. doi: 10.3390/s21041525.
Communicating in social and public environments are considered professional skills that can strongly influence career development. Therefore, it is important to proper train and evaluate students in this kind of abilities so that they can better interact in their professional relationships, during the resolution of problems, negotiations and conflict management. This is a complex problem as it involves corporal analysis and the assessment of aspects that until recently were almost impossible to quantitatively measure. Nowadays, a number of new technologies and sensors have being developed for the capture of different kinds of contextual and personal information, but these technologies were not yet fully integrated inside learning settings. In this context, this paper presents a framework to facilitate the analysis and detection of patterns of students in oral presentations. Four steps are proposed for the given framework: , , , and . Data Collection step is responsible for the collection of students interactions during presentations and the arrangement of data for further analysis. Statistical Analysis provides a general understanding of the data collected by showing the differences and similarities of the presentations along the semester. The Clustering stage segments students into groups according to well-defined attributes helping to observe different corporal patterns of the students. Finally, Sequential Pattern Mining step complements the previous stages allowing the identification of sequential patterns of postures in the different groups. The framework was tested in a case study with data collected from 222 freshman students of Computer Engineering (CE) course at three different times during two different years. The analysis made it possible to segment the presenters into three distinct groups according to their corporal postures. The statistical analysis helped to assess how the postures of the students evolved throughout each year. The sequential pattern mining provided a complementary perspective for data evaluation and helped to observe the most frequent postural sequences of the students. Results show the framework could be used as a guidance to provide students automated feedback throughout their presentations and can serve as background information for future comparisons of students presentations from different undergraduate courses.
在社交和公共环境中进行沟通被认为是专业技能，它可以对职业发展产生重要影响。因此，对学生进行这种能力的适当培训和评估非常重要，这样他们才能在专业关系中更好地互动，在解决问题、谈判和冲突管理中更好地互动。这是一个复杂的问题，因为它涉及到身体分析和评估直到最近几乎无法定量衡量的方面。如今，许多新技术和传感器已经被开发出来，用于捕捉不同类型的情境和个人信息，但这些技术尚未完全集成到学习环境中。在这种情况下，本文提出了一个框架，以方便分析和检测学生在口头报告中的模式。该框架提出了四个步骤：数据收集、统计分析、聚类和序列模式挖掘。数据收集步骤负责收集学生在演示过程中的互动，并为进一步分析安排数据。统计分析通过显示整个学期演示文稿的差异和相似性，提供对所收集数据的总体理解。聚类阶段根据定义明确的属性将学生分成组，帮助观察学生的不同身体模式。最后，序列模式挖掘步骤补充了前几个阶段，允许识别不同组中姿势的序列模式。该框架在一个案例研究中进行了测试，该研究的数据来自计算机工程（CE）课程的 222 名新生，在两年的三个不同时间收集。分析结果将演示者分为三个不同的组，根据他们的身体姿势。统计分析有助于评估学生的姿势在每年的发展情况。序列模式挖掘为数据评估提供了一个补充视角，并有助于观察学生最常见的姿势序列。结果表明，该框架可用于为学生提供整个演示过程的自动反馈，并可作为不同本科课程学生演示文稿未来比较的背景信息。
