Penteado Bruno Elias, Paiva Paula Maria Pereira, Morettin-Zupelari Marina, Isotani Seiji, Ferrari Deborah Viviane
Institute of Mathematical and Computer Sciences, University of São Paulo, São Carlos, Brazil.
Speech Language Pathology and Audiology Department, Dental School of Bauru, University of São Paulo, Bauru, Brazil.
Am J Audiol. 2018 Nov 19;27(3S):513-525. doi: 10.1044/2018_AJA-IMIA3-18-0020.
This article introduces concepts and a general taxonomy used by the educational data mining (EDM) community, as well as examples of their applications, with the aims of providing audiology educators with a referential basis for developing this area.
A narrative review was carried out to present an overview of EDM and its main methods. Some of these methods were exemplified with analysis of real data from an Internet-based specialization course on pediatric auditory rehabilitation.
The review introduced EDM main concepts and applications and described methods from its area. Real data examples illustrated EDM use to predict interpersonal help-seeking, model interpersonal interaction, analyze students' trajectories within a course's module, and understand how students approached group assignments. Some of the insights provided by EDM to support teaching and learning processes were also described.
EDM methods offer new tools to discover knowledge from digital traces (i.e., logs) and support key stakeholders (students, instructors, or course administrators) to raise awareness about course dynamics. This approach has the potential to foster a better understanding and management of educational processes in audiology distance education.
本文介绍了教育数据挖掘（EDM）社区所使用的概念和一般分类法，以及它们的应用示例，旨在为听力学教育工作者提供发展该领域的参考依据。
进行了一项叙述性综述，以概述EDM及其主要方法。其中一些方法通过对基于互联网的小儿听觉康复专业课程的真实数据进行分析来举例说明。
该综述介绍了EDM的主要概念和应用，并描述了其领域的方法。真实数据示例说明了EDM用于预测人际求助行为、模拟人际互动、分析学生在课程模块中的学习轨迹以及了解学生如何完成小组作业。还描述了EDM为支持教学过程提供的一些见解。
EDM方法提供了新工具，可从数字痕迹（即日志）中发现知识，并支持关键利益相关者（学生、教师或课程管理人员）提高对课程动态的认识。这种方法有可能促进对听力学远程教育中教育过程的更好理解和管理。
