Intelligent advice generator for personalized language learning through social networking sites

C. Troussas, K. Espinosa, M. Virvou
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引用次数: 4

Abstract

Computer-based advising is focusing on factors that facilitate advice giving in educational systems. In this paper, an intelligent advice generation module is presented. As a testbed for our research, we have incorporated the advice generation mechanism in a Facebook application for language learning. A significant feature in the proposed framework of advice generation is user modeling. The advice generation is required to reason about the students' knowledge status and to decide appropriate advice. Actions, recorded by the application and transformed by the student modeler into student models, serve as the source from which diagnostic information about the student can be extracted. This information indicates which concepts were presented to the student, the duration of time the student spent working with learning objects related to a specific concept, which concepts probably mastered by the student and which concepts not yet mastered. The advice generation can identify the status of students and groups of students. This is used as a source for the generation of appropriate advice to the course instructor, who can then pass the advice to the students or consequently take some pedagogical actions that should be educationally appropriate. The advice generating mechanism based on a set of criteria for selecting appropriate advice according to the current student situation is explained in detail.
通过社交网站进行个性化语言学习的智能建议生成器
基于计算机的咨询侧重于促进教育系统提供建议的因素。本文提出了一种智能建议生成模块。作为我们研究的测试平台,我们在一个用于语言学习的Facebook应用程序中加入了建议生成机制。建议生成框架的一个重要特性是用户建模。建议生成需要对学生的知识状况进行推理,并决定适当的建议。由应用程序记录并由学生建模者转换为学生模型的操作作为可以从中提取有关学生的诊断信息的源。这一信息表明了向学生展示了哪些概念,学生花在与特定概念相关的学习对象上的时间,哪些概念可能被学生掌握了,哪些概念还没有掌握。建议生成可以识别学生和学生群体的状态。这被用作向课程讲师提供适当建议的来源,然后课程讲师可以将建议传递给学生,或者因此采取一些应该在教育上适当的教学行动。详细解释了基于一组标准的建议生成机制,以便根据当前学生的情况选择适当的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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