具有分析洞察力的协作学习管理系统:初步研究

Daniel Lai, S. Lew, S. Ooi
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引用次数: 0

摘要

几十年来,教学模式发生了翻天覆地的变化。因此,一种方法不一定适合所有情况。协作学习促进学生之间的协作,以共同的目标完成给定的任务。本文提出了问题陈述:(i) 虚拟学习环境中学生与教师之间的协作一直处于最低水平,(ii) 已实施的学习管理系统没有充分利用学术上收集的数据和信息。此外,本文还采用了系统文献综述(SLR)的方法来研究有关协作学习、学习管理系统(LMS)和学生特征分析方法的见解。本文旨在解决系统文献综述中形成的三个研究问题:(i) 什么是最常用的协作学习方法?(iii) 影响学生使用学习管理系统的因素有哪些?此外,协作学习可让学生进行小组讨论和作业,促进学生之间的相互交流和知识创造。此外,第三方学习管理系统缺乏同步聊天功能。学生档案能让教育工作者深入了解学生的学习成绩和学习进度。这些信息有助于在分析方法的帮助下预测学生的成绩。所应用的分析方法提供了有关学生学习行为的有用信息,使教师能够采取适当的行动。因此,构建了一个概念框架,并提出了一些假设,反映了每个构件之间的关系。此外,具有机器学习功能的专用协作学习管理系统是一个理想的解决方案,可以解决学生在同学之间和教师之间的协作问题,并将他们的学习成绩和行为考虑在内。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Collaborative Learning Management System with Analytical Insights: A Preliminary Study
The mode of teaching and learning had been drastically changed over the decades. Therefore, one approach might not fit into all scenarios. Collaborative learning promotes collaboration between the students in completing given tasks with common goals. In this paper, problem statements were formed: (i) the collaboration between students and their teachers in the virtual learning environment has been at the bare minimum, (ii) the learning management system implemented has not been fully utilised with the data and information collected academically. Moreover, systematic literature review (SLR) is practised to investigate insights about collaborative learning, learning management system (LMS) and analytical approaches for student profiling. The aim of this paper is to address three research questions formed in the SLR: (i) What is the most commonly practised methodology for collaborative learning? (ii) What are the typical practised analytical methods and models for student profiling? and (iii) What factors influence students to use the learning management system? Besides, collaborative learning enables the students to conduct group discussions and assignments, promoting mutual interactions and creating knowledge amongst them. Additionally, the third-party LMS lacks synchronous chat feature. A student's profile grants educators valuable insights into the student's academic performance and learning progress. This information contributes to predicting the student’s performance with the assistance of analytical approaches applied. The applied analytical approaches provide useful information about the student’s learning behaviour, allowing the teachers to take adequate action. As a result, a conceptual framework is constructed with hypotheses formulated, reflecting the relations between each construct. Besides, a dedicated collaborative learning management system with machine learning capabilities is an ideal solution, tackling students’ collaboration among peers and between teachers with their academic performance and behaviour taken into account.
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