Towards a Framework on Sentiment Analysis of Educational Domain for Improving the Teaching and Learning Services

Harnani Mat Zin, N. Mustapha, M. A. Azmi Murad, Nurfadhlina Mohd Sharef
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Abstract

Analyzing students’ feedback and their expressed emotions toward any subjects could help lecturers to understand their students’ learning behaviour. Several platforms are used by students to express their feelings such as through social networking sites, blogs, discussion forums and the university survey systems. However, the feedbacks typically contain thousands of sentences and are from various sources which makes analyzing them a cumbersome and tedious work. In this regard, sentiment analysis (SA) has been proposed to automate the process of mining user feedback into valuable information. This paper discusses the principles of SA, its potential benefits, and its application in the educational field based on the synthesis of previous studies. We suggest that SA can help lecturers to easily understand the needs and problems of their students. In particular, a framework and a performance evaluation method were proposed to help guide the implementation of the SA in the education domain.
面向教与学服务的教育领域情感分析框架
分析学生的反馈和他们对任何科目表达的情绪可以帮助教师了解学生的学习行为。学生们通过社交网站、博客、论坛和学校调查系统等多个平台来表达自己的感受。然而,反馈通常包含数千个句子,并且来自不同的来源,这使得分析它们成为一项繁琐而乏味的工作。在这方面,已经提出了情感分析(SA)来自动化挖掘用户反馈为有价值信息的过程。本文在综合前人研究的基础上,论述了人工智能的原理、潜在效益及其在教育领域的应用。我们建议SA可以帮助讲师更容易地了解学生的需求和问题。特别提出了一个框架和绩效评估方法,以帮助指导SA在教育领域的实施。
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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