Longitudinal trends in sentiment polarity and readability of an online masters of computer science course

Ida Camacho, Ashok K. Goel
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引用次数: 7

Abstract

In four years, the Georgia Tech Online MS in CS (OMSCS) program has grown from 200 students to over 6000. Despite early evidence of success, there is a need to evaluate the program's effectiveness. In this paper, we focus on trends from Fall 2014 to Fall 2017 in the on-campus and online sections of one OMSCS course, Knowledge-Based Artificial-Intelligence (KBAI). We leverage sentiment analysis and readability assessments to quantify the evolving quality of discourse on the online forum discussions of the various sections. The research was conducted as a longitudinal study, and aims to evaluate the success of the KBAI course by comparing trends between residential and online sections. Despite slight downward trends in online discourse quality and sentiment polarity, our results suggest that the growing OMSCS program has been successful in replicating the quality of learning experienced by on-campus students in the KBAI course.
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计算机科学硕士在线课程情感极性与可读性的纵向趋势
在四年的时间里,佐治亚理工学院在线计算机科学硕士(OMSCS)项目的学生人数从200人增长到6000多人。尽管有早期成功的证据,但仍有必要评估该计划的有效性。在本文中,我们重点关注了从2014年秋季到2017年秋季,OMSCS课程“基于知识的人工智能”(KBAI)的校园和在线部分的趋势。我们利用情感分析和可读性评估来量化各部分在线论坛讨论的话语质量。该研究是一项纵向研究,旨在通过比较校内课程和在线课程的趋势来评估KBAI课程的成功程度。尽管在线话语质量和情感极性略有下降,但我们的研究结果表明,不断增长的OMSCS项目在复制校园学生在KBAI课程中体验到的学习质量方面取得了成功。
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