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What Do Linguistic Expressions Tell Us about Learners’ Confusion? A Domain-Independent Analysis in MOOCs
IEEE Transactions on Learning Technologies ( IF 3.7 ) Pub Date : 2020-09-29 , DOI: 10.1109/tlt.2020.3027661 Thushari Atapattu , Katrina Falkner , Menasha Thilakaratne , Lavendini Sivaneasharajah , Rangana Jayashanka
IEEE Transactions on Learning Technologies ( IF 3.7 ) Pub Date : 2020-09-29 , DOI: 10.1109/tlt.2020.3027661 Thushari Atapattu , Katrina Falkner , Menasha Thilakaratne , Lavendini Sivaneasharajah , Rangana Jayashanka
The substantial growth of online learning, and in particular, through massively open online courses (MOOCs), supports research into nontraditional learning contexts. Learners’ confusion is one of the identified aspects which impact the overall learning process, and ultimately, course attrition. Confusion for a learner is an individual state of bewilderment and uncertainty as to how to move forward. The majority of recent works in MOOCs neglects the individual factor and measure the influence of community-related aspects (e.g., votes
, views
) for confusion classification. While these are useful measures, these models neglect the personalized context, such as an individual's affect or emotions. Toward this, and within the MOOC context, we propose machine learning models based solely on language and discourse features extracted from learners’ discussion posts. With over 83% F1-score in all domains, it is evident that linguistic-only features are highly effective in confusion classification, with our models outperforming previous models developed for confusion classification. In this article, we also obtain good performance for cross-domain confusion classification between 70.7% and 84.5% F1-score for all domain pairs (i.e., training on one domain and testing on another domain). Primarily, this article contributes through the development of a novel linguistic feature set that is predictive for effective confusion classification.
中文翻译:
语言表达告诉我们关于学习者困惑的什么?MOOC中与域无关的分析
在线学习的显着增长,尤其是通过大规模开放在线课程(MOOC)的支持,支持了对非传统学习环境的研究。学习者的混乱 是已确定的方面之一,会影响整个学习过程,并最终影响课程损耗。学习者的困惑是如何前进的困惑和不确定性的个体状态。MOOC中的大多数最新作品都忽略了个人 评估和衡量社区相关方面的影响(例如, 票数
, 意见
)进行混淆分类。尽管这些措施很有用,但这些模型忽略了个性化的环境,例如个人的情感或情感。为此,在MOOC上下文中,我们提出了仅基于从学习者的讨论帖子中提取的语言和话语特征的机器学习模型。在所有领域中,F1得分均超过83%,这很明显仅语言 这些功能在混淆分类中非常有效,我们的模型优于以前为混淆分类开发的模型。在本文中,对于所有域对,F1分数在70.7%和84.5%之间的跨域混淆分类(即,在一个域上进行训练并在另一域上进行测试)也获得了良好的性能。首先,本文通过开发可预测有效混淆分类的新型语言功能集做出了贡献。
更新日期:2020-09-29
中文翻译:
语言表达告诉我们关于学习者困惑的什么?MOOC中与域无关的分析
在线学习的显着增长,尤其是通过大规模开放在线课程(MOOC)的支持,支持了对非传统学习环境的研究。学习者的