Effects of recommendations on message quality and community formation in online conversations.

As asynchronous online discussions gain a broader usage due to the COVID-19 epidemic, the need for understanding of students' ideas from unstructured textual data becomes more pressing. In this experimental study, we examine the effects of recommendations on message quality and community formation from voluminous online discussions. Drawing on literature from group cognition, knowledge building discourse, and learning analytics, we calculate message quasi-quality index (QQI) scores based on message lexical complexity and topic-related keyword usage by participants in explaining their ideas.

Health information text characteristics.

Millions of people search online for medical text, but these texts are often too complicated to understand. Readability evaluations are mostly based on surface metrics such as character or words counts and sentence syntax, but content is ignored. We compared four types of documents, easy and difficult WebMD documents, patient blogs, and patient educational material, for surface and content-based metrics.