Abstract.Many massive open online courses (MOOCs) offer mainly videobased lectures, which limits the opportunity for interactions and communications among students and instructors. Thus, the discussion forums of MOOC become indispensable in providing a platform for facilitating interactions and communications. In this research, discussion forum users who continually and actively participate in the forum discussions throughout the course are identified. We then employ different measures for evaluating whether those active users have more influence on overall forum activities. We further analyze forum votes, both positive and negative, on posts and comments to verify if active users make positive contributions to the course conversations. Based the result of analysis, users who constantly participate in forum discussions are identified as statistically more influential users, and these users also produce a positive effect on the discussions. Implications for MOOC student engagement and retention are discussed.

focusing on the intersection of technology and pedagogy. Barton works collaboratively with faculty across disciplines to explore how emerging technologies and trends, such as MOOCs, digital badges, and learning analytics, impacts both students and instructors.

The objective of this research is to mine textual data (e.g., online discussion forums) generated by students enrolled in Massive Open Online Courses (MOOCs) in order to quantify students’ sentiment, in relation to their course performance. Massive Open Online Courses (MOOCs) are free to anyone with a computing device and a means of connecting to the internet and serve as a new paradigm for distance based education. While student interactions in traditional based brick and mortar classes are readily observable by students and instructors, quantifying the sentiments expressed by students in MOOCs remains challenging. This is in part due to the quantity of textual data being generated by students enrolled in MOOCs, in addition to a lack of quantitative methodologies that discover latent, previously unknown knowledge pertaining to student interactions and sentiments in the digital world. The authors of this work introduce a data mining driven methodology that employs natural language processing techniques and text mining algorithms to quantify students’ sentiments, based on their textual data provided during course assignment discussions. The researchers of this work aim to help educators understand the factors that may impact student performance, team interactions and overall learning outcomes in digital environments such as MOOCs.

While discussion forums in online courses have been studied in the past, no one has proposed a model linking messages in discussion forums to a learning taxonomy, even though forums are widely used as educational tools in online courses. In this research, we view forums as information seeking events and use a keyword taxonomy approach to analyze a large amount of MOOC forum data to identify the types of learning interactions taking place in forum conversations. Using 51,761 forum messages from 8,169 forum threads from a MOOC with a 50,000+ enrollment, messages are analyzed based on levels of Bloom's Taxonomy to categorize the scholarly discourse. The results of this research show that interactions within MOOC discussion forums are a learning process with unique characteristics specific to particular cognitive learning levels. Results also imply that different types of forum interactions have characteristics relevant to particular learning levels, and the volume of higher levels of cognitive learning incidents increase as the course progresses.

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