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.

The authors of this work propose a virtual reality approach that overcomes two fundamental challenges experienced in physical learning environments; i) variations in audial quality, and ii) variations in visual quality, in an effort to achieve individual customization of information content. In physical brick and mortar environments, the dissemination of information is influenced by the medium that the information travels through, which is typically distorted by line of sight constraints and constraints that distort sound waves. The fundamental research question is how to achieve consistent quality of information being disseminated, as the number of audience members increases? There exists a knowledge gap relating to the creation of a scalable, networked, system for enabling real time, information exchange. The authors propose a virtual reality approach to address these limitations of physical learning spaces that minimizes the variability in audial and visual information dissemination. A real time, networked architecture is proposed that enables multiple individuals to simultaneously experience the same quality of audial and visual information, based on the optimal geospatial position for audial and visual exposure determined. A case study is introduced that first quantifies simulations of the audial and visual information loss experienced by audience members receiving information at different geospatial locations in a brick and mortar environment. This information loss is compared against the proposed virtual reality architecture that minimizes the variation in information dissemination. The authors demonstrate that the proposed solution is an improved, scalable multi-user system, unlike brick and mortar environments that are constrained by size and geospatial positioning.