Using principles of Oldenburg's concept of third place to characterize sense of community, this paper establishes a framework for the creation of sustainable, informal learning communities that promote dialogue and interpersonal relationships. This paper discusses Oldenburg's theory of third place, presents a framework to implement online third place communities in educational settings, and offers examples, recommendations and implications for future research. This paper provides a framework for third place development in online learning environments. We propose that virtual third places can meet all criteria established by Oldenburg. The development of virtual third places only occurs over time and violations of third place characteristics are part of the development process. However, these violations should be eliminated over time through continuous evaluation, modification, and implementation of the space, users, activities and organizational needs, goals and resources.
The main purpose of this study is to quantitatively identify the most recurrent ethical challenges instructors usually encounter in their online teaching and the most effective strategies to solve and avoid these challenges. Among the faculty members, 52% stated that they encountered certain ethical challenges in their teaching. Although ANOVA tests showed that there is no significant difference between instructors with different academic degrees and different teaching experiences. This shows that that the probability of these challenges could occur in online learning environments regardless instructors’ academic degree or online teaching experiences.
Recent years have witnessed an increased interest in online education, both massive open online courses (MOOCs) and small private online courses (SPOCs). This significant interest in online education has raised many challenges related to student engagement, performance, and retention assessments. With the increased demands and challenges in online education, several researchers have investigated ways to predict student outcomes, such as performance and dropout in online courses. This paper presents a comprehensive review of state-of-the-art studies that examine online learners’ data to predict their outcomes using machine and deep learning techniques. The contribution of this study is to identify and categorize the features of online courses used for learners’ outcome prediction, determine the prediction outputs, determine the strategies and feature extraction methodologies used to predict the outcomes, describe the metrics used for evaluation, provide a taxonomy to analyze related studies, and provide a summary of the challenges and limitations in the field.
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