Professors' Concerns after the Shift from Face-to-face to Online Teaching amid COVID-19 Contingency: An Educational Data Mining analysis

The Covid-19 outbreak forced education into a distance modality. Professors and educators suddenly confronted unexpected challenges, including a lack of technical skills to implement efficient pedagogies in this modality. One rescuing element was social media, increasingly used inside organizations. It allows users to create content and provide valuable information on human interactions and collective behavior, mainly textual data. The objective of the current research was to identify professorial concerns after the shift to distance education and the first 15 months of confinements. Specifically, we analyzed the comments expressed in the social networks by more than 5,700 faculty members of a Mexican private university that implemented online teaching. Applying Educational Data Mining to 680 remarks retrieved from the social network, we used Voyant Tools and R programming for text and sentiment analysis. The results evidenced that the professors created a kind social network, sharing tips and digital media as educational resources, which led to a natural learning curve for developing online teaching competencies. Other relevant findings included the need to provide the professors continuous training in communication and learning management platforms to engage in ongoing discussions on topics, such as whether turning on the cameras should be compulsory during online lectures. This work's results have value to higher education institutions and professors seeking a better understanding of their requirements and decision-making to improve education delivery under current and future constraints.