Developing Coding Structures For Becoming Affect-Savy in the Fully Online Community Model
This study aims to provide support for the efficacy of the Fully Online Learning Community (FOLC) Model by examining communication between participants within a series of recorded online focus groups and by investigating the behaviours that are undertaken by participants. A coding system based on body language expressions is proposed as an outcome of this study and the affective domain of the participants is analyzed through facial expressions, body language and content (words) employed. Findings suggest that affects (emotions) have a preeminent role in the social presence in FOLC environments. Positive emotions are easier to detect as individuals exhibit them without masking, with some possible exceptions arising from personal dispositions and cultural inferences. Negative emotions can also be detected through a combination of facial expressions and body language coding. However, findings were not consistent for determining sadness and surprise states and further studies will have to explore ways to differentiate these affects from others. The instigations set forward by the participants and affective responses to the behaviours of instigators provided support for the empirical study about the efficacy of facilitation and interactions within fully online learning environments.