Affect Analysis: A Literature Survey on Student-Specific and General Users’ Affect Analysis

The increasing significance of understanding users’ experiences during computer interactions has prompted a growing need for focused attention on affect analysis. Affect analysis, involving the scrutiny of users’ exhibited moods and emotions during these interactions, stands as a crucial avenue for comprehending the nuanced aspects of user engagement. This research intends to explore different approaches that tackle the intricacies of affect analysis, with an emphasis on both online learning environments and other general contexts. Through an assessment of various literature, the study investigates databases, methods, and approaches used in affect analysis, with a focus on how important it is to find appropriate procedures. The findings reveal a notable under-utilization of certain datasets-such as written texts, speech, body language, and multimodal data-for the study of learner affect, but with a predominant application in general affect analysis. This underscores the need for a more targeted exploration of these datasets in the context of student affect analysis. The study contributes valuable insights into the existing methodologies for affect analysis and emphasizes the critical need for further research in the realm of learner affect analysis. The review underscores the potential benefits of harnessing untapped datasets for a more comprehensive understanding of the intricate interplay between affect and learning experiences. This article serves as a comprehensive review of investigations and developments in the evolving field of affect analysis, particularly within the context of online learning environments that have the potential to broaden human-computer interaction settings.