Computational models that automatically detect learners' affective states are powerful tools for investigating the interplay of affect and learning. Over the past decade, affect detectors--which recognize learners' affective states at run-time using behavior logs and sensor data--have advanced substantially across a range of K-12 and postsecondary…

Increased attention to the relationships between affect and learning has led to the development of machine-learned models that are able to identify students' affective states in computerized learning environments. Data for these affect detectors have been collected from multiple modalities including physical sensors, dialogue logs, and logs of…

This paper addresses the affective human experiences in terms of the emotions of South African teacher-students while attaining computer competencies for teaching and learning, and for ODL. The full mixed method study investigated how computers contribute towards affective experiences of disadvantaged teacher-students. The purposive sample related…

Used the language intensity/message affect coding metric developed by R. Rogan and M. Hammer to evaluate variability in the language of 160 Euro-American and African-American college students responding to a free-response scenario. Results indicate significant differences in the level of affect present in messages communicated by the two groups.…