Investigating transitions in affect and activities for online learning interventions

• Main Authors: Inventado, Paul Salvador B., Legaspi, Roberto S., Suarez, Merlin Teodosia C., Numao, Masayuki
• Format: text
• Published: Animo Repository 2011
• Subjects: Learning; Education—Data processing; Computer-assisted instruction; Computer Sciences
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/1472
• Institution: De La Salle University

In this paper we investigated student online learning and non-learning related activities. The data collected in the research showed that students felt certain affective states when performing particular activity types and performed particular activity types when they felt certain affective states. These transitions were further investigated by generating transition likelihoods between all pairs of activity types and affective states. The transition likelihoods were used to create a model that could predict possible student behavior when they learn online. Certain transitions wherein students may need interventions were identified, so that feedback can be put in place to prevent them from transitioning to activity types and affective states that do not support learning.