Seminar | Researching Affective Response through Facial Expressions: A New Status Quo?

Fluctuations in learners' affective responses (e.g., enjoyment, anxiety, boredom, willingness to communicate) during task performance impact what gets processed, how it is stored in memory, and its availability for future retrieval (Kensinger & Holland, 2013). However, affective responses are difficult measure (Lambert & Aubrey, 2023), and SLA researchers have relied primarily on self-reports (post-task questionnaires & interviews). Unfortunately, these methods result in bias toward the most recent or most salient events in task performances and are limited to conscious experiences, while much affective variation impacting performance and learning takes place below the level of conscious awareness (Hajcak et al., 2012). An advance in this line of research is the idiodynamic method (IDM) in which learners rate their task performances on a moment-to-moment basis during stimulated video recalls and are interviewed on emergent patterns in their ratings (MacIntyre, 2023). However, implementing IDM is time-consuming. This talk will discuss the affordances of automated facial expression analysis (FEA) software which analyses learners' affective responses during task performance in real-time at a sampling rate of 30 times per second through a web-camera as learners perform tasks. FEA will be introduced together with some of its recent on task performance and memory (Lambert, 2025; Lambert & Aubrey, 2025), and its advantages and disadvantages as a new standard for measuring affective response in future SLA research will be discussed.