Publication Tag: Education

Emotions play a crucial role in learning and self-assessment processes, yet measuring them is challenging. This study evaluates the efficiency of FaceReader during a self-assessment test by comparing its instant measurements with researchers’ estimations of students’ emotions in real-time observations. Statistical analysis revealed some discrepancies between FaceReader’s and researchers’ assessments, particularly concerning ‘Disgusted’ and ‘Angry’ emotions. Overall, the results indicate that FaceReader can measure emotions with over 87% efficacy during self-assessment tests and could be effectively integrated into computer-aided learning systems for affect recognition. Additionally, the study provides valuable insights into students’ emotional states during self-assessment tests and learning procedures.

This study evaluates the synchronization of three emotional measurement methods—automatic facial expression recognition, self-report, and electrodermal activity—and their agreement regarding learners’ emotions. Data were collected from 67 undergraduates at a North American university who learned about a complex science topic while interacting with MetaTutor, a multi-agent computerized learning environment. Videos of learners’ facial expressions, captured with a webcam, were analyzed using automatic facial recognition software . Learners’ physiological arousal was recorded using Affectiva’s Q-Sensor 2.0 electrodermal activity measurement bracelet. Learners self-reported their experience of 19 different emotional states on five different occasions during the learning session, which were used as markers to synchronize data from FaceReader and Q-Sensor. The study found a high agreement between the facial and self-report data , but low levels of agreement between them and the Q-Sensor data, suggesting that a tightly coupled relationship does not always exist between emotional response components.

This paper extends traditional emotional measurement frameworks in Intelligent Tutoring Systems by examining co-occurring emotions through a novel methodological approach. The study analyzed the occurrence of students’ embodiment of basic single discrete emotions and COEs, in addition to neutral states, using the automatic facial expression recognition program FaceReader 4.0. Focusing on the sub-goal setting task during learners’ interaction with MetaTutor, where a pedagogical agent assisted students in setting three relevant sub-goals for their learning session, results indicated that neutral and sadness were the most experienced SDEs and the most represented emotions in COE pairs. COEs accounted for nearly a quarter of students’ embodied emotions.

This study investigates whether changes in facial microexpression states can identify moments of conceptual conflict, a pathway to conceptual change. Involving 102 university and high school students, approximately 80% held misconceptions on the chosen scientific topic. Findings revealed that most students who made erroneous predictions exhibited FMES changes during conceptual conflict. Notably, the absence of FMES change indicated a reduced likelihood of conceptual change, whereas its presence doubled this likelihood. These results suggest that FMES can effectively determine learners’ awareness of conflicting concepts and their progress toward scientific understanding. Educational implications are discussed.