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Article Citation - WoS: 156Citation - Scopus: 192An Eye-Tracking Study of How Color Coding Affects Multimedia Learning(Pergamon-elsevier Science Ltd, 2009) Ozcelik, Erol; Karakus, Turkan; Kursun, Engin; Cagiltay, KursatColor coding has been proposed to promote more effective learning. However, insufficient evidence currently exists to show how color coding leads to better learning. The goal of this study was to investigate the underlying cause of the color coding effect by utilizing eye movement data. Fifty-two participants studied either a color-coded or conventional format of multimedia instruction. Eye movement data were collected during the study. The results indicate that color coding increased retention and transfer performance. Enhancement of learning by color coding was due to efficiency of locating corresponding information between illustration and text. Color coding also attracted attention of learners to perceptually salient information. (C) 2009 Elsevier Ltd. All rights reserved.Article Citation - WoS: 207Citation - Scopus: 240Why Does Signaling Enhance Multimedia Learning? Evidence From Eye Movements(Pergamon-elsevier Science Ltd, 2010) Ozcelik, Erol; Arslan-Ari, Ismahan; Cagiltay, KursatPrevious studies have suggested that signaling enhances multimedia learning. However, there is not enough evidence showing why signaling leads to better performance. The goal of this study was to examine the effects of signaling on learning outcomes and to reveal the underlying reasons for this effect by using eye movement measures. The participants were 40 undergraduate students who were presented with either signaled or nonsignaled multimedia materials. Labels in the illustration were signaled by temporarily changing the color of the items. The results suggest that the signaled group outperformed the nonsignaled group on transfer and matching tests. Eye movement data shows that signaling guided attention to relevant information and improved the efficiency and effectiveness of finding necessary information. (C) 2009 Elsevier Ltd. All rights reserved.Article Recognition and Misclassification Patterns of Basic Emotional Facial Expressions: An Eye-Tracking Study in Young Healthy Adults(MDPI, 2025) Alkan, NeşeAccurate recognition of basic facial emotions is well documented, yet the mechanisms of misclassification and their relation to gaze allocation remain under-reported. The present study utilized a within-subjects eye-tracking design to examine both accurate and inaccurate recognition of five basic emotions (anger, disgust, fear, happiness, and sadness) in healthy young adults. Fifty participants (twenty-four women) completed a forced-choice categorization task with 10 stimuli (female/male poser x emotion). A remote eye tracker (60 Hz) recorded fixations mapped to eyes, nose, and mouth areas of interest (AOIs). The analyses combined accuracy and decision-time statistics with heatmap comparisons of misclassified versus accurate trials within the same image. Overall accuracy was 87.8% (439/500). Misclassification patterns depended on the target emotion, but not on participant gender. Fear male was most often misclassified (typically as disgust), and sadness female was frequently labeled as fear or disgust; disgust was the most incorrectly attributed response. For accurate trials, decision time showed main effects of emotion (p < 0.001) and participant gender (p = 0.033): happiness was categorized fastest and anger slowest, and women responded faster overall, with particularly fast response times for sadness. The AOI results revealed strong main effects and an AOI x emotion interaction (p < 0.001): eyes received the most fixations, but fear drew relatively more mouth sampling and sadness more nose sampling. Crucially, heatmaps showed an upper-face bias (eye AOI) in inaccurate trials, whereas accurate trials retained eye sampling and added nose and mouth AOI coverage, which aligned with diagnostic cues. These findings indicate that the scanpath strategy, in addition to information availability, underpins success and failure in basic-emotion recognition, with implications for theory, targeted training, and affective technologies.
