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Thao Pham; Wu-Yuin Hwang; Xuan-Lam Pham – Journal of Computer Assisted Learning, 2024
Background: Examining student attention in physical classrooms is crucial, but it faces challenges due to the lack of accurate monitoring. Constraints posed by device limitations and the design of educational materials impede the integration of eye-tracking technology in these settings. Objectives: This study aims to (1) develop a wearable…
Descriptors: Attention, Eye Movements, Physical Environment, Classroom Environment
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Schüler, Anne; Merkt, Martin – Journal of Computer Assisted Learning, 2021
In two experiments, the multimedia contradiction paradigm was used to investigate whether learners map information conveyed through the audio and the picture track of a video. In Experiment 1 (N = 85), the information conveyed through the audio track and the picture track was always consistent (control group) or was made inconsistent by changing…
Descriptors: Video Technology, Cognitive Processes, Multimedia Materials, Eye Movements
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Meier, Julius; Jong, Bastian; Montfort, Dorien Preusterink; Verdonschot, Anouk; Wermeskerken, Margot; Gog, Tamara – Journal of Computer Assisted Learning, 2023
Background: There are only few guidelines on how instructional videos should be designed to optimize learning. Recently, the effects of social cues on attention allocation and learning in instructional videos have been investigated. Due to inconsistent results, it has been suggested that the visual complexity of a video influences the effect of…
Descriptors: Video Technology, Cues, Attention, Social Influences
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Xie, Heping; Zhao, Tingting; Deng, Sue; Peng, Ji; Wang, Fuxing; Zhou, Zongkui – Journal of Computer Assisted Learning, 2021
Eye movement modelling examples (EMME) are computer-based videos displaying the visualized eye gaze behaviour of a domain expert person (model) while carefully executing the learning or problem-solving task. The role of EMME in promoting cognitive performance (i.e., final scores of learning outcome or problem solving) has been questioned due to…
Descriptors: Eye Movements, Attention, Cognitive Ability, Learning Processes
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Ziyi Kuang; Fuxing Wang; Frank Andrasik; Xiangen Hu – Journal of Computer Assisted Learning, 2024
Background: Little is known about the effectiveness of instructors when presenting content in videos alone. In recent years, researchers have increasingly begun to explore the effects of instructors' social cues (e.g., eye gaze, body orientation, etc.) on learning. However, previous studies exploring the effects of eye gaze have confounded the…
Descriptors: Teacher Behavior, Eye Movements, Human Body, Teacher Effectiveness
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Chisari, Lucia B.; Mockeviciute, Akvile; Ruitenburg, Sterre K.; van Vemde, Lian; Kok, Ellen M.; van Gog, Tamara – Journal of Computer Assisted Learning, 2020
Eye movement modelling examples (EMMEs) are instructional videos of a model's demonstration and explanation of a task that also show where the model is looking. EMMEs are expected to synchronize students' visual attention with the model's, leading to better learning than regular video modelling examples (MEs). However, synchronization is seldom…
Descriptors: Eye Movements, Video Technology, Models, Attention
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Eder, Thésése F.; Scheiter, Katharina; Richter, Juliane; Keutel, Constanze; Hüttig, Fabian – Journal of Computer Assisted Learning, 2022
Background: When interpreting medical images such as dental panoramic radiographs (Orthopanthomogram, OPT), errors are frequent. Previous research has shown that eye movement modelling examples (EMME) are a supportive training method for medical image interpretation to reduce errors. To date, EMME support for OPTs has not been verified.…
Descriptors: Eye Movements, Dentistry, Visual Aids, Radiology
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Yawen Shi; Zengzhao Chen; Mengke Wang; Shaohui Chen; Jianwen Sun – Journal of Computer Assisted Learning, 2024
Background: Guided gaze is the instructor's gaze towards teaching materials to guide students' attention, and it plays a vital role in enhancing video-based education. The duration of guided gaze, indicating how long instructors focus on teaching materials, varies based on the lecture design. Nevertheless, the impact of varying durations of guided…
Descriptors: Teacher Behavior, Eye Movements, Lecture Method, Video Technology
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Kok, Ellen; Hormann, Olle; Rou, Jeroen; Saase, Evi; der Schaaf, Marieke; Kester, Liesbeth; Gog, Tamara – Journal of Computer Assisted Learning, 2022
Background: Performance monitoring plays a key role in self-regulated learning, but is difficult, especially for complex visual tasks such as navigational map reading. Gaze displays (i.e. visualizations of participants' eye movements during a task) might serve as feedback to improve students' performance monitoring. Objectives: We hypothesized…
Descriptors: Metacognition, Eye Movements, Task Analysis, Visualization
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Huh, Dami; Kim, Ji-Hyun; Jo, Il-Hyun – Journal of Computer Assisted Learning, 2019
One of the golden rules in instructional design methods is to optimize the use of working memory capacity and avoid cognitive overload. The study of cognitive load has historically relied on one's introspection. However, it is difficult to capture changes in cognitive load levels during learning sensitively. This paper suggests an approach to…
Descriptors: Cognitive Processes, Difficulty Level, Change, Video Technology
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Wang, Hongyan; Pi, Zhongling; Hu, Weiping – Journal of Computer Assisted Learning, 2019
Instructor behaviour is known to affect learning performance, but it is unclear which specific instructor behaviours can optimize learning. We used eye-tracking technology and questionnaires to test whether the instructor's gaze guidance affected learners' visual attention, social presence, and learning performance, using four video lectures:…
Descriptors: Video Technology, Lecture Method, Nonverbal Communication, Eye Movements