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Kshitij Sharma; Serena Lee-Cultura; Sofia Papavlasopoulou; Michail Giannakos – Journal of Computer Assisted Learning, 2025
Background: Effort measurement is essential for adaptation to interactive learning technologies. Most contemporary technologies measure effort through the log data (reaction time and correctness). Some adaptive technologies use facial expressions and attention to adapt. Objectives: We present a novel, complementary, and multimodal definition of…
Descriptors: Academic Persistence, Educational Technology, Technology Uses in Education, Assistive Technology
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
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
Angelica Ronconi; Lucia Mason; Lucia Manzione; Anne Schüler – Journal of Computer Assisted Learning, 2025
Background: During digital reading on internet-connected devices, students may be exposed to a variety of on-screen distractions. Learning by reading can therefore become a fragmented experience with potentially negative consequences for reading processes and outcomes. Objectives: This study investigated the effects of on-screen distractions, as…
Descriptors: Eye Movements, Electronic Learning, Computer Uses in Education, Reading
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
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
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
Yuko Suzuki; Fridolin Wild; Eileen Scanlon – Journal of Computer Assisted Learning, 2024
Background: Cognitive load during AR use has been measured conventionally by performance tests and subjective rating. With the growing interest in physiological measurement using non-invasive biometric sensors, unbiased real-time detection of cognitive load in AR is expected. However, a range of sensors and parameters are used in various subject…
Descriptors: Computer Simulation, Cognitive Processes, Difficulty Level, Physiology
van Marlen, Tim; van Wermeskerken, Margot; Jarodzka, Halszka; Raijmakers, Maartje; van Gog, Tamara – Journal of Computer Assisted Learning, 2022
Background: Eye movement modelling examples (EMME) are demonstrations in which learners' not only see a model's (e.g., a teacher's) task performance on a computer screen (as in regular video examples) but also the model's eye movements (represented as moving coloured dots overlaid on the screen). Thereby EMME help guide learners' attention towards…
Descriptors: Eye Movements, Logical Thinking, Technology Uses in Education, Task Analysis
Stárková, Tereza; Lukavský, Jirí; Javora, Ondrej; Brom, Cyril – Journal of Computer Assisted Learning, 2019
Anthropomorphizing graphical elements in multimedia learning materials improves learning outcomes. The reasons for enhanced learning are unclear. We extended a seminal anthropomorphism study in order to examine whether the effect of anthropomorphisms on learning outcomes, both immediate and delayed, is caused by the anthropomorphized elements'…
Descriptors: Multimedia Instruction, Educational Technology, Technology Uses in Education, Learning
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
Baceviciute, Sarune; Lucas, Gordon; Terkildsen, Thomas; Makransky, Guido – Journal of Computer Assisted Learning, 2022
Background: The increased availability of immersive virtual reality (IVR) has led to a surge of immersive technology applications in education. Nevertheless, very little is known about how to effectively design instruction for this new media, so that it would benefit learning and associated cognitive processing. Objectives: This experiment…
Descriptors: Computer Simulation, Simulated Environment, Eye Movements, Diagnostic Tests
Jiarui Hou; James F. Lee; Stephen Doherty – Journal of Computer Assisted Learning, 2025
Background: Recent research has demonstrated the potential of mobile-assisted learning to enhance learners' learning outcomes. In contrast, the learning processes in this regard are much less explored using eye tracking technology. Objective: This systematic review study aims to synthesise the relevant work to reflect the current state of eye…
Descriptors: State of the Art Reviews, Eye Movements, Electronic Learning, Handheld Devices
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
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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