User Experience and Learning in Video-Based and Computer-Vision-Based CPR (CVCPR) Training Dataset
Description
[Dataset Description] This dataset contains user experience and learning response data collected from a randomized between-subjects experiment comparing two CPR (cardiopulmonary resuscitation) education approaches: conventional video-based CPR education and computer-vision-based CPR training. A total of 60 adults aged 19–29 participated in the experiment and were randomly assigned to two independent conditions, with 30 participants in each condition. The control group received CPR education through a conventional instructional video, while the experimental group used a computer-vision-based CPR training system using real-time skeleton detection. The system detected participants’ body movements through a camera and provided real-time visual feedback during CPR practice, including feedback related to compression posture, depth, and pace. The dataset includes participant demographics and pre-experiment responses concerning prior technology experience, interest in CPR, CPR confidence, procedural knowledge, previous training difficulty, and prior CPR training experience. Following the assigned training condition, participants completed a 10-item knowledge acquisition assessment and 7-point Likert-scale measures covering Cognitive Load (CL), Self-Efficacy (SE), Academic Interest (AI), Motivation (MO), Immersion (IM), Learnability (LE), and Preference (PR). These measures represent cognitive, motivational, and experiential dimensions of the CPR learning experience. The dataset also includes responses to five open-ended questions addressing participants’ overall impressions, perceived advantages and disadvantages of the training method, willingness and perceived ability to perform CPR in a future emergency, and suggestions for improvement. These qualitative responses complement the quantitative measures by capturing participants’ perceptions of feedback, interaction, realism, usability, and hands-on learning. Overall, the dataset supports comparative research on video-based and computer-vision-based CPR education, particularly in relation to learning outcomes, cognitive load, self-efficacy, motivation, immersion, user experience, and interactive emergency training. Keywords: CPR Education; Computer Vision; Skeleton Detection; Video-Based Learning; User Experience; Real-Time Feedback; Self-Efficacy; Cognitive Load; Immersion; Interactive Learning; Emergency Training
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Institutions
- University of SeoulSeoul, Seoul