The present study aimed to evaluate the effects of virtual reality (VR) simulations combined with bedside assignments on nurses’ self-efficacy in providing pre-treatment educational services. Between March 2019 and November 2020, we conducted a study of VR educational materials that were developed to cover information about the treatment of oral cancers. The effects of the VR simulation, the thinking-path tracking map method, and bedside assignments on the nurses’ treatment decision-related knowledge were evaluated in a ward for oral cancer patients at Taipei Veterans General Hospital, Taipei, Taiwan. The blended training model significantly increased nurses’ familiarity (P<0.01) and confidence (P<0.03) regarding their knowledge of treatments and treatment decision-related knowledge. This model also significantly increased their confidence in their skills in bedside pre-treatment education for admitted oral cancer patients (P<0.002). Oral cancer-specific VR materials enhanced the effectiveness of skills training among nurses in the oral cancer ward.
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Purpose Acceptance of a learning technology affects students’ intention to use that technology, but the influence of the acceptance of a learning technology on learning approaches has not been investigated in the literature. A deep learning approach is important in the field of health, where links must be created between skills, knowledge, and habits. Our hypothesis was that acceptance of a hybrid learning model would affect students’ way of learning.
Methods We analysed these concepts, and their correlations, in the context of a flipped classroom method using a local learning management system. In a sample of all students within a single year of study in the midwifery program (n= 38), we used 3 validated scales to evaluate these concepts (the Study Process Questionnaire, My Intellectual Work Tools, and the Hybrid E-Learning Acceptance Model: Learner Perceptions).
Results Our sample had a positive acceptance of the learning model, but a neutral intention to use it. Students reported that they were distractible during distance learning. They presented a better mean score for the deep approach than for the superficial approach (P< 0.001), which is consistent with their declared learning strategies (personal reorganization of information; search and use of examples). There was no correlation between poor acceptance of the learning model and inadequate learning approaches. The strategy of using deep learning techniques was moderately correlated with acceptance of the learning model (rs= 0.42, P= 0.03).
Conclusion Learning approaches were not affected by acceptance of a hybrid learning model, due to the flexibility of the tool. However, we identified problems in the students’ time utilization, which explains their neutral intention to use the system.
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