Pub Date : 2024-01-24DOI: 10.3390/informatics11010006
Sofía Ramos-Pulido, Neil Hernández-Gress, Gabriela Torres-Delgado
Current research on the career satisfaction of graduates limits educational institutions in devising methods to attain high career satisfaction. Thus, this study aims to use data science models to understand and predict career satisfaction based on information collected from surveys of university alumni. Five machine learning (ML) algorithms were used for data analysis, including the decision tree, random forest, gradient boosting, support vector machine, and neural network models. To achieve optimal prediction performance, we utilized the Bayesian optimization method to fine-tune the parameters of the five ML algorithms. The five ML models were compared with logistic and ordinal regression. Then, to extract the most important features of the best predictive model, we employed the SHapley Additive exPlanations (SHAP), a novel methodology for extracting the significant features in ML. The results indicated that gradient boosting is a marginally superior predictive model, with 2–3% higher accuracy and area under the receiver operating characteristic curve (AUC) compared to logistic and ordinal regression. Interestingly, concerning low career satisfaction, those with the worst scores for the phrase “how frequently applied knowledge, skills, or technological tools from the academic training” were less satisfied with their careers. To summarize, career satisfaction is related to academic training, alumni satisfaction, employment status, published articles or books, and other factors.
目前对毕业生职业满意度的研究限制了教育机构制定获得高职业满意度的方法。因此，本研究旨在根据对大学校友调查收集到的信息，利用数据科学模型来了解和预测职业满意度。数据分析采用了五种机器学习（ML）算法，包括决策树、随机森林、梯度提升、支持向量机和神经网络模型。为了达到最佳预测效果，我们利用贝叶斯优化法对五种 ML 算法的参数进行了微调。我们将五种 ML 模型与逻辑回归和序数回归进行了比较。然后，为了提取最佳预测模型中最重要的特征，我们采用了 SHapley Additive exPlanations（SHAP），这是一种提取 ML 中重要特征的新方法。结果表明，梯度提升是一种略胜一筹的预测模型，与逻辑回归和序数回归相比，其准确率和接受者工作特征曲线下面积（AUC）高出 2-3%。有趣的是，关于职业满意度低的问题，在 "如何经常应用学术培训中的知识、技能或技术工具 "这一短语上得分最差的人对自己的职业满意度较低。总之，职业满意度与学术培训、校友满意度、就业状况、发表的文章或书籍以及其他因素有关。
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Pub Date : 2024-01-15DOI: 10.3390/informatics11010005
Charlee Kaewrat, D. Anopas, Si Thu Aung, Yunyong Punsawad
This study presents an augmented reality application for training chest electrocardiography electrode placement. AR applications featuring augmented object displays and interactions have been developed to facilitate learning and training of electrocardiography (ECG) chest lead placement via smartphones. The AR marker-based technique was used to track the objects. The proposed AR application can project virtual ECG electrode positions onto the mannequin’s chest and provide feedback to trainees. We designed experimental tasks using the pre- and post-tests and practice sessions to verify the efficiency of the proposed AR application. The control group was assigned to learn chest ECG electrode placement using traditional methods, whereas the intervention group was introduced to the proposed AR application for ECG electrode placement. The results indicate that the proposed AR application can encourage learning outcomes, such as chest lead ECG knowledge and skills. Moreover, using AR technology can enhance students’ learning experiences. In the future, we plan to apply the proposed AR technology to improve related courses in medical science education.
本研究介绍了一款用于培训胸部心电图电极放置的增强现实应用。我们开发了以增强对象显示和交互为特色的 AR 应用程序，以促进通过智能手机学习和训练心电图（ECG）胸导联置放。使用基于 AR 标记的技术来跟踪物体。拟议的 AR 应用程序可将虚拟心电图电极位置投射到人体模型的胸部，并向学员提供反馈。我们设计了实验任务，利用前测、后测和练习环节来验证所提议的 AR 应用程序的效率。对照组被分配使用传统方法学习胸腔心电图电极放置，而干预组则被介绍使用拟议的 AR 应用程序进行心电图电极放置。结果表明，拟议的 AR 应用程序可促进学习成果，如胸导联心电图知识和技能。此外，使用 AR 技术还能增强学生的学习体验。今后，我们计划将所提出的 AR 技术应用于医学教育的相关课程中。
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