Mental wellbeing of university students is a growing concern that has been worsening during the COVID-19 pandemic. Numerous studies have gathered empirical data to explore the mental health impact of the pandemic on university students and investigate factors associated with higher levels of distress. While the online questionnaire survey has been a prevalent means to collect data, regression analysis has been observed a dominating approach to interpret and understand the impact of independent factors on a mental wellbeing state of interest. Drawbacks such as sensitivity to outliers, ineffectiveness in case of multiple predictors highly correlated may limit the use of regression in complex scenarios. These observations motivate the underlying research to propose alternative computational methods to investigate the questionnaire data. Inspired by recent machine learning advances, this research aims to construct a framework through feature permutation importance to empower the application of a variety of machine learning algorithms that originate from different computational frameworks and learning theories, including algorithms that cannot directly provide exact numerical contributions of individual factors. This would enable to explore quantitative impact of predictors in influencing student mental wellbeing from multiple perspectives as a result of using different algorithms, thus complementing the single view due to the dominant use of regression. Applying the proposed approach over an online survey in a UK university, the analysis suggests the past medical record and wellbeing history and the experience of adversity contribute significantly to mental wellbeing states; and the frequent communication with families and friends to keep good relationship as well as regular exercise are generally contributing to improved mental wellbeing.

中文翻译：

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调查英国大学 COVID-19 大流行期间大学生的心理健康状况：使用特征排列重要性的机器学习方法

大学生的心理健康日益受到关注，在 COVID-19 大流行期间，这种健康状况一直在恶化。许多研究收集了经验数据，以探讨大流行对大学生心理健康的影响，并调查与较高程度的痛苦相关的因素。虽然在线问卷调查一直是收集数据的普遍手段，但回归分析已被认为是解释和理解独立因素对感兴趣的心理健康状态影响的主要方法。对异常值敏感、多个预测变量高度相关时无效等缺点可能会限制回归在复杂场景中的使用。这些观察结果激发了基础研究提出替代计算方法来调查问卷数据。受最新机器学习进展的启发，本研究旨在通过特征排列重要性构建一个框架，以赋能源自不同计算框架和学习理论的各种机器学习算法的应用，包括无法直接提供个体精确数值贡献的算法因素。这将能够通过使用不同的算法从多个角度探索预测因素对影响学生心理健康的定量影响，从而补充由于回归的主要使用而导致的单一观点。将所提出的方法应用于英国一所大学的在线调查，分析表明过去的医疗记录和健康史以及逆境经历对心理健康状态有显着贡献；与家人和朋友经常沟通以保持良好的关系以及定期锻炼通常有助于改善心理健康。