Multimodal Psychological Dataset for Engineering Students in Art Therapy Research

In recent years, mental health issues among engineering students have become increasingly prominent, with academic stress and emotional suppression leading to widespread negative psychological states such as anxiety and depression. As a non-pharmacological intervention, art therapy is believed to have the potential to promote psychological recovery and enhance well-being, thanks to its emotional regulation and self-expression characteristics. However, there is currently a lack of multimodal psychological data on art therapy for engineering students, which limits in-depth research in this field. To address this, we present a multimodal psychological dataset for engineering students focused on art therapy research, systematically documenting the psychological changes and multi-source behavioral patterns of 26 participants before and after art therapy. The dataset follows a "pre-test—intervention—post-test" design, with questionnaires covering scales for anxiety, depression, subjective well-being, perceived stress, personality traits, and specific art therapy experiences. It also includes videos of the therapy process and subjective experience texts. Importantly, this dataset does not provide manually annotated emotion labels or behavior-category annotations derived from the video recordings.Instead, the video and audio data are released as raw, anonymized recordings intended to support downstream computational analysis, such as automated feature extraction, model-driven emotion recognition, and exploratory behavioral modeling by future researchers.Accordingly, no inter-rater annotation protocol or inter-rater reliability (IRR) metrics (e.g., Cohen’s Kappa or ICC) are reported in this study, as no human labeling process was conducted on the behavioral or emotional content of the recordings.This design choice was made deliberately to preserve the ecological validity of the intervention process and to avoid introducing subjective bias associated with predefined annotation schemas.The dataset is therefore positioned as a foundational multimodal resource rather than a fully annotated benchmark, allowing researchers to design and evaluate their own labeling strategies, annotation protocols, and reliability assessments according to specific research objectives. This dataset serves as a reusable empirical foundation for exploring the psychological mechanisms of art therapy, emotional expression patterns, and intervention effect prediction.

近年来，工科学生的心理健康问题日益凸显，学业压力与情绪压抑共同引发了焦虑、抑郁等广泛存在的负面心理状态。作为一种非药物干预手段，艺术治疗（art therapy）凭借其情绪调节与自我表达的特性，被认为具备促进心理康复、提升主观幸福感的潜力。但目前针对工科学生艺术治疗的多模态心理数据仍较为稀缺，这限制了该领域的深入研究。为填补这一研究空白，本研究构建了面向艺术治疗研究的工科学生多模态心理数据集，系统记录了26名参与者在艺术治疗干预前后的心理变化与多源行为模式。该数据集遵循“前测—干预—后测”的研究范式，问卷模块涵盖焦虑、抑郁、主观幸福感、感知压力、人格特质等相关量表，以及针对艺术治疗专属体验的调查问卷；同时收录了治疗过程的视频数据与参与者的主观体验文本。需要特别说明的是，本数据集未提供人工标注的情绪标签，亦未生成基于视频录制内容的行为类别标注。所有视频与音频数据均以未经人工标注、已完成匿名化处理的原始录制格式发布，旨在为后续计算分析提供支持，例如自动化特征提取、基于模型的情绪识别，以及未来研究者开展的探索性行为建模工作。因此，本研究未报告评分者间标注协议或评分者间信度（inter-rater reliability, IRR）指标（如科恩κ系数或组内相关系数ICC），因为未对录制内容的行为与情绪信息开展任何人工标注流程。做出这一设计选择，是为了保留干预过程的生态效度，避免引入与预定义标注框架相关的主观偏差。本数据集并非完全标注的基准数据集，而是作为基础性多模态研究资源，允许研究者根据具体研究目标自行设计与评估标注策略、标注协议及信度评估方案。本数据集可为探索艺术治疗的心理机制、情绪表达模式及干预效果预测提供可复用的实证研究基础。