Multimodal Educational Data Fusion for Students’ Mental Health Detection

Abstract

Mental health issues can lead to serious consequences like depression, self-mutilation, and worse, especially for university students who are not physically and mentally mature. Not all students with poor mental health are aware of their situation and actively seek help. Proactive detection of mental problems is a critical step in addressing this issue in this case. However, accurate detections are hard to achieve due to the inherent complexity and heterogeneity of unstructured multi-modal data generated by campus life. Against this background, we propose a detection framework for detecting students' mental health, named CASTLE (educational data fusion for mental health detection). Three parts are involved in this framework. First, we utilize representation learning to fuse social life, academic performance, and physical appearance. An algorithm, named MOON (multi-view social network embedding), is proposed to combine students' social life in a comprehensive way by fusing students' heterogeneous social relations effectively. Second, a synthetic minority oversampling technique algorithm (SMOTE) is applied to the label imbalance issue. Finally, a DNN (deep neural network) model is utilized for the final detection. The extensive results demonstrate the promising performance of the proposed methods in comparison to an extensive range of state-of-the-art baselines.

Diagnosis of Depression Based on Four-Stream Model of Bi-LSTM and CNN From Audio and Text Information

“…Experimental results show that the proposed method is very effective. Guo et al [11] proposed a detection framework for detecting student mental health named educational data fusion for mental health detection (CASTLE). This framework is largely divided into three parts.…”

FCMCPS-COVID: AI propelled fog–cloud inspired scalable medical cyber-physical system, specific to coronavirus disease

“…Healthcare is one of the most extensive factors that need to be efficiently managed for the development of any country, as it is a significant determinant of the well-being of its citizens [1] . A country with an inadequate healthcare system can have a profound impact on the health of its people, resulting in a higher mortality rate, particularly during outbreaks.…”

Internet-based identification of anxiety in university students using text and facial emotion analysis

“…It allows individuals to express their opinions, feelings and thoughts on any subject. In addition, people with mental disorders often share their mental states or discuss mental health issues with others through platforms by posting text messages, photos, videos and other links ( Guo et al, 2022 ; Zhang et al, 2022 , Zhang et al, 2023 ). Thus, given the popularity of social networks, it would be possible to use social network data to detect stress, depression and anxiety ( Jia, 2018 ).…”