Empowering Predictions of the Social Determinants of Mental Health through Large Language Model Augmentation in Students' Lived Experiential Essays
Saved in:
| Title: | Empowering Predictions of the Social Determinants of Mental Health through Large Language Model Augmentation in Students' Lived Experiential Essays |
|---|---|
| Language: | English |
| Authors: | Mohammad Arif Ul Alam, Madhavi Pagare, Susan Davis, Geeta Verma, Ashis Biswas, Justin Barbern |
| Source: | International Educational Data Mining Society. 2024. |
| Availability: | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/ |
| Peer Reviewed: | Y |
| Page Count: | 11 |
| Publication Date: | 2024 |
| Document Type: | Speeches/Meeting Papers Reports - Research |
| Descriptors: | Mental Health, At Risk Students, Prediction, Automation, Artificial Intelligence, Student Experience, Essays |
| Abstract: | Recognizing the Social Determinants of Mental Health (SDMHs) among students is essential, as lower backgrounds in these determinants elevate the risk of poor academic achievement, behavioral issues, and physical health problems, thereby affecting both physical and emotional well-being. Leveraging students' self-reported lived experiential essays yields substantial insights into the SDMHs. However, constructing an automated prediction tool for SDMHs necessitates thorough planning, efficient design of a web-based tool for data collection, articulation of SDMHs within the context of lived experiences, expert data annotation, and the implementation of an efficient multi-label classifier for prediction. This paper investigates the capabilities of Large Language Models (LLMs) in the development of a multi-label SDMHs prediction system in students' lived experiential essays covering the above aspects. In this regard, we propose a novel Human-LLM Interaction for Annotation (HLIA) method to label texts pertaining to predetermined SDMHs and develop a Multitask Cascaded Neural Network (MTCNN) classification algorithm to predict these determinants in students' experiential essays. Additionally, we developed a web-tool based lived experience essay data collection system, developed a dataset of ~1500 lived experiential essays collected from 800+ students annotated by 4 educational experts (IRB approved) and evaluated the proposed framework. [For the complete proceedings, see ED675485.] |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | ED675582 |
| Database: | ERIC |
| Abstract: | Recognizing the Social Determinants of Mental Health (SDMHs) among students is essential, as lower backgrounds in these determinants elevate the risk of poor academic achievement, behavioral issues, and physical health problems, thereby affecting both physical and emotional well-being. Leveraging students' self-reported lived experiential essays yields substantial insights into the SDMHs. However, constructing an automated prediction tool for SDMHs necessitates thorough planning, efficient design of a web-based tool for data collection, articulation of SDMHs within the context of lived experiences, expert data annotation, and the implementation of an efficient multi-label classifier for prediction. This paper investigates the capabilities of Large Language Models (LLMs) in the development of a multi-label SDMHs prediction system in students' lived experiential essays covering the above aspects. In this regard, we propose a novel Human-LLM Interaction for Annotation (HLIA) method to label texts pertaining to predetermined SDMHs and develop a Multitask Cascaded Neural Network (MTCNN) classification algorithm to predict these determinants in students' experiential essays. Additionally, we developed a web-tool based lived experience essay data collection system, developed a dataset of ~1500 lived experiential essays collected from 800+ students annotated by 4 educational experts (IRB approved) and evaluated the proposed framework. [For the complete proceedings, see ED675485.] |
|---|
